Home » Biology » Environment

Category Archives: Environment

Want to Get Involved In Research?

[su_heading size="15" margin="0"]The BioInnovation Group is an undergraduate-run research organization aimed at increasing undergraduate access to research opportunities. We have many programs ranging from research project teams to skills training (BIG-RT) and Journal Club.

If you are an undergraduate interested in gaining research experience and skills training, check out our website (https://bigucd.com/) to see what programs and opportunities we have to offer. In order to stay up to date on our events and offerings, you can sign up for our newsletter. We look forward to having you join us![/su_heading]

Newest Posts

Preliminary evidence for differential habitat selection between bird species of contrasting thermal-tolerance levels

June 3, 2023

By Phillips.


Author’s note: Since coming to college, I have wanted to conduct research on the environmental impacts of agriculture and contribute to efforts to make farming work for both people and nature. In pursuit of this goal, I signed up as an intern with Daniel Karp’s agroecology lab in my freshman year and stayed with them for my entire undergrad. During this internship, I worked alongside several Ph.D. students, such as Katherine Lauck and Cody Pham, who research the cumulative effects of land conversion and climate change on native avifauna at Putah Creek. I was so inspired by their work that I decided to conduct an independent project investigating similar phenomena. Specifically, I was curious about how birds respond to temperature across multiple landscapes, and how this pattern of behavior might influence their choice of habitat. While reading this paper, I would like you to consider the broader implications of the findings as they pertain to species conservation in the context of climate change.

Abstract

Increasing frequency and severity of temperature spikes caused by climate change will disproportionately impact heat-sensitive species. However, certain types of vegetation may protect animals from temperature spikes. Heat-sensitive species can retreat to shaded microhabitats when temperature increases, allowing them to avoid detrimental effects on fitness. Here, we examined habitat selection and behavioral responses to temperature of Western Bluebirds (Siailia mexicana) and Northern Mockingbirds (Mimus polyglottos). We conducted transect surveys and collected behavioral data on bird movement for two months in riparian forest and perennial cropland in the Central Valley of California, where breeding season temperatures are often above 35°C. Bluebirds were observed more frequently in shaded riparian forest, while mockingbirds were observed more frequently in exposed agricultural fields. Correspondingly, bluebirds became less active at higher temperatures, while mockingbirds exhibited no response. Together, our results imply that heat-sensitive species may be more likely to select natural or semi-natural habitats and change their behaviors when temperatures spike. The results of this study imply that the combined effects of anthropogenic land development and climate change may be more destructive for heat-sensitive species than for heat-tolerant species.

Introduction

Climate change is increasing the frequency and intensity of temperature spikes across the world [1]. Many species will likely experience increased mortality due to these extreme conditions [2–4], with heat-sensitive species experiencing especially detrimental effects [5,6]. However, thermally-buffered habitats could mitigate the impact of heat spikes on organisms, as certain habitat features, like vegetative cover, have been shown to cool local temperatures through shading and evapotranspiration [7,8]. Landscapes with high amounts of thermally-buffered habitats, such as closed-canopy forests, have been shown to have less dramatic temperature extremes than open habitats [9,10]. Furthermore, it has been shown that animals in these thermally-buffered habitats are less likely to be impacted by rising global temperatures [11,12]. As such, organisms that are sensitive to temperature extremes may preferentially select for these habitats, and therefore may be able to avoid potentially lethal effects. Birds have been observed to retreat to shaded habitats when temperatures spike [13]. However, it is unclear whether heat-sensitive species specifically select for thermally-buffered habitats, or if heat-tolerant species persist in non-buffered habitats. Therefore, we sought to understand how the habitat selection of bird species may be associated with their behavioral responses to temperature.

Bird populations in North America are in rapid decline [14], and are predicted to continue declining with climate change [15]. As such, determining the habitat requirements of birds in response to increasingly extreme temperatures could be crucial to their conservation. We conducted behavioral surveys of birds in the Central Valley of California to address two questions: 1) does habitat selection differ between Western Bluebirds and Northern Mockingbirds, and 2) are behavioral responses to temperature different between these species? We hypothesized that birds species which exhibit significantly different behavior during high temperature will preferentially select habitats with more vegetation cover.

Methods

Experimental design

We selected two sites along Putah Creek in the Central Valley of California. In this system, temperatures often reach 35°C during the hottest months of the year. These sites contain a combination of riparian (forest existing along a river bank) and agricultural land and are approximately five miles apart from each other. At each site, the two focal land cover types–riparian forest and perennial agriculture–were present within one half-mile of each other (Figure 1). We obtained observations along four 100 meter (m) transects. In the riparian areas, we placed transects along regions of the sites where vegetation was sparse enough that birds could be observed, as dense vegetation made it difficult to track the individual birds. In the agricultural areas, we placed transects along areas that were close enough to the crops that birds could be spotted. Transects were placed approximately 50 meters apart from each other. 

We focused on Western Bluebirds (Siailia mexicana) and Northern Mockingbirds (Mimus polyglottos) due to their high abundance at Putah Creek. Additionally, we chose these species because they forage on the ground rather than in the air, and therefore were easier to observe with the naked eye.

Figure 1. Our two study sites were in close proximity to both riparian and agricultural habitats along Putah Creek. At each site, we observed birds along a total of 16 transects (depicted in red).

Data collection

We conducted our surveys from late April to early July 2022, the height of the breeding season for our study species. We visited each site at least once a week, in either the morning or early afternoon. During each visit, I would walk along the transects. Once a bird of either target species was spotted, I would track the bird for two minutes and record all behaviors displayed, along with the amount of time spent engaging in each behavior. These behaviors included “foraging” (searching for, chasing, or eating an insect), “moving” (locomotion with wings or legs), “resting” (standing or sitting motionless), “singing” (repetitive vocalization for more than three seconds), “preening” (use of beak to position feathers), and “disputing” (fighting between birds that occurs due to territorial disputes). I recorded temperature and wind speed each hour using a Kestrel 2000 Weather Meter.

Data analysis

We ran Fisher’s exact tests to determine if mockingbirds and bluebirds preferentially selected different landscape types across sites. The variables in this model included ‘species,’ and ‘landscape type,’ which was defined as either “Agriculture” or “Riparian.” We ran the model across both sites and did not distinguish between the two separate sites depicted in Figure 1.

Then, we implemented multiple linear regression models examining the relationship between the time spent engaging in various behaviors and temperature for each species. We considered the time spent engaged in a particular behavior to be the percentage of time during the two-minute observation period in which the individual bird exhibited that behavior (i.e., time spent moving, foraging, resting, preening, singing, or disputing).

To account for the effects of spatial autocorrelation (or the tendency of areas which are close together to provide similar data values), we first included a site covariate in our models. We additionally attempted to control for the effects of a natural circadian rhythm on behavior by including a time-of-day covariate. As temperature and time were highly correlated (r = 0.696 for bluebird observations and 0.548 for mockingbird observations), we included these covariates using a temperature residual approach. Specifically, we regressed time against temperature and obtained residual values, representing whether temperatures were hotter or cooler than the average expected temperature at any given time of day. We then ran a multiple linear regression including the effects of temperature residuals, time of day, and site on bird behavior.

Results

Landscape preference

Bluebirds and mockingbirds exhibited significantly different habitat preferences. Bluebirds preferentially resided in riparian areas, whereas mockingbirds preferentially resided in agricultural landscapes across both sites (Fisher’s exact test, p = 4.583E-15; Figure 2).

Figure 2. Mockingbirds (n=34) are observed to reside in agricultural landscapes more frequently than riparian landscapes. Bluebirds are observed to reside in riparian landscapes more frequently than agricultural landscapes (n=35).

Changes in patterns of behavior

We found that temperature negatively affected the amount of time that bluebirds spent moving (Linear regression, p = 0.0077, F = 8.069, df = 1, 33; Figure 3; Supp. 1). However, temperature did not significantly affect mockingbird movement (Linear regression, p = 0.297, F = 1.125, df = 1, 32; Figure 3; Supp. 1).

Results were broadly similar after including ‘site’ as another effect in the model to account for multiple observations at the same location. Specifically, temperature still did not affect mockingbird movement (Multiple regression, p = 0.635, F = 0.577, df = 3, 30; Supp. 3) and marginally affected bluebird movement (Multiple regression, p = 0.0682, F = 2.622, df = 3, 31; Supp. 3). However, one of the sites had very few bluebird observations (n=4); when this site was removed from the model, temperature again negatively affected bluebird movement (Linear regression, p = 0.0123, F = 7.138, df = 1, 29; Supp. 2).

The last model we ran tested the effects of both temperature residuals and time of day on bird behavior. Using these models, temperature again did not have a significant effect on the behavior of mockingbirds but did have a marginal effect on bluebird movement (p = 0.07; Supp. 4).

For all of the models, resting, foraging, disputing, singing, and preening of bluebirds and mockingbirds exhibited no significant association with any environmental variable (Supp. 1, Supp. 2, Supp. 3, Supp. 4).

Figure 3. Bluebirds (left) are observed to reduce the percentage of time they spend moving as temperature increases. Mockingbird movement (right) did not significantly decline with rising temperature. The black points represent individual bird observations, the solid lines represent the linear model predictions, and the gray bands represent the 95% confidence intervals.

Discussion

Our results suggest that bluebirds select for shaded riparian habitats, while mockingbirds select for exposed agricultural habitats. Correspondingly, the temperature-altered patterns of movement in bluebirds suggest that they are sensitive to heat and may potentially select for thermally-buffered habitats as a result. In contrast, a lack of observed heat sensitivity in mockingbirds suggests that persistence in open habitats could in part be driven by thermal tolerance. While more data are required to make definitive conclusions, considering only patterns at our site with sufficient data, we found significant evidence for temperature-altered patterns of movement. Together, these results suggest that temperature sensitivity could drive patterns of habitat selection.

Previous research also suggests that habitats with low vegetative cover (i.e., without thermally-buffered microclimates) are likely to contain heat-tolerant species [16,17]. For example, Wilson et al. 2007 demonstrated that populations of leaf-cutter ants (Atta sexdens) residing in cities took 20% longer to succumb to high temperatures than ants dwelling in rural areas. In Brans et al. 2017, it was observed that water fleas (Daphnia magna) from urban areas were more tolerant to high temperatures than rural populations, partially because they had smaller body sizes. Both studies imply that organisms must have high heat tolerance to live in habitats with low vegetative cover. This is similar to our finding that mockingbirds, a heat-tolerant species, were more likely to reside in unvegetated agricultural landscapes than were bluebirds, a heat-sensitive species. However, while the previous studies provide evidence that organisms become heat-tolerant in these landscapes due to natural selection, our findings suggest that behavioral differences between heat-tolerant species and heat-sensitive species may also cause unvegetated landscapes to become dominated by heat-tolerant species.

Additionally, we demonstrate that riparian and other thermally-buffered habitats could be crucial to the persistence of heat-sensitive species. Other studies have shown that vertebrates are more likely to exhibit heat-related mortality in habitats with low vegetation cover [12,18]. For example, Zuckerberg et al. 2018 demonstrated that avian survival in small grassland patches was negatively associated with temperature, while survival in large grassland patches was not. Additionally, Lauck et al. 2023 showed that temperature spikes are associated with a decline in bird reproduction across the continental United States for organisms living in agricultural areas, but not for organisms living in forests. These results suggest that vegetation protects vertebrates from heat stress. Although the mechanisms of this protection are not clear, one potential explanation is that vegetation provides shaded areas that animals can use as refuges to avoid lethal temperatures [7]. Additionally, it has been shown that plants regulate local temperatures through evapotranspirative cooling [8], potentially playing a role in protecting vertebrates from heat spikes.

One caveat of our study is that bluebird responses were only marginally significant under multiple regression models that included time of day as a covariate. Associations between bird behavior and time could either be due to circadian rhythms or temperature shifts; it is difficult to statistically disentangle the effects of temperature and time of day. However, the significant results from the models including only temperature imply that bluebirds do indeed alter their behavior in response to environmental factors that likely include temperature.

Conclusion

Our findings provide preliminary evidence that Western Bluebirds are temperature-sensitive and preferentially select vegetated habitats, while Northern Mockingbirds do not preferentially select vegetated habitats. To obtain enough data to provide definitive evidence of these patterns, the methods could be repeated for several more years and across more sites. Nonetheless, the results from this study suggest that anthropogenic land development will be more destructive for heat-sensitive species than for heat-resistant species. As such, we suggest incorporating thermally-buffered habitats such as groups of trees or hedgerows in working landscapes to mitigate the negative impacts of anthropogenic land development on heat-sensitive organisms.

Western Sandpiper Population Decline on the Pacific Coast of North America

May 26, 2023

By Emma Hui, Biological Sciences ‘26 

INTRODUCTION

The migration of Western Sandpipers from the high Arctics to Southern California has always been a treasured gem in the fall. Yet as decades roll by, Western Sandpiper populations have been in continuous decline, and the rugged coastline of the Pacific Northwest seems lonelier than ever [1]. As a migratory bird species, the Western Sandpiper plays crucial ecological roles as an indicator of ecosystem health and by connecting diverse habitats across continents.

The purpose of this essay is to introduce the ongoing decline of Western Sandpiper populations in recent years, with a particular focus on the population decline in North America. This paper will provide an overview of Western Sandpiper migration and population changes, examine the potential causes behind the dynamics, and analyze the decline’s corresponding ecological effects. I will also explore possible remedies for the issue from the perspectives of habitat restoration, conservation, and legislative measures. The ultimate objective of this essay is to raise awareness and promote action for the ecological conservation of Western Sandpipers before it is too late.

Background

Western Sandpipers are small migratory birds that breed in high Arctic regions of Alaska and Siberia and migrate south to the Pacific coast of North and South America for winter. Their migration is 15,000 kilometers every year along the Pacific Flyway, spanning from Alaska to South America. During winter, their nonbreeding season, they move to coastal areas with mudflats, estuaries, and beaches, which allows the birds to rest and forage for food. In spring, the Western Sandpipers take a similar reverse migration route, stopping at critical habitats along the way until they reach the treeless Arctic tundra. As they fly north, they breed in Northwestern Alaska and Eastern Siberia, and each female lays three to four eggs. 

They measure 6 to 7 inches in length and have reddish brown-gold markings on their head and wings. Their most salient features are their slender pointed bills and long legs. The bills are adapted for foraging crustaceans, insects, and mollusks in muddy areas, while their pair of long but thin legs are used for wading in shallow water and sand. These small, darting birds can be seen in tidal areas, foraging in mudflats for invertebrates and biofilms at low and middle tides with other shorebird communities.

Having multiple species foraging together makes shorebirds among the most difficult birds to identify, especially with many species being quite similar in morphology as well as call. As they always smoothly blend into the community, it is not surprising that the population decline of the small Western Sandpipers went unnoticed at first and was reported only when changes in population levels became more obvious.

Causes of Western Sandpiper population decline

The population decline in the Western Sandpiper population has been continuous throughout the past decade. According to the North American Breeding Bird Survey, which monitors populations of breeding birds across the continent, the Western Sandpiper had a relatively stable population trend in the United States from 1966 to 2015, with an annual population decline of 0.1% over this period [2]. In more recent years, a research team in British Columbia, Canada that investigates estuary condition change has noticed the decline in Western Sandpipers inhabiting the Fraser River estuary. Observing the Western Sandpiper population during Northern migration on the Fraser River estuary, the team concluded a 54% decline in Western Sandpipers over the entire study period of 2019 []. The negative trend in migrating Western Sandpipers in North America is consistent with this study in Fraser River. A study using Geolocator wetness data to detect periods of migratory flight examined the status and trends of 45 shorebird species in North America, including the Western sandpiper. The author found that the Western Sandpiper population in the U.S. declined by 37% from 1974 to 2014, with an estimated population of 2,450,000 individuals in 2014 compared to 3,900,000 individuals in 1974.[3]

Currently, on BirdLife International Data Zone, Western Sandpipers have been labeled “least concern” for their wide range of inhabitation, but their population is decreasing. The species faces threats from habitat loss and degradation, pollution, and disturbance, particularly in its wintering and stopover sites along the North American Pacific coast. Habitat loss due to human activities, namely agricultural expansion and oil development, has contributed to the loss and degradation of Western Sandpiper’s breeding, wintering, and stopover habitats. [4] The loss of these habitats has led to reductions in breeding success, migration stopover times, and overwintering survival of Western Sandpipers. Meanwhile, Western Sandpipers are constantly exposed to various pollutants including pesticides, heavy metals, oil spills, and plastics. These contaminants affect Western Sandpiper’s health and reproductive success directly and impact Western Sandpiper’s prey and predators. As habitat loss leads to reduced food resources, Western Sandpipers’ overall health is negatively impacted, making them even more vulnerable to pollutants and contaminants. 

Climate change is also expected to have future impacts on the species. One possible shift that climate change can impose is on the timing, intensity, and distribution of precipitation. The precipitation shifts have caused droughts and floods in areas that are breeding and stopover habitats for Western Sandpiper and other shorebirds, leading to reduced breeding success and increased mortality in the Western Sandpiper population. Climate change also imposes effects on sea level, temperature, and the frequency and severity of extreme weather, which can all affect the quality of breeding habitat and food availability for Western Sandpipers.

The interactions between these factors are complex and can lead to a feedback loop of negative impacts on the population. As habitat loss leads to reduced food resources, Western Sandpipers’ overall health is negatively impacted, making them even more vulnerable to pollutants and contaminants.

Effects of Western Sandpiper population decline

The decline of the Western Sandpiper population can have significant impacts on ecosystems. As a migratory shorebird, the Western Sandpiper’s ecological role lies in coastal environments; by preying on invertebrates along the coastal shoreline, the Western Sandpipers control their prey species populations and balance the ecosystem. The decline of the Western Sandpiper population can lead to an increase in their prey species such as polychaete worms and bivalves, which can lead to changes in the composition of other species that prey on similar invertebrates and perturbates the ecosystem’s equilibrium. Furthermore, many predator species, such as falcons and owls, depend on the Western Sandpiper as a food source, and their decline will negatively impact these predator species.

Aside from predator-prey dynamics, Western Sandpipers also forage with many other migratory shorebird species in muddy areas along the coast. These birds, such as the Marble Godwit and the Red Knot, depend on the same stopover habitats as the Western Sandpiper during their own migrations and thus compete for similar resources. As the Western Sandpiper population declines, changing interspecies dynamics will shift the survival and reproductive success of other species, disturbing the equilibrium of the stopover ecosystems.

Western Sandpipers are a popular bird species among birdwatchers and nature enthusiasts, and their migration stopover sites in the Pacific Northwest and Alaska have an important role in ecotourism and its respective economic and cultural values. The economic impact of Western Sandpiper ecotourism in the Copper River Delta, Alaska was evaluated to produce over $1.5 million in revenue and 100 jobs. [5]

Overall, the decline of the Western Sandpiper population can have a complex and far-reaching impact on both the ecosystem and human society. By interacting with native species and migratory species in their natural habitats, the Western Sandpiper’s role deeply interweaves within the ecosystem.

Conservation efforts and solutions

Conservation efforts to protect and restore Western Sandpiper populations are critical in maintaining ecosystem health. One of the main strategies to protect the Western Sandpiper is to conserve their stopover sites and breeding grounds by monitoring and researching invasive species and coastal development. Aside from consistent restoration of degraded habits after human disturbance, prevention of further human development in Western Sandpiper habitats is also critical in maintaining the habitat’s health.

Educating the public about the importance of Western Sandpipers and their habitats is a crucial aspect in raising awareness and gaining support for conservation efforts. Outreach such as public lectures, bird festivals, and school tours are great opportunities to connect humans to the beautiful avian community and improve public consciousness regarding ecosystem conservation. An example includes the Monterey Bay Birding Festival, which is an annual festival in California during shorebird fall migration season. This festival promotes awareness of shorebirds with its educational workshops and bird tours.[6]

Currently, conservation efforts of shorebird populations face limitations in funding and coordination. Significant funding efforts are required to restore what has been lost, but limited budgets restrict the scope and effectiveness of conservation approaches. In addition, since conservation efforts are implemented on a site-by-site basis, there is a need for improved coordination among different agencies to solve problems together. Potential solutions to the need for adequate funding and coordination are the implementation of stronger policies of avian conservation and habitat conservation as well as the encouragement of sustainable tourism and outreach efforts.

CONCLUSION

The Western Sandpiper population in North American tidal areas has been experiencing a significant decline in recent years, largely due to human activities and subsequent climate change. Population changes of this small, long-legged shorebird affect many species that interact and co-exist with them in the coastal ecosystem. They are one of the most abundant shorebird species in North America and play a vital part in the ecological and cultural values along the coast. Population dynamics vary year to year and between different populations, and increasing efforts in the monitoring and conservation of the Western Sandpiper community and their respective habitats is essential to ensuring the species’ survival. We need to investigate the causes behind the population’s decline in recent years and take action before the negative effects have gone too far and these ballerinas of the beach are unable to recover.

REFERENCES

[1] Andres, B., Smith, B. D., Morrison, R. I. G., Gratto-Trevor, C., Brown, S. C., Friis, C. A., … Paquet, J. (2013). Population estimates of North American shorebirds, 2012. Wader Study Group Bulletin, 119, 178-194.

 [2] The Cornell Lab of Ornithology. (n.d.). Western Sandpiper Overview, All About Birds, Cornell Lab of Ornithology. Cornell University. https://www.allaboutbirds.org/guide/Western_Sandpiper/overview

[3] The Wader Study Group. (n.d.). Geolocator Wetness Data Accurately Detect Periods of Migratory Flight in Two Species of Shorebird. https://www.waderstudygroup.org/article/9619/

[4] Smith, B. D., Andres, B. A., & Morrison, R. I. G. (2017). Declines in shorebird populations in North America. Wader Study, 124(1), 1-11.

[5] Vogt, D. F., Hopey, M. E., Mayfield, G. R. III, Soehren, E. C., Lewis, L. M., Trent, J. A., & Rush, S. A. (2012). Stopover site fidelity by Tennessee warblers at a southern Appalachian high-elevation site. The Wilson Journal of Ornithology, 124(2), 366-370. https://doi.org/10.1676/11-107.1

[6] Cornell Lab of Ornithology. (2019, September 24). Monterey Bay Festival of Birds [Web log post]. All About Birds. https://www.allaboutbirds.org/news/event/monterey-bay-festival-of-birds/#

 [7] Haig, S. M., Kaler, R. S. A., & Oyler-McCance, S. J. (2014). Causes of contemporary population declines in shorebirds. The Condor, 116(4), 672-681.

 [8] Kallenberg, M. (2021). The 121st Christmas Bird Count in California. Audubon. https://www.audubon.org/news/the-121st-christmas-bird-count-california

 [9] Reiter, P. (2001). Climate change and mosquito-borne disease. Environmental Health Perspectives, 109(1). https://doi.org/10.1289/ehp.01109s1141

 [10] Sandpipers Go with the Flow: Correlations … – Wiley Online Library. (n.d.). Wiley Online Library. https://doi.org/10.1002/ece3.7240

[11] The Wader Study Group. (n.d.). Comparison of Shorebird Abundance and Foraging Rate Estimates from Footprints, Fecal Droppings an,d Trail Cameras. https://www.waderstudygroup.org/article/13389/

 [12] US Fish and Wildlife Service. (2022). Western Sandpiper (Calidris mauri). https://www.fws.gov/species/western-sandpiper-calidris-mauri 

[13] Wamura, T., Iwamura, T., & Possingham, H. P. (2013). Migratory connectivity magnifies the consequences of habitat loss from sea-level rise for shorebird populations. Proceedings of the Royal Society B, 280(1761), 20130325. https://doi.org/10.1098/rspb.2013.0325

A Warmer World Leading to a Health Decline

May 23, 2023

By Abigail Lin, Biological Sciences.

INTRODUCTION

Rising temperatures due to global climate change cause several detrimental impacts on the world around us. This paper will analyze the consequences of climate change, specifically temperature changes, within California. Livelihoods of farmers and fishermen, distribution of disease, and fire intensity are examples of how California is affected by this crisis. Climate change in California is especially visible because California dominates the nation’s fruit and nut production, two water-intensive crops. The state’s reliance on large quantities of water to fuel its agricultural system makes it particularly susceptible to drought. Proliferation of detrimental disease vectors, loss of beneficial crops, and elevated levels of dryness imply a complex interaction between California ecosystems and climate change. 

Crops

There are many farmers and agricultural workers in California impacted by changing climates, as the state is a major agricultural hotspot. Two-thirds of the nation’s fruits and over one-third of the nation’s vegetables are produced in California [1]. Crops such as apricots, peaches, plums, and walnuts are projected to be unable to grow in 90% or more of the Central Valley by the end of the century because of the increase of disease, pests, and weeds that accompany rising temperatures [1]. 

Figure 1. Projection of crop failure by the end of the century. Heat increases diseases, pests, and weeds. Plum, apricot, peach and walnut crops will be unable to grow in 90% of Central Valley as a result.

Crop yields significantly decrease when heat sensitive plants are not grown in cool enough conditions. Fruits and nuts require chill hours, when the temperature is between 32 and 45 degrees Fahrenheit, to ensure adequate reproduction and development [2]. However, with increasing temperatures, crops are receiving less chill hours during the winter. California grows 98% of the country’s pistachios, but changes in chill hours have affected fertilization [3]. A study found that pistachios need 700 chill hours each winter, yet there have been less than 500 chill hours over the past four years combined [1]. As a result, in 2015, 70% of pistachio shells were missing the kernel (the edible part of the nut) that should have been inside [3]. 

Repeated crop failures have also left farmers mentally taxed. Evidence suggests that suicide rates for farmers are already rising in response to farm debt that accumulates in response to poor crop yields [4]. Not only is people’s financial well-being threatened by climate change, but so is their mental health. Mental stress threatens to rise as climates warm around the world, causing economic loss and upheaving agricultural careers. 

Crab Fisheries

Crab fisheries and fishers in California are also negatively impacted by the rise in temperatures. Warming oceans have led to an uncontrollable growth of algal blooms, which contaminates crab meat with domoic acid, a potent neurotoxin that causes seizures and memory loss [5]. The spread of this toxin has forced many fisheries to close. California fishers lost over half the crabs they regularly catch per season, and qualified for more than 25 million dollars of federal disaster relief, during 2015 to 2016 [5]. In response to financial loss, fishers adapted by catching seafood species other than crab, moved to locations where algal blooms have not contaminated their catch, or in the worst case, stopped fishing altogether [5]. California crab fishers’ careers have already been dramatically altered by global warming, and the amount of algal blooms will only continue to increase if warming continues. 

Disease

Temperature plays a major role in the prevalence of infectious diseases because it increases the activity, growth, development, and reproduction of disease vectors, living organisms that carry infectious agents and transmit them to other organisms. It is predicted that warm, humid climates will allow bacteria and viruses, mosquitoes, flies, and rats (all common disease vectors) to thrive [6]. Most animal disease vectors are r-selected, meaning they put little parental investment into individual offspring, but produce many. Warm temperatures allow r-selected species to grow quickly and reproduce often. However, warm temperatures speed up biochemical reactions and are very energy demanding on organism metabolism [7]. In response, disease vector ectotherms, organisms requiring external sources of heat for controlling body temperature, have successfully adapted to changing temperatures. These organisms thermoregulate, or carry out actions that maintain body temperature [7]. Behavioral thermoregulation has shifted the geographical distribution of infectious diseases as disease vectors move to the warm environments that they favor [7]. 

Initial models about the distribution and prevalence of disease suggested a net increase of the geographical range of diseases, while more recent models suggest a shift in disease distribution [7]. Recent models recognize that vector species have upper and lower temperature limits that affect disease distribution [7]. It is estimated that by 2050, there will be 23 million more cases of malaria at higher latitudes, where previously infections were nonexistent, but 25 million less cases of malaria at lower latitudes, where previously malaria proliferated rapidly through populations, because the conditions necessary for malaria transmission will shift [7]. 

Figure 2. Shift of malaria disease distribution by 2050. Higher latitudes will have 23 million more cases of malaria while lower latitudes will have 25 million less cases. Although habitat suitability changed, there is little net change in malaria cases. 

Cases of Coccidioidomycosis (Valley fever), an infectious disease spread from inhaling Coccidioides fungal spores, have recently reached record highs in California [8]. Valley fever is especially prevalent in areas experiencing fluctuating climates, vacillating between extreme drought and high precipitation [8]. After studying 81,000 cases collected over 20 years, researchers identified that major droughts have a causal relationship with increasing Coccidioidomycosis transmission rates [8]. Initially, drought will suppress disease transmission because it prevents proliferation of the Coccidioides fungi. However, transmission rebounds in the years following drought because competing bacteria die off in high heat [8]. Fungi have a number of traits that make them more tolerable to drought compared to bacteria including osmolytes for maintaining cell volume, thick cell walls to mitigate water loss, melanin which aids in thermoregulation, and hyphae that extend throughout the soil to forage for water [9]. Disease spikes are seen after drought, such as the wet season between 2016 and 2017, which had about 2,500 more cases of Valley fever in comparison to the previous year. [8]. 

The role of rising temperatures in increasing Valley fever cases is evident in Kern County, one of the hottest and driest regions of California. Kern Country has the highest Valley fever incident rates in California; 3,390 cases occurred in a 47-month drought from 2012 to 2016 [8]. Kern County has many cases of Valley fever because of its drought-like conditions. As climate change pushes areas throughout California that are usually cool and wet year-round into alternating dry and wet weather conditions, Valley fever cases are projected to increase. 

Fires

Climate change is also associated with an increase in fire season intensity. The Western United States experienced three years of massive wildfires from 2020 to 2022, with each year burning more than 1.2 million acres [10]. The ongoing drought has led to an accumulation of dry trees, shrubs, and grasses [10]. A 2016 study found that this increase of dry organic plant material has more than doubled the number of large fires in the Western United States since 1984 [10]. One of the ways that dry matter may ignite is by lightning. Projections show that by 2060, there will be a 30% increase of area burned by lightning-ignited wildfires compared to 2011 [10]. 

Residents in California are in danger of losing their lives and property to fire damage. A single fire can lead to massive destruction. In 2018, the Woolsey Fire burned 96,949 acres and hundreds of homes, and killed three people [11]. Over one million buildings in California are within high-risk fire zones, and this number is projected to increase as temperatures continue to rise [10]. With the amount of dry organic matter increasing and wildfire incidence surging, there will be more cases of property damage and loss of life in California. High temperatures and extreme weather events make it more likely that people will fall victim to these life-threatening disasters. 

CONCLUSION

Increases in global temperature have a negative effect on human physical health and mental wellbeing. Climate change is making it more difficult to secure a livelihood, changing the spread of disease, and destroying lives and property. However, projections about rising temperatures allow farmers the chance to make informed decisions about which crops to grow, fishermen to relocate to areas that are less impacted by algal blooms, health experts to predict when and where outbreaks of certain diseases might occur, and fire protection services to increase their presence in high-risk areas. Projections help people predict where and when a climate change associated event is likely to occur, so that they may hopefully respond quicker and more efficiently. Consequences of climate change can be mitigated by using models as a guide for what to expect in California’s future. 

REFERENCES

  1. James I. 2018. California agriculture faces serious threats from climate change, study finds. The Desert Sun. Accessed January 31, 2023. Available from www.desertsun.com/story/news/environment/2018/02/27/california-agriculture-faces-serious-threats-climate-change-study-finds/377289002/
  2. U.S. Department of Agriculture. Climate Change and WINTER CHILL. Accessed December 23, 2023. Available from www.climatehubs.usda.gov/sites/default/files/Chill%20Hours%20Ag%20FS%20_%20120620.pdf
  3. Zhang S. 2015. Time to Add Pistachios to California’s List of Woes. WIRED. Accessed February 15, 2023. Available from www.wired.com/2015/09/time-add-pistachios-californias-list-problems/
  4. Semuels A. 2019. ‘They’re Trying to Wipe Us Off the Map.’ Small American Farmers Are Nearing Extinction. TIME. Accessed January 31, 2023. Available from time.com/5736789/small-american-farmers-debt-crisis-extinction/
  5. Gross L. 2021. As Warming Oceans Bring Tough Times to California Crab Fishers, Scientists Say Diversifying is Key to Survival. Inside Climate News. Accessed January 31, 2023. Available from insideclimatenews.org/news/01022021/california-agriculture-crab-fishermen-climate-change/
  6. Martens P. 1999. How Will Climate Change Affect Human Health? The question poses a huge challenge to scientists. Yet the consequences of global warming of public health remain largely unexplored. Am Scien. 87(6):534–541. 
  7. Lafferty KD. 2009. The ecology of climate change and infectious diseases. Ecol Soc Amer. 90(4):888-900. 
  8. Hanson N. 2022. Climate change drives another outbreak: In California, it’s a spike in Valley fever cases. Courthouse News Service. Accessed March 8, 2023. Available from www.courthousenews.com/climate-change-drives-another-outbreak-in-california-its-a-spike-in-valley-fever-cases/
  9. Treseder KK, Berlemont R, Allison SD, & Martiny AC. 2018. Drought increases the frequencies of fungal functional genes related to carbon and nitrogen acquisition. PLoS ONE [Internet]. 13(11):e0206441. doi.org/10.1371/journal.pone.0206441
  10. National Oceanic and Atmospheric Administration. 2022. Wildfire climate connection. Accessed January 31, 2023. Available from www.noaa.gov/noaa-wildfire/wildfire-climate-connection#:~:text=Research%20shows%20that%20changes%20in,fuels%20during%20the%20fire%20season
  11. Lucas S. 2019. Los Angeles is the Face of Climate Change. OneZero. Accessed January 31, 2023. Available from onezero.medium.com/los-angeles-is-burning-f9fab1c212cb

Current threats to the Greater Everglades Ecosystem by invasive Burmese pythons

March 10, 2023

By Jessica Baggott, Evolution Ecology and Biodiversity Major, Professional Writing Minor, ’23

Author’s note: I wrote this piece in the Spring Quarter of 2022 for UWP 102B, Writing in the Disciplines: Biology. I wrote this piece partially because I have always fostered an interest in invasive species — how they enter, alter, and succeed in ecosystems. And, how we as scientists and policymakers address these threats to native ecosystems. I was also compelled to write this review because of the abundance of recent literature and the lack of another review, to my knowledge, that covered the same topics as I intended to.

I hope that readers walk away from this piece with a greater understanding of the Burmese python in the Florida Everglades — their invasion, success, and alterations to a fragile and precious ecosystem. I wish for readers to recognize the connections that I have made, combing through the literature, and I wish for them to make their own connections, too. There is no greater gift than your engagement with my work.

 

INTRODUCTION

Southern Florida’s Greater Everglades Ecosystem (GEE) once included over 8 million acres of 0.5-2.0 foot deep wetland from the Kissimmee Chain of Lakes just south of Orlando to the southern tip of Florida Bay [1]. Now, the GEE is estimated to be half of its historical size and is fragmented into various national, state, regional, and local parks as well as more than 12 wildlife refuges and marine preserves [2, 3, 4]. Everglades National Park (ENP), one of the federally protected regions of the GEE, only includes 1.5 million acres of this vast ecosystem [5]. However, even within the protected region of ENP, canals, pump stations, and roads have been  constructed to increase human accessibility to the Everglades, severely altering precise hydrological processes [1, 6]. These hydrological alterations, encroaching human settlements, degraded water quality, anthropogenic climate change, and the introduction of invasive species all pose significant threats to the GEE, and work in conjunction to increase negative effects on the GEE [4]. 

Perhaps the most infamous invasive species in the U.S., the Burmese python is the most well known threat to the GEE (Python molurus bivittatus). The snakes’ long lifespan, high fecundity or ability to produce offspring, as well as their generalist lifestyle which allows them to adapt their behavior and dietary habits to their environment, has allowed a small number of pythons to establish and thrive in the GEE [7]. Currently, Burmese pythons are drastically altering trophic structures as well as introducing and transmitting disease in the GEE. Furthermore, Burmese pythons have and have the potential to extend their range northward, putting other ecosystems and species at risk. A comprehensive literature review is required to inform policy decisions and assess the risk posed by Burmese pythons beyond the GEE.

Background

Native to Southeast Asia, the Burmese python was introduced into the GEE in the 1980s during a boom in the exotic pet trade and the subsequent release of the snakes into the Everglades by owners [7]. Since being first recognized in ENP in 2000, the invasive range of the Burmese python has rapidly expanded to the entirety of ENP and much of Big Cypress National Preserve [8]. However, population estimates have been hindered by the combination of cryptic python behavior (including long periods of inactivity), excellent natural camouflage, and human park management goals that include the removal of every python encountered without necessarily documenting the removed numbers [9]. These factors have caused extremely low python detection probabilities, ranging from 0.0001 to 0.0146 using visual surveys and radio transmitters [9]. Given low detection probability, population estimates range from tens of thousands to hundreds of thousands [9, 10]. Better population estimates are required for effective management strategies and to monitor changing populations of pythons [9].

Northward Range Expansion

Burmese pythons exhibit seasonal habitat preference, primarily choosing covered habitats close to water, though recent studies have found evidence that they may also be attracted to human development [11-17]. Smith et al. (2021) found that within their native range in Thailand, Burmese pythons do not avoid human dominated landscapes. Similarly, Bartoszek et al. (2021) found that in a northwest portion of ENP, within their invasive range, Burmese python hotspots were merely 515 meters from urban development on average. Researchers attributed this proximity to high quantities of readily available prey in these areas, in the form of livestock and birds attracted to the artificial lakes [11, 16]. However, egg clutches deposited in or near urban areas may exhibit lower survival rates than those in other habitats [8]. Though juveniles can travel long distances, particularly through use of agricultural canals, Pittman & Bartoszek (2021) hypothesize that in fact adult pythons with more sophisticated navigational capacities are the population driving expansion [18]. Adult sufficiency in and attraction to urban environments indicates that northward Burmese python expansion may not be hindered by human settlements. 

Besides suitable habitat, the range of ectotherms such as the Burmese python is typically limited by climate and/or the possession of behavioral adaptations such as retreating into underground refugia during winter months [19]. Though a conservative estimate allows Burmese pythons to survive for short periods of time at 5 °C,, temperatures must be above 16 °C in order for them to maintain digestion [19]. In isolation, these requirements make further expansion of the Burmese python in more northern parts of Florida extremely unlikely without the additional development of hibernation behaviors [19]. However, other researchers have found evidence of rapid adaptation for increased thermal tolerance after an extreme cold event in 2010 that caused high python mortality [20]. Adaptations included the maintenance of an active digestive system and changes in gene expression related to regenerative organ growth and behavior [20]. This rapid evolution by natural selection may permit Burmese pythons to expand their range northward into more temperate climates. 

However, there have been no studies in the last decade examining Burmese python’s potential for northward expansion, despite advances in climate and habitat models, tracking, and a greater understanding of Burmese python cold physiology. What studies do exist were inconclusive and results varied greatly: Rodda et al. (2009) and Pyron et al. (2008) provided oppositional potential range estimates. Rodda et al. (2009) concluded that the potential Burmese python range could include most of the southern U.S., from California through North Carolina. In contrast, Pyron et al. (2008) only included southern Florida and extreme southern Texas as the potential range of Burmese python expansion. Previous studies examining potential Burmese python range primarily agreed with Pyron et al. (2008) and all but two directly refuted the range suggested by Rodda et al. (2009) [19, 23-25]. Furthermore, climate change is projected to decrease the frequency and intensity of cold events in North America, allowing tropical species historically found at or near the equator, such as the Burmese python, to move poleward [26]. A literature review examining potential northward expansion of tropical organisms as a whole, with brief mentions of the Burmese python in Florida, posits that Burmese python range expansion is likely given the evidence for rapid adaptation for cold tolerance presented by Card et al. (2018) [26]. However, a complete understanding of the adaptive capacity of species, ecosystems, and biomes to climate change still remains lacking [26]. 

In addition to rapid adaptation to cold temperatures, Burmese pythons have shown evidence of hybridizing with another closely related invasive species, the Indian python (Python molurus) [27]. Hybridization has increased the population’s genetic diversity and allowed Burmese pythons to mitigate the founding and bottleneck effects — loss of genetic diversity due to a small founding population size or environmental effects [27]. Additionally, Hunter et al. (2018) found evidence of multiple paternity—the insemination of a female by more than one male during a single reproductive event—in Burmese pythons, also increasing python diversification rate. These behaviors allow for pythons to increase genetic diversity and will likely increase fitness, increasing the probability of northward expansion. 


Burmese Python Presence (1979–2016), Conyers & Sen Roy 2021.

Disease 

The invasion of the Burmese python in the GEE has introduced at least one pathogen, a lung parasite known as Raillietiella orientalis. Lacking coevolution with North American hosts, the spread and severity of this pathogen has increased in native species. This parasite now affects 13 species of native snakes and has extended beyond the python range into north central Alachua County, Florida, approximately 170 miles from the northernmost point of the GEE [28-30]. Researchers observed higher infection intensity, prevalence, and body size of R. orientalis in native snakes than in Burmese pythons, as native snakes do not share evolutionary history with R. orientalis and therefore are immunologically naive [29]. Infection by R. orientalis may be lethal or sublethal, and may be the cause of population decline of the pygmy rattlesnake [29, 31]. Additionally, R. orientalis’ native snake hosts have the highest rate of competence, or are most likely to transmit a resultant infection to a new host or vector after being exposed to a parasite. Furthermore, as R. orientalis’ native snake hosts are three of the most abundant snakes in North America [29], the parasite has a high likelihood of continued expansion throughout North America and possibly beyond [29]. Since the snakes of North America have not coevolved with R. orientalis, infections will be more severe and may cause population wide declines potentially resulting in devastating trophic cascades. The negative effects of the introduced parasite compound with those of Burmese python predation create weakened native populations more susceptible to parasitism, disease, and other stressors. More research is needed to ascertain the complete range of R. orientalis, expansion rate, intermediate hosts, sublethal effects on native snakes, and impact on populations. 

In addition to introducing a novel pathogen, Burmese pythons are competent hosts of at least one native pathogen and are suspected to be competent hosts of more [28, 32]. As a competent host to native pathogens, the Burmese python likely acts as a reservoir for these pathogens, and increases transmission to native species and humans [28, 32]. However, Burmese pythons are also able to change disease transmission through alteration of host communities via predation. Such is the case with the endemic Everglades Virus (EVEV), which can cause inflammation of the active tissues of the brain, known as clinical encephalitis, in humans. Decreased mammal diversity as a result of Burmese python predation was found to increase blood meals on amplifying hosts—hosts in which infectious agents multiply rapidly to high levels—increasing EVEV infection in mosquitoes [12]. Thus, it is possible that Burmese pythons could increase disease prevalence in humans as well, though contact with infected hosts is required for spread and therefore human disease may be driven by different factors than those in the mosquito-rodent cycle [12]. Understanding of the complex relationship between Burmese python predation on host species while also acting as hosts themselves remains lacking for many other important diseases, and presents an opportunity for future research. Additionally, studies should be conducted to estimate human risk as a result of the Burmese python altering host communities.

Further disease spillback is mediated by elevated rates of mosquito feedings on Burmese pythons [32]. The mosquitos that prefer feeding on Burmese Pythons also feed on a range of other species, including mammals, birds, reptiles, and amphibians [32]. Additionally, mosquito ranges extend beyond that of the Burmese python [32]. Thus, through both preferential feeding by mosquitoes on Burmese pythons and large mosquito range, the introduction of the Burmese python into the Everglades has increased disease spread beyond the python range.

Predation

The Burmese python has more than 40 prey documented in the Everglades, including a wide range of mammals and birds, and occasionally American alligators [33]. Given their appetite and potentially large population numbers, Burmese pythons are able to exert control over species populations. The decline of particular species relative to others can then cause ecosystem-wide cascades. Pythons have been found to cause severe mammal population declines through predation in their invasive range including 99.3%, 98.9%, and 87.5% decreases in observation frequency of raccoons, opossum, and bobcats respectively [33, 34]. Additionally, pythons have caused a complete local extinction of marsh rabbits, once one of the most commonly seen animals in ENP [33, 35, 36]. When reintroduced to ENP, marsh rabbits were able to establish a breeding population five months after translocation, but by 11 months after reintroduction, 77% of deaths were attributed to Burmese pythons and the population was unable to reestablish [35]. This disproportionate predation makes the reestablishment of this and other similarly affected species impossible as long as the python persists. Similarly, an analysis of anthropogenic stressors and those posed by pythons found that the strongest predictor for marsh rabbit occurrence was distance from the epicenter of python invasion [36]. These results indicate that pythons have profound effects on ecosystem composition through predation and are able to cause trophic cascades, damaging the ecosystem. Additionally, as is the case with Marsh Rabbits, species may be unable to reestablish in the core invasion area, even with translocation efforts. This demonstrates that without removal of Burmese pythons from the GEE, biodiversity and community composition of the GEE may be irreparably damaged.

Large, highly fecund species with wide habitat breaths were found to be the least susceptible to increased pressure from pythons, so the decline of a highly fecund and habitat generalist such as the marsh rabbit is especially concerning [37]. Using trait relationships, researchers predicted exclusively negative responses in occupancy probabilities to the presence of Burmese pythons regarding five unobserved species of concern: the everglades mink, feral hog, gray fox, red fox, and Key Largo woodrat [37]. Though rodent populations were previously thought to be resistant to the effects of pythons, declines in these populations have also been observed, and due to their lack of evolutionary history, one species, the Eastern woodrat, has even been suggested to be attracted to python scent [34, 38]. These results and research conducted on mammal resilience to pythons have shown that there is little evidence of resilience among mammals within the core invasion area, which only further contributes to the homogenization of the ecosystem [34]. Additionally, it is likely that loss of diversity and competition will allow other invasive species to establish more easily [34]. The results show the need for continued monitoring of species to analyze trends, research on response to novel predators, and the mechanisms for negative responses of native species to Burmese pythons. Furthermore, these results suggest that removal or significant population reduction of Burmese pythons may be the only way to curb their negative impacts. 

CONCLUSION

The purpose of this review was to examine the effects of the Burmese python in the GEE through predation, introduction and alteration of disease transmission, and potential range expansion. It is evident from this review that the Burmese python, through predation trophic alteration, has had severe effects on the native fauna of the GEE. Ultimately, it is the lack of coevolution between the Burmese python and native fauna that have led to the acute and persistent problems in the GEE. Burmese python establishment in the GEE has proved to be extremely detrimental to an ecosystem already facing considerable anthropogenic stressors. Given this, special attention should be paid to curb further Burmese python expansion to avoid similar ecological catastrophes due to the Burmese python. Further studies should be conducted regarding native resilience and recovery as populations eventually enter the third stage of invasion. Additionally, studies should be conducted to better quantify python density as to frame future understanding of ecosystem dynamics. The Burmese python is a prime example of many regarding invasive species across the globe. So, it is not only critical to better understand these aspects of python success and native fauna response, but the results may be applicable in the broader effort to manage invasive species. 

REFERENCES

  1. South Florida Water Management District. 2022. History of the Greater Everglades Ecosystem: Role of the Everglades in the Greater Everglades ecosystem.
  2. Office of Economic & Demographic Research. 2022. Annual Assessment of The Everglades. 5:1-19.
  3. Congressional Research Service. 2017. Everglades Restoration: Federal Funding and Implementation progress.
  4. Defenders of Wildlife. Greater Everglades.
  5. National Park Service. 2021. Everglades National Park Frequently Asked Questions.
  6. Florio. 2021. Removing the cork in the bottle: Reconstructing Tamiami Trail to restore water flow to Everglades National Park. 
  7. Willson, et al. 2011. Biol Invasions. 13: 1493-1504. 
  8. Pittman & Bartoszek. 2021. BMC Zoology. 6(33).
  9. Nafus, et al. 2020. Journal of Herpetology. 54(1): 24-30. 
  10. Janos. 2020. How Burmese Pythons Took Over the Florida Everglades. History. 
  11. Bartoszek, et al. 2021. Ecosphere. 12(6). 
  12. Burkett-Cadena, et al. 2021. Communications Biology. 4(804). 
  13. Conyers & Sen Roy. 2021. Spatial Information Research. 29: 749–760. 
  14. Hart, et al. 2015. Animal Biotelemetry. 3(8).
  15. Mutascio, et al. 2018. Landscape Ecology. 33, 257–274. 
  16. Smith, et al. 2021. Sci Rep-UK. 11(7014). 
  17. Walters, et al. 2016. Journal of Herpetology. 50(1): 50-56.
  18. Pittman, et al. 2014. Biology Letters. 10(3). 
  19. Jacobson, et al. 2012. Integrative Zoology. 7(3): 271-285. 
  20. Card, et al. 2018. Molecular Ecology. 27(23): 4744-4757. 
  21. Rodda, et al. 2009. Biol Invasions. 11: 241–252. 
  22. Pyron, et al. 2008. PLOS ONE. 3(8): e2931.
  23. Avery, et al. 2010. Biol Invasions. 12: 3649–3652.
  24. Dorcas, et al. 2011. Biol Invasions. 13: 793–802.
  25. Mazzotti, et al. 2016. Ecosphere. 7(8): e01439. 
  26. Osland, et al. 2021. Glob Change Biol. 27(13): 3009-3034. 
  27. Hunter, et al. 2018. Ecol Evol. 8(17): 9034-9047. 
  28. Miller, et al. 2018. Ecol Evol. 8(2): 830–840. 
  29. Miller, et al. 2020. Ecosphere. 11(6): e03153. 
  30. Walden, et al. 2020. Frontiers in Veterinary Science. 7:467.
  31. Farrell, et al. 2019. Herpetological Review. 50(1): 73-76.
  32. Reeves, et al. 2018. PLOS ONE. 13(1): e0190633. 
  33. Dorcas, et al. 2012. PNAS. 109(7): 2418-2422. 
  34. Taillie, et al. 2021. Biol Conserv. 261: 109290.
  35. McCleery, et al. 2015. P. Roy Soc B-Biol Sci. 282(1805). 
  36. Sovie, et al. 2016. Biol Invasions. 18: 3309–3318. 
  37. Soto-Shoender, et al. 2020. Biol Invasions. 22: 2671–2684. 
  38. Beckmann, et al. 2021. J Mammal. 103(1): 136–145.

Recovering Individual Based Model Outcomes on Spatiotemporally Coarsened Data

March 2, 2022

By Sameerah Helal, Applied Mathematics, Under supervision of Stephanie Dodson

Author’s Note: Individual Based Models (IBMs) are commonly used to study animal migrations and foraging behaviors. These flexible models are powerful in identifying the mechanisms driving animal movement; however, when fed spatially or temporally coarse environmental data, IBMs can often produce inaccurate model outcomes. Here, we investigate how model adaptations can mitigate negative consequences of poor data using an IBM of blue whales. Specifically, we find that models running on coarse data lead the simulated whale agents to clump together in their foraging behaviors and migrations paths. Algorithm adaptations, like altering the rate at which the whales update their locations, can reduce the locational clustering effect incited by spatially coarse data, and introducing available fine data to coarse data can mitigate the behavioral inaccuracies caused by temporally coarse data. These changes improve verified utilization distributions of whale positions, behavioral state plots, and associated metrics.

 

1 Introduction 

Recognizing how species interact and forage within their environment is an important aspect for understanding animal migrations. Predicting movement and behaviors given environmental conditions is crucial for understanding how changes in environment, like climate change, will affect future migrations. Though many species migrate, we focus on the foraging behaviors of Northern Pacific blue whales. 

The largest animal to have ever lived, North Pacific blue whales are well known marine mammals and seasonal migrators [1]. In winter, they can be found in breeding grounds off the coast of Mexico and Baja California; during summer and fall, they migrate to foraging grounds off the coast of California. Blue whales forage only on krill and need to consume large amounts of prey to meet their energetic needs. Their seasonal foraging is timed with coastal upwelling in the California current system, which leads to high densities of krill. Blue whales are also a threatened species, and their population growth is hindered by human intervention in the form of shipping lanes, discarded fishing gear, and changing climate [1]. 

Modeling is a common method used to understand animal migrations and foraging behavior. Individual Based Models, or IBMs, treat individual animals as autonomous agents whose movements and behaviors are governed by a set of probabilistic rules. An example of this is the IBM in [2]. This model has individual, autonomous, simulated blue whale agents interact with and move through a domain: a restricted geographic area throughout which the agents are allowed to move; at any time they occupy either a transit or forage state. The state is selected based on krill density and sea surface temperature (SST) [2]. This model was used to evaluate the relative importance of prey and environmental factors in driving movement distributions of whales. Previously, the IBM was only run effectively on very fine spatiotemporal resolution Regional Ocean Modeling System (ROMS) data: computer generated sea data that is assimilative and hindcast, meaning that it gives realistic ocean conditions constructed from past observations. With ROMS being computationally expensive to generate and not available in real time, ROMS data is not feasible to use for current prediction purposes. 

Our long-term goal is to predict real whale locations and behaviors by simulating whale agents on current environmental conditions using real-time satellite data. However, satellite data is messy and often of poor resolution, having spatiotemporal resolutions as coarse as 12 km grid cells and 8 day time-steps compared to the ROMS data’s 3 km and 1 day resolutions. A time-step is the length of time between each day for which the satellite has collected data. This poor resolution data yields inaccurate predictions and misleading results when applied to the blue whale IBM models. 

Figure 1, created using output from our own models, highlights that models run on low resolution data return misleading predictions of whale positions, forecasting them to be clustered in much smaller areas than they actually are. In Figure 2, which shows the proportion of whale agents foraging over time and gives a sense of population-level behaviors, it can be seen that the models inaccurately recreate whale behavior by causing a behavioral update lag that corrects itself in jumps. Similarly unrealistic predictions are expected for IBMs of other animal populations. 

In this study, our objective is to understand how to adapt the IBM developed by Dodson et al., as well as the data, to produce realistic predictions in spite of poor input resolution [2]. To do so, we will coarsen the original ROMS data to mimic poor satellite data, then compare the results of the IBM run on the coarse data to the results of the model run on the unaltered ROMS data. From there, we will formulate model adaptations that will yield accurate results from the IBM, even when run on coarse data. 

In the methods section, we detail the functions of the IBM, our data manipulation methods, and 

Figure 1: Contour map comparing the 95% utilization distribution map for the Gold Standard and 12 km resolution on September 1, 2008. Points are whales from the 12 km population. 

our proposed adaptations, as well as the metrics to compare outputs of the IBM. In the results, we describe the efficacy of our solutions, showing quantitatively and qualitatively how they improve the model output. We analyze these results, their advantages, shortcomings, and future implications in the discussion section. 

2 Methods 

2.1 The ROMS Data 

Throughout the study, we utilized ROMS (Regional Ocean Modeling System) Data, with two geospatial fields: sea surface temperature (SST) and krill [3] [4]. The domain is located off the California coast (32 − 42◦N Latitude and 116 − 128◦W Longitude) for the year 2008. 

The datasets are identical in shape and structure, but contain different information: SST is sea surface temperature in Celsius and krill is the relative density with values between 0 and 1. The resolution of this data, which we will henceforth refer to as the ‘gold standard’, is 3 km spatial and 1 day temporal. Imagine the environmental data to be two-dimensional maps stacked vertically, one for each time interval (Figure 3). 

2.2 Description of the IBM 

Our IBM, or individual based model, takes inputs of sea surface temperature (SST) and krill data as environmental information that whale agents interact with and use to inform their movements around the domain. This choice of input is motivated by the fact that real-life whales partially decide their movements based on krill availability, which is influenced by sea surface temperature. Note that all mentions of whales from this point onward refer to the simulated whale agents in the IBM. As they progress through the model, the whales can occupy either a transit state (S1), where they simply travel through the domain, or a foraging state (S2), where they “forage” for krill; the movement distribution of each state is dictated by the step length and turning angle variables. These behavioral states are inspired by the analysis of blue whale tagging data in [5]. This type of model 

Figure 2: Proportion of population foraging over time for the Gold Standard and 8 day resolution. 

is also referred to as a “state-switching” or “semi-Markovian” model, respectively because the whales switch to random states and because the switch depends largely on their current and previous locations. In a fully Markovian model, the state variable would not depend at all on past information, only current.

Movement updates are parametrized by step lengths and turning angles. On any position update, the turning angle determines at what angle from its current trajectory a whale should turn, and the step length determines how far in that direction to travel. For each whale these parameters are drawn from respective Gamma and Von Mises, two probability distributions with their own parameters µ and σ. Distinct distributions are defined for whales in the foraging versus transit state. The distributions of the step length and turning angle are also functions of the time intervals given as parameters in the model. They have moments µ = h · 3700, σ = h · 2150 for the transiting state, and µ = h · 1050, σ = h · 970 foraging state [5]. Here, h represents the number of hours in one time-step (the amount of time between a location update) as follows: 

Figure 3: Visualization of the SST and krill data as a 3-dimensional tensor. 

(a) Forage state

(b) Transit state 

Figure 4: Step length probability distributions for whales in forage or transit state for rate 1, 2, and 4. 

Note that, while the step length and turning angle themselves are random, the absolute direction in which the whale turns depends on its current and previous location, which form the start of the zero degree angle from which the whale will turn. Hence, the “semi-Markovian” reference. At every update, each individual reads its local SST and krill conditions, which influence the probability of foraging through a distribution defined by 

where z = 3 is a normalization parameter that ensures that the probability does not exceed 1, w1 = 1 and w2 = 2 are weights, and P SST and P krill are the respective probability distributions of foraging given SST and krill.

Lower SST and higher krill densities in a whale’s surroundings are associated with an increased likelihood of it either switching to or continuing in the foraging state (Figure 5). 

(a) Based on surrounding SST 

(b) Based on surrounding krill density 

Figure 5: Probability of a whale foraging based on its environment. 

To summarize the process: the IBM is initiated on May 1st in the southern portion of the domain, and at each update, the whales’ movement aligns with a movement distance based on the distributions in Figure 4 and their current location. Their foraging state is determined from their environmental conditions. This update cycle repeats until the end of the simulation, which, in this case, is the end of the year. 

2.3 Data Coarsening in Space and Time 

We manipulated the ROMS data to mimic the data collection shortcomings of satellites to achieve data coarsening. Specifically, satellite readings are plagued by coarse spatial and temporal resolutions. These are in part due to a satellite’s movement, type of readings, and presence of cloud cover. Making the ROMS data ‘bad’ in the way that satellite data is flawed required coarsening the data in space and time. 

Spatial coarsening of the SST and krill ROMS data was accomplished through Matlab’s meshgrid and interp3 functions, for which the input data was the original 3 km spatial resolution, and returned it in lower resolutions of 6 km, 9 km, and 12 km. These coarser resolutions were selected to match available satellite data and test the limits of our methods. 

To make up for missing data over time, satellites tend to use moving means: averaging the data over an interval, shifting the interval forward, and repeating the process; thus, our temporal coarsening was accomplished through averaging the data over time. To obtain coarsened data with an n−day resolution, we averaged the data in every n time-slices. The n slices were compressed into a single slice, resulting in a total of 1/n times the number of original time-slices. We performed this temporal averaging for 3 days and 8 days to mimic time resolutions consistent with satellite data. 

Figure 6: SST data from September 1, 2008, coarsened temporally and spatially. The axes of each map show the number of grid cells from the bottom left corner. 

Note that minor alterations to the IBM’s parameters, not mentioned because of their triviality, were required so that the model would run given the new dimensions of the data. 

2.4 Issues from Coarser Data 

Once we had coarsened the data, feeding it into the IBM no longer resulted in the same whale behavior or locations as the gold standard, which used data with 3 km, 1 day spatiotemporal resolution. Unlike many of the models based on coarser data, the gold standard results in whale locations spread over the coast with continuous population foraging behavior. In model iterations parametrized by the more extreme resolutions, like 12 km in space or averaged over 8 days in time, using the coarse data resulted in perceiving the whale population to act completely differently. 

When tracking movement based on data coarsened in space (Figure 1), the whales tended to move closer to each other as the simulation progressed, resulting in them clustering unrealistically at the coast instead of being spread over the domain. Adding temporal coarsening to the data only exacerbated this clumping effect. 

In addition to causing the whales to cluster over time, temporal coarsening alone also altered

Figure 7: Zoomed in utilization distribution maps of spatially coarse data vs. the gold standard. Curves show the 95% contour of the UDs. The full domain is shown on the right, with the regions on the left indicated by the orange box. 

whale behavior. Abrupt jumps in the percentage of whales foraging became apparent at the end of each time-slice, making the whales’ feeding behavior appear unrealistically stepwise for extremely coarse time (Figure2). 

Figure 8: Percentage of population foraging over time for temporally coarse data vs. the gold standard. 

These deviations from the gold standard output required that we alter the IBM or its inputs such that the IBM would continue to produce realistic results, even with poor data. 

2.5 Model Adaptations: Spatial 

In an effort to have the IBM running on coarse data reproduce the same movement and behavioral patterns as the gold standard IBM running on the gold standard ROMS data, we modified parts of its algorithm; in particular, we scaled the time-steps of the IBM. 

To mitigate the issues that arose specifically from spatially coarse data, we adjusted the rate at which the whales’ locations and behavioral states were updated. In the gold standard model, the whales take 4 steps per day (every 6 hours), with step length selected from a Gamma distribution with parameters µ0 and σ0. In order to recover realistic whale movements,  i.e. to have the whales move the same distance over the same amount of time, we scaled this distribution by a parameter h, defined in equation 1, to accommodate for the increased size of grid cells caused by coarse spatial data.

From the gold standard model’s rate of 4 time-steps, we changed our rates to 1 and 2, corresponding, respectively, to time-steps of 24 and 12 hours. This way, the whales updated locations less often, but took larger steps that were matched to the larger grid cells [5], preventing them from getting trapped in grid cells due to limited movement ability, allowing them to move freely as they did when basing their movements on the ROMS data. 

2.6 Model Adaptations: Temporal 

To mitigate the disruption to whale behavior caused by temporally coarse data in the IBM, we introduced temporally finer data (when available) to the coarsened data, resulting in a hybrid coarse-fine environmental dataset that we could pass to the IBM. Our intention was to develop a methodology for handling temporally coarse input data while remaining consistent with the available satellite data. Temporally finer satellite data (e.g. 1 day, or daily) is often prohibitively sparse for IBM use due to missing data from clouds or the location of the satellite, but 8-day averages are sufficiently dense. We used this structure to our advantage and augmented the coarse data with available finer resolutions. 

We regard the coarse data as our primary input, and the fine data as our secondary input. First, we created gaps in the coarse data by using Matlab’s randi function to select 30% of the indices in the data matrix uniformly at random. Then, to fill in the gaps in the coarse data, we replaced the randomly selected indices of the coarse data with the fine. In short, given temporally coarse data, we backed it up, or combined it with data of similarly coarse spatial, but finer temporal (1 day) resolution. For example, we might modify the 6 km, 8 day coarse data by replacing 30% of it with the finer 6 km, 1 day data. 

Note that combining data of different resolutions in this way required some interpolation of the coarser data to the larger dimensions of the fine. 

Before selecting 70% as the ratio of temporally coarse data, we analyzed real GOES (Geostationary Operational Environmental Satellites) satellite data recorded from the same area of the ocean as our ROMS data. Measurements of the GOES data are gathered by the GOES Imager, a multi-channel radiometer carried aboard the satellite. This satellite data is available in 6 km spatial resolution (about 0.05 degree latitude-longitudinal resolution) and in 1, 3, 8, 14, and 30 day composites averaged in time. Satellite SST data was extracted from NOAA GOES Imager Western Hemisphere satellite and accessed via [6]. Examples of data with few holes and many holes can respectively be found in Figure 9. 

(a) A day with very few holes

(b) A day with many holes 

Figure 9: SST GOES satellite data on days with varying amounts of holes. Dark blue indicates missing data. 

Plotting the percentage of the data that was missing (Figure 10) showed that the highest percentage of clouds at any time, for the first temporal resolution (1 day), was 80%, with an average of 40%. That is, on any given day, there is a wide range of data that might be missing. We also tested a range of coarse to fine ratios (i.e. percentages of missing data) on our own data, and the lowest percentage of fine data that returned reasonably improved results from no fine data at all was 30%. Thus, in what follows, we fix 30% as the amount of fine data to introduce, and 70% as the amount of ‘missing’ or coarse data. The improvements will be further discussed in the Results section; see table 3. 

2.7 Metrics 

The behaviors that we are trying to replicate in the gold standard are the foraging behaviors over time and over the whales’ habitat area. We used two established metrics to compare model outputs between the adjustments, as well as a third that we designed to be a combination of the two. 

The L2 norm, or the Mean Square Error, was used to measure how close the whales’ foraging behavior resulting from any of the models was to that of the gold standard model output. We extracted the vector of the percentage of whales foraging over time for both models and, after applying a moving mean, compared them using the L2 norm of the data. This metric provided an understanding of the foraging behaviors of the whale population through time. We define 

Note that XGS represents the output produced by the gold standard model. Lower L2 norms indicate a more accurate reproduction of the gold standard outcomes, and an L2 norm of zero indicates that the models have identical foraging behavior. 

Figure 10: Percentage of the missing data for real, daily satellite GOES data. Dashed vertical lines indicate the beginning of each month. 

Utilization distributions are probability distributions built from the data points of individual positions and are commonly used to understand and compare animal habitats. To quantify the accuracy of the utilization distribution produced by a given model, we used the volume intersection (‘VI’ in the adehabitathr R package) between the utilization distribution of the final positions of the whales from the given model versus the gold standard model. The positions were taken from September 1, 2008, a date by which the whales had had sufficient time to explore and interact with the domain. This gives a measure of how close the model output is to the gold standard in terms of whale positions. We refer to this volume as the ‘VI’. The equation for the VI integral is 

for model run X compared to the gold standard model at all points (x, y) in our domain. 

Because we are measuring overlap of the utilization distributions, a higher value indicates better model performance, with the maximum being 1. Due to the stochastic, meaning somewhat random, nature of the model, a VI value of 1 was not achieved even by comparing two of the same model runs from the gold standard, which returned an overlap value of around 99%; thus, for our purposes, VI values near 0.9 are considered good. 

The UDs and VI values were computed using R. We formatted the final whale positions of each model, labeled by the resolution of its input data, into a dataframe, then used the kerneloverlap and kernelUD functions from the R (version 4.0.2) package adehabitatHR to return the VI overlap value [7]. Our choice of setting the method argument to ’VI’ gave us the computed volume of intersection between the gold standard and coarse data model outputs. 

Finally, our own ∆ metric is the combination of the L2 and VI norms: for a model outcome X, we defined 

Since all efforts to adapt the model were done in order to produce equivalent results to the gold standard when fed into the simulator, our goal was to minimize the ∆ of the model; i.e. minimize the L2 and maximize the VI values. We measured the effectiveness of a model adjustment by quantifying model improvement using the fold decrease in ∆ values before and after the adjustment was applied. 

3 Results 

3.1 Changing Rate Counteracts Spatially Coarsened Data 

We focus first on purely spatially coarsened data, which has the same 1 day temporal resolution as the data used in the gold standard. The model run on spatially coarsened data resulted in unrealistic behaviors from the whales: resolutions lower than the gold standard of 3 km prevented the whales from exploring the entirety of the domain and caused them to move closer together as the IBM progressed. In particular, the agents ended up in clusters near the coast instead of spread over the domain. In an attempt to recreate the spread-out behavior of the gold standard IBM, we decreased the rate at which the whales updated their locations and behavioral states. 

Without any temporal coarsening, adjusting the rates significantly mitigated the clustering effects of spatial coarsening on the locations of the whales. Changing the rate in the IBM caused the whales to forage in generally the same areas with coarse data as they did with the gold standard data. The utilization distributions of the whales’ positions on the date September 1, which we noted was chosen as a sufficiently advanced time in the simulation to show model- representative whale behaviors (Figure 7), became visibly more similar to that of the gold standard. 

Table 1: Original and corrected VI for spatially coarsened data, with fold decrease in ∆ values. 

The original VI values for the coarse data for 9 km, and 12 km were 0.705, 0.480, and 0.256 respectively. Recall that higher VI values, closer to the maximum of 1, are more desirable. After adjusting the rate, these values became 0.710, 0.654, and 0.549; all improvements, even to the 6 km, which show less dramatic improvement due to already being relatively close to the gold standard. In addition , before changing the rate, the ∆ values were about 1.9, 2.8, and 3.0; all very high and 

Figure 11: Utilization distribution maps of spatially coarsened data with and without altered rates. 

indicative of undesirable model results that did not align with the gold standard. After changing the rate, these values reduced to 1.2, 1.4, and 1.7; at minimum a 1.6-fold decrease each, with the 9 and 12 km resolutions seeing the greatest favorable impacts. 

3.2 Introducing Finer Data Counteracts Temporally Coarsened Data 

We now consider purely temporally coarsened data, keeping the spatial resolution constant at 3 km. Recall that coarsening the data temporally caused issues with population-level whale foraging behavior. For lower temporal resolutions than the gold standard’s 1 day, the whales experienced abrupt, en masse changes in foraging state, causing the percentage of whales foraging over time to look unrealistically segmented. To mitigate this effect, we introduced finer data as a replacement for 30% of the coarse data as described in the Methods section. With the addition of the finer data, the foraging percentages were visually less step-like and numerically closer to the gold standard results. 

Table 2: L2 norms for temporally coarsened data before and after the addition of 30% 1 day data, with fold decrease in ∆ values. 

When naively fed temporally coarse data, i.e. when no changes are made to the data or the model to counter the effects of the temporal coarseness, the IBM results had ∆ values of 1.4 and 2.0 respectively for 3 day and 8 day. These high ∆ values are largely due to jump-like behaviors in the percentage of whales foraging. After replacing 30% of the coarse data with available fine data, we found that the ∆ values decreased over 1.6-fold and 2-old, to a value near 1. Similarly, the L2 norms decreased, as desired, from 1.2 and 1.7 to 0.77 and 0.93 for the coarse temporal resolutions of 3 day and 8 day. 

Figure 12: Proportion of the population foraging over time for temporally coarsened data with and without added finer data. 

3.3 Combined Spatial and Temporal Fixes 

Negative influences on whale foraging behaviors were amplified when the environmental data had both poor spatial and temporal resolutions: with no fixes, the spatiotemporally coarsened data had a minimum ∆ of 3 across all resolutions. Combining the fixes by substituting available fine data, then changing model rate was successful in improving the behaviors in the combined coarseness scenario. By reducing the jumps in the number of whales in each behavioral state, and increasing the overlap between the utilization distributions of the corrected and gold standard models, we were able to decrease the ∆ value to be capped at 1.6. This was at least a 2.6-fold decrease in ∆s for each of the spatiotemporally coarsened datasets, greater than for any of the adjustments applied to data that was coarse in only one dimension. 

Table 3: Fold decrease in ∆ for all spatiotemporal coarsening combinations. 

Figure 13: Bar chart of ∆ values for spatiotemporally coarse data before and after algorithm modifications. 

4 Discussion 

Our long-term goal was to run the IBM on satellite data to predict real-time locations of blue whales. However, the IBM had only been shown to produce realistic predictions on fine-scale input data. Specifically, gaps and poor resolution of SST and krill data would lead to inaccurate simulations and predictions of whale movements. To the end of mimicking this ‘bad’ data, we coarsened our data spatially and temporally. By decreasing the rate at which the whales updated their locations and introducing available fine data to the coarse data, we successfully improved  the results of the IBM when running on coarse data, as measured by the VI, L2, and ∆ metrics,. 

Of our two directions of coarsening, the first we dealt with was spatial. The lower spatial resolution (e.g. 12 km instead of the gold standard 3 km), or larger grid cells, caused the whales to clump together over time instead of exploring the entire domain. We anticipated that this was because the increased size of grid cells caused the whales to be trapped in areas of high foraging; the whales’ step lengths were too short to exit areas with high foraging probability. Since the step lengths are a function of the step rate, with larger average lengths corresponding to lower rates, by decreasing the rate at which the whales updated locations, the step length became better aligned with the spatial size of the grid cells. Larger step lengths prevented the whales from getting stuck in rich foraging locations and allowed agents to step out of a grid cell and continue to explore the domain. For example, changing the update from 6 to 24 hours (rate 4 to rate 1) shifted the average step length of foraging from 6.3 km to 25 km. The larger average step length then allowed individuals to exit a 12 km grid cell and continue to explore the domain. 

Our second direction of coarsening was temporal; lower temporal resolutions were apparent in sudden changes in the percentage of whales foraging. The jump-like behaviors were due to the whales operating on old information: the coarser the temporal resolution, the more outdated the whales’ knowledge of their surroundings. Our model correction was to provide the agents with the most up to date information when possible. Adding finer data into the coarse data removed the unrealistic jumps in the whales’ behavior and yielded population-level foraging activity that better resembled the gold standard. 

Our solution for temporal coarsening is particularly powerful because of its scale: we only required 30% of the coarse data to be randomly replaced with the fine to see great improvement. Analysis of GOES satellite data showed that the average missing data during the summer and fall, commonly due to cloud cover, is usually less than 40% and rarely exceeds 70%. Thus, utilizing 30% of 1 day temporal resolution data with 70% of temporally coarse data is a realistic quantity, and actually uses the upper bound of 70% for the amount of missing satellite data. We introduced the fine data to the coarse data uniformly at random as the natural first step, since it did not require any assumptions about the locations or shapes of the missing data. One next step would be to target localized, perhaps even moving, regions for more ‘cloud-like’ replacement. 

The methods we develop here are not at all unique to modeling blue whale populations; they can be applied to any individual based model in which the agents make decisions based on environmental input data. These solutions are particularly useful for models that, like ours, have inputs that are dynamic in space and time; nearly any IBM for animal movement could be similarly adapted. By incorporating finer temporal data when possible and aligning the step lengths to be of the same order as the spatial grid resolution, we believe other individual based models can be used with coarse environmental data to make predictions that are considerably more similar to those they might make with finer, more accurate data. 

The problem of predicting animal behavior based on environmental conditions is an important one, especially with continually changing impacts from humans and climate change. With these results and further exploration in our suggested direction, it is our hope that it will be possible to make accurate, real-time predictions of whale positions based on satellite data. 

 

References 

  1. Scales KL, Hazel EL, Jacox Mg, Edwards CA, Boustany AM, Oliver MJ, Bograd SJ. 2017. Scale of inference: on the sensitivity of habitat models for wide-ranging marine predators to the resolution of environmental data. Ecography [Internet]. 40:210-220. doi:10.1111/ecog.02272.
  2. Dodson S, Abrahms B, Bograd SJ, Fiechter J, Hazen EL. 2020. Disentangling the biotic and abiotic drivers of emergent migratory behavior using individual-based models. International Journal on Ecological Modelling and Systems Ecology [Internet]. 432. doi:10.1016/j.ecolmodel.2020.109225.
  3. Rose KA, Fiechter J, Curchitser EN, Hedstrom K, Bernal M, Creekmore S, Haynie A, Ito S, Lluch-Cota S, Megrey BA, Edwards CS, Checkley D, Koslow T, McClatchie S, Werner F, MacCall A, Agostini V. 2015. Demonstration of a fully-coupled end-to-end model for small pelagic fish using sardine and anchovy in the California Current. Progress in Oceanography [Internet]. 138(B): 348-380. doi:10.1016/j.pocean.2015.01.012.
  4. Fiechter J, Huckstadt LA, Rose KA, Costa DP. 2016. A fully coupled ecosystem model to predict the foraging ecology of apex predators in the California Current. Marine Ecology Progress Series [Internet]. 556:273-285. doi:10.3354/meps11849.
  5. Bailey H, Mate BR, Palacios DM, Irvine L, Bograd SJ, Costa DP. 2009. Behavioural estimation of blue whale movements in the Northeast Pacific from state-space model analysis of satellite tracks. Endangered Species Research [Internet]. 10:93-106. doi:10.3354/esr00239.
  6. 2019. SST, GOES Imager, Day and Night, Western Hemisphere. https://coastwatch. pfeg.noaa.gov/erddap/index.html.
  7. Calenge C. 2006. The package adehabitat for the R software: tool for the analysis of space and habitat use by animals. Ecological Modelling 197:516-519. doi:10.1016/j.ecolmodel.2006.03.017.

Lazarus Dies, Lazarus Lives Again

October 1, 2021

By Jesse Kireyev, History ‘21

Each of these photos captures a landscape in slow degradation. Berryessa, for all the wintergreen beauty that it holds, has experienced horrifying fires numerous times over the past few years. The natural bridge that dominates the landscape of its namesake park in Santa Cruz now remains alone, at risk of collapsing like its sibling did, forever leaving the shoreline empty of its beauty. This risk only grows as sea levels rise and as human interaction puts it at greater risk. The salt flats of the Dead Sea used to be covered in water — now nature struggles to fill the few remaining pools as the sea rapidly shrinks. Captured in these three horizons are the struggles of nature to sustain itself despite the present beauty. For all the tranquility of the Ansel Adams-esque lines jutting forth from the foreground, a great and slow war is playing itself out in the back, often hidden to the gazing eye of the unaware viewer. The horizons both serve as a reminder of the danger that lurks in our future, as well as the distant (and perhaps unreachable) hope of resurrection in the face of annihilation.  

 

1. Berryessa Foothills, Solano, California.

Storm clouds move over the fields and lush wetlands, both morphing into the mountains hugging Lake Berryessa. Just a few months prior, the mountains had been scorched by the dizzying flames of the LNU Lightning Complex Fire, a fire whose smoke blotted out the sun for weeks in two of the largest metropolitan areas in America. The ebb-and-flow of the surroundings give us a stark reminder of just how fast a place can be destroyed and can flourish once again from the ashes. Canon EOS 5D Mark III. April, 2021.

 

2. West Cliff, Natural Bridges State Park, Santa Cruz, California.

Pelicans and seagulls huddle together as they hunt for fish and fight the buffeting winds. The remainders of the natural bridges, which once dominated the state beach, still serve as a helpful vantage point for the seabirds. Locals hope that this vantage point can survive, even as climate change puts the bridge at greater risk every year. Canon EOS 630, Kodak Tri-X 400TX 35mm film. June, 2017.

 

3. Dead Sea Salt Flats, Masada, Israel.

The salt flats are all that is left of the once sea-filled expanse below Masada. A combination of climate change and human changes to the environment are driving the evaporation of the Dead Sea, which at current rates is expected to be gone in the next three decades. Sony a700. December, 2018.

The Universal Solvent

August 6, 2021

By Elaina Covey, Biochemistry & Molecular Biology ‘22

This is a digital painting I drew that was inspired by the importance of clean water on our planet. I painted this with my iPad Pro using the latest version of Procreate. The girl, who is the subject of this drawing, is meant to represent life on Earth. I stuck to a color palette consisting primarily of greens and blues to reflect nature and the planet. I was also inspired by a quote from American educator, Loren Eisely, who wrote in an essay titled “The Flow of the River” that “if there is magic on this planet, it is contained in water.” For this reason, I wanted to make a piece that evokes a feeling of magic and wonder. However, there is also a sense of danger. The girl, who is nearly drowning, serves to remind us that pollution in the form of oil runoffs, plastics, agricultural waste, and acidification threatens our oceans daily. Additionally, as carbon pollution increases and annual temperatures rise, sea levels are rising as well. The girl’s drowning may also serve to remind the viewer of the danger faced by many species who rely on our planet’s ice caps for survival. I hope this piece can inspire others to recognize the importance of protecting Earth’s amazing biodiversity. 

The Technological Impact on Coffee Growing in the Face of Climate Change

December 19, 2020

By Anushka Gupta, Genetics & Genomics, ‘20

Author’s Note:  Climate change is an important topic and must be discussed in order to mitigate the severe consequences. Unbeknownst to most people, however, coffee is also heavily impacted by climate change due to the sensitive conditions necessary for proper cultivation. I hope I can bring to light some of the less serious impacts of climate change and how something normal, like coffee, may become extinct without interference. 

 

Over fifty percent of Americans enjoy a daily cup of coffee, with over 500 million cups of coffee  served everyday. Unfortunately, with the increasing temperatures due to climate change, coffee is at high risk of extinction. However, with new advances in technology, coffee can now be grown in a wider range of environmental conditions. Specifically, the integration of modern technology to pre-existing growing practices and the use of artificial intelligence have both contributed to making a future with coffee a possibility.  

To understand the new developing technologies, it is crucial to understand the severity of climate change and how it specifically affects the coffee production business. Given the rapidly increasing rate of global temperatures, coffee will likely be much more expensive and be of a much lower level of quality within just 30 years. On top of this, the amount of land that is available for coffee growing will be cut in half by 2050, according to Climate Institute, a company in Australia. Coffee is grown mostly in tropical regions, like Honduras and Brazil, which also happen to be the regions hardest hit by climate change. In fact, the top countries that are most affected by climate change are the same countries where the majority of the world’s coffee beans are grown. 

This becomes problematic as coffee beans are also extremely sensitive to temperatures outside of their ideal growing temperature. Most coffee beans will only grow in the range of 18°C to 21°C at high altitudes. They also require a perfect amount of rain, as anything outside of these optimal conditions will damage or even kill the plants. Climate change has already had its effects on coffee production around the world. Heavy rains in Columbia, droughts in Indonesia, and coffee leaf rust (a fungus that attacks coffee bean leaves) in Central and South America have significantly decreased the coffee yield in the past few years. These are only a few of the many examples of how coffee growers are struggling to maintain their crop yield each year [1]. 

One way coffee growers are preparing for climate change is by engineering a new resistant strain of coffee beans. Currently, only one type of coffee bean, Arabica, dominates the entire industry. Arabica is known for its high quality flavor and aroma, but lacks genetic diversity, commonly leaving it susceptible to coffee leaf rust [2]. The coffee leaf rust fungus preys on the leaves of coffee plants, and eats away at the leaf until an orange-brown color is left instead of the previous green leaf, thus destroying the plant’s ability to make its own energy [3]. The lack of genetic diversity allows for this fungus to spread rampantly across coffee bean farms. For instance, if one strain of the fungus affects a particular variety of Arabica, it is also extremely likely that other Arabica plants will also be afflicted [2]. With increasing temperatures already posing a greater threat to plants, the leaf rust fungus is expected to have an even more apparent impact on coffee yield. In addition, the availability of farmable land is decreasing, as coffee plants only grow in a narrow range, a range that is shrinking due to increasing global temperatures. However, creating a hybrid coffee bean can resolve this problem by choosing strains in hopes of achieving a desired quality, such as coffee leaf rust resistance [2].  

Coffee breeder William Solano works at the tropical agricultural research and higher education center (CATIE) in Costa Rica doing just that. He works on creating coffee hybrids by combining genetically distant yet complementary coffee strains in hopes to achieve a product that takes in characteristics from each parent coffee strain [3]. At CATIE, he created the Centroamericano coffee bean, a cross between the Ethiopian landrace variety Rume Sudan and another coffee bean called T5296, which is known for its coffee leaf rust resistance [2]. On its own, the Centroamericano has proven to be twenty percent more productive than other coffee beans and is tolerant to coffee leaf rust. However, it soon became clear that it fares better against the effects of climate change as well, as it can survive temperatures below freezing. This was an especially surprising find as the plant was originally designed with only disease resistance in mind [3].  

On top of changing the coffee bean, scientists are also finding a way to use new technologies to make coffee farming more efficient. The most promising example seen so far has been the implementation of artificial intelligence (AI) to the coffee growing business. AI technology now allows farmers to accurately analyze soil fertility properties and compute an estimation of coffee yields [4]. The American technology company IBM has developed an AI-powered device that does just that. The device, called the AgroPad, is portable and about the size of a business card. This device has the capability to quickly analyze the soil to check for chemical composition, allowing coffee farmers to make educated decisions on how to manage their crops. Coffee growers can improve sustainability of their crops as well as save money since they know the amount of water and fertilizer that would be most beneficial to the crop to maximize yield. 

To activate the device, only a small sample size is needed. The sample can either be a drop of water or a liquid soil extract that is produced from a pea-sized clump of dirt, depending on the type of analysis needed. Within about 10 seconds, the device will generate a report using a microfluidics chip inside that performs data analysis. The device can give accurate information on the pH, and amount of various chemicals, such as nitrite, aluminum, magnesium, and chloride. This information is given in the form of circles that correlate to the soil composition. These circles give colorimetric test results, where each circle will represent the amount of a specific chemical that is in the sample [6]. The figure below shows what a sample report may look like. Once this output is given out by the AgroPad, the farmer can use an app to take a picture of the output where the app will read the data. The implementation of this device could allow coffee to be grown in more parts of the world as it will be evident what specifically must be done to ensure the productive growing of coffee in these fields [5]. 

 

Dedicated mobile app scanning of a sample report created by AgroPad

Fig. 1. Peskett, Matt. “IBM’s Instant AI Soil Analysis – the AgroPad.” Food and Farming Technology, 28 Jan. 2020, www.foodandfarmingtechnology.com/news/soil-management/ibms-instant-ai-soil-analysis-the-agropad.html. 

 

Technology has the potential to save some of these coffee plants in the face of climate change, however, at the current rate of climate change, it is difficult to say how the world will look like thirty or even fifty years in the future, and if coffee will be a part of that world. Hopefully, more technological advances will continue to rise over the years giving hope to both coffee growers and coffee drinkers alike around the globe. 

 

Sources

  1. Campoy, Ana. “Another Species Threatened by Climate Change: Your Morning Cup of Coffee.” Quartz, Quartz, 3 Sept. 2016, qz.com/773015/climate-change-will-kill-coffee-by-2100/. Accessed 1 Jun. 2020.
  2. Mu, Alejandra, and Hernandez. “Coffee Varieties: What Are F1 Hybrids & Why Are They Good News?” Perfect Daily Grind, Perfect Daily Grind, 20 Apr. 2020, perfectdailygrind.com/2017/06/coffee-varieties-what-are-f1-hybrids-why-are-they-good-news/#:~:text=Centroamericano%20is%20a%20cross%20between,high%20yielding%20and%20rust%2Dresistant.&text=SEE%20ALSO%3A%20Bourbon%20vs%20Caturra,Variety%20%26%20Why%20Should%20I%20Care%3F. Accessed 1 Jun. 2020.
  3. Ortiz, Arguedas. “The Accident That Led to the Discovery of Climate-Change-Proof Coffee.” MIT Technology Review, MIT Technology Review, 2 Apr. 2020, www.technologyreview.com/2019/04/24/135937/the-accident-that-led-to-the-discovery-of-climate-change-proof-coffee/. Accessed 1 Jun. 2020.
  4. “The Future of Coffee: 3 Technologies to Be on the Lookout for in 2019.” Royal Cup Coffee, 28 Dec. 2018, www.royalcupcoffee.com/blog/articles/future-coffee-3-technologies-be-lookout-2019. Accessed 1 Jun. 2020.
  5. “Enveritas Pilots IBM’s AI-Powered AgroPad to Help Coffee Farmers.” IBM Research Blog, 10 Dec. 2019, www.ibm.com/blogs/research/2019/12/enveritas-pilots-ibms-ai-powered-agropad-to-help-coffee-farmers/. Accessed 1 Jun. 2020.
  6. “No Farms, No Food.” IBM Research Blog, 7 Mar. 2019, www.ibm.com/blogs/research/2018/09/agropad/. 
  7. “IBM’s Instant AI Soil Analysis – the AgroPad.” Food and Farming Technology, 28 Jan. 2020, www.foodandfarmingtechnology.com/news/soil-management/ibms-instant-ai-soil-analysis-the-agropad.html.

Will This Pandemic Unite Us Against Climate Change?

October 31, 2020

By Pilar Ceniceroz, Environmental Science and Management ‘21

Author’s Note: I originally wrote this piece for a UWP104E assignment. However, the topic remains relevant to people all around the world. In the past, it has been hard to visualize our individual impacts on the environment. COVID-19 has become a great example of how behavioral changes can drastically transform our surroundings. I would like my readers to understand the power of unity in the face of what might be the next global crisis, climate change. 

 

Introduction 

After the World Health Organization (WHO) declared a global health emergency on January 30th 2020, the world has seen extreme changes as the daily lifestyle of almost everyone in the world has been rapidly altered [1]. The ongoing effort to slow the spread of the virus while sheltering-in-place has not been without sacrifice—countless people lost their jobs, most cannot physically go to school, and everyday activities have been significantly modified. However, this halt of “business as usual” has fascinating impacts on the environment. Stay-at-home orders shut down production in industrial facilities and power plants and minimized personal vehicle use [2]. With a major decrease in economic activity, highly polluted cities around the world are now seeing clearer skies. The seemingly dull, repetitive routine of quarantine life has allowed the environment to flourish.While consequences of COVID-19 include global economic devastation, the environment has seen both indirect positive and negative impacts as a result of stay-at-home orders and the declining economy. Regions with COVID-19 restrictions experienced a decrease in air and water pollution. These restrictions included a stop to nonessential work and travel as well as closing of restaurants and bars. Concurrently, the amount of single use products has significantly increased to limit the spread of the virus. Decreasing air pollution is a major milestone for our modern world, however, as the world returns to normal life, pollution levels will follow. Although a short term decrease in greenhouse gas (GHG) emissions is not a sustainable way to support the environment, communities around the globe have witnessed the instantaneous impacts of our everyday habits on the environment due to COVID-19. 

 

Drop in Atmospheric and Water Pollution 

Today, 91% of the world population lives in places where poor air quality exceeds the permissible limits set by the WHO [2]. Air quality is an important contributor to human health and living in an area with poor air quality can exacerbate the symptoms of COVID-19. According to the 2016 WHO report, air pollution contributes to 8% of total deaths in the world [2]. Countries that normally struggle with unhealthy air, such as China, USA, Italy, and Spain, have since seen clearer skies for the first time in decades after taking aggressive measures to slow the spread of the virus. There has been a dramatic decrease in the amount of CO2, NO2, and particulate matter emitted in China with the halt of industrial operations with the decrease in demand for coal and crude oil (see fig. 1) [3]. 

 

Changes in nitrogen dioxide emission levels in China from before and after lockdown. 

Fig. 1. Zambrano-Monserrate, Manuel A., et al. “Indirect Effects of COVID-19 on the Environment.” Science of The Total Environment, vol. 728, 20 Apr. 2020, p. 138813., doi:10.1016/j.scitotenv.2020.138813.

 

Since this same time last year, air pollution levels have dropped 50% in New York [1]. There has been a 25% decrease in air pollution since the start of this year in China, one of the largest manufacturing countries [1]. The closing of factories contributed to a 40% reduction in coal usage at one of China’s largest power plants [1]. The average coal consumption of power plants has reached its lowest point in the past four years [3]. Clearly, the outbreak has improved short term air quality and has contributed to reducing global carbon emissions. Fewer flights and social distancing guidelines have reduced carbon emissions as well as other forms of pollution. Tourism significantly decreased worldwide, and beaches around the world have been cleaned up. For example, citizens of Venice, Italy were amazed to see crystalline waters and healthy fish in their canals [1]. 

 

 Comparison of air quality in some of the biggest cities around the world before the COVID-19 pandemic and while the lockdown. 

Fig. 2. Saadat, Saeida., et al. “Environmental Perspective of COVID-19.” Science of The Total Environment, vol. 728, 22 April 2020, p. 138870., doi:10.1016/j.scitptenv.2020.139815. 

 

Increased Single Use Plastics 

In order to completely analyze the impact of COVID-19 on environmental health, the negative impacts on the environment from the virus are equally as important as the positive effects. Although travel restrictions have led to less pollution caused by tourism, the amount of single use plastics and medical equipment has significantly increased waste around the world. In the USA, there has been a significant increase in the amount of single-use personal protective equipment, such as masks and gloves [1]. 

Imagine the amount of trash created when millions of people use one or a couple of masks daily, single use gloves and hand sanitizers. With a population of eleven million people, the city of Wuhan produced an average of 200 tons of clinical trash on any single day in February 2020, compared to their previous average of fifty tons per day [1]. This number is four times the amount the city’s only dedicated facility can incinerate per day [1]. 

The demand for plastics has increased as consumers move to online purchasing. Shelter-in-place guidelines established in most countries have driven consumers to increase their demand for online orders and home delivery [2].The increasing demand for shipping and packaging greatly increases the amount of waste produced as well as GHG emissions with increased activity in supply lines. Out of concern of spreading the virus through the plastic surfaces in recycling centers, some cities stopped their recycling programs in the U.S. [2]. Additionally, in some of these cities, citizens are not allowed to use reusable bags at grocery stores. Similarly, some European cities have seen restrictions within waste management. Italy has prohibited infected residents from sorting their personal household waste [2]. Industries have repealed the disposable bag bans, many have switched to single-use packaging, and online food ordering has increased in popularity [2]. The consumption of single use plastics has skyrocketed to limit transmission [1, 3]. Suspension of sustainable waste management practices potentially escalate environmental pollution. 

 

Medical wastes generated during COVID-19 pandemic in the environment.

Fig. 3. Saadat, Saeida., et al. “Environmental Perspective of COVID-19.” Science of The Total Environment, vol. 728, 22 April 2020, p. 138870., doi:10.1016/j.scitptenv.2020.139815.

 

Where Do We Go From Here? 

Over the last few months, people were enamoured by the modern-day pollution that vanished before their eyes. Strict stay-at-home orders decreased the amount of air and water pollution in otherwise unhealthy cities. Contrarily, the considerable increase in single use plastics may have a lasting negative impact on the environment. Although these outcomes may be hard to compare in magnitude, they help put into perspective the larger picture. Short term change is not a sustainable way to clean up the environment especially when it occurs alongside economic devastation. Before the pandemic, individual action against climate change felt like an abstract idea, out of reach due to its lack of immediacy. Now, the world has seen changes to our environment from worldwide behavior. Visible skies and vibrant waterways are distinguishable changes that are legitimate grounds to build momentum and take action for a healthier future. Although the pandemic may not have a drastic impact on the future of the environment itself due to conflicting effects, it can instigate discussion to improve personal actions that impact the environment. Long-term structural change and individual behavior changes are critical in combating environmental pollution. Moving forward, it is imperative that the unification of collective conscious behavior be a driving force to combat climate change. If neglected, climate change is likely to take many lives in the future, portraying this pandemic as a minor devastation. Let the urgency of our united global response to COVID-19 influence our future response to the next global crisis; climate change. 

 

References

[1] Saadat, Saeida., et al. “Environmental Perspective of COVID-19.” Science of The Total Environment, vol. 728, 22 April 2020, p. 138870., doi:10.1016/j.scitptenv.2020.139815. 

[3] Wang, Qiang, and Min Su. “A Preliminary Assessment of the Impact of COVID-19 on Environment – A Case Study of China.” Science of The Total Environment, vol. 728, 22 Apr. 2020, p. 138915., doi:10.1016/j.scitotenv.2020.138915.

[2] Zambrano-Monserrate, Manuel A., et al. “Indirect Effects of COVID-19 on the Environment.” Science of The Total Environment, vol. 728, 20 Apr. 2020, p. 138813., doi:10.1016/j.scitotenv.2020.138813.

The Parable of the Passenger Pigeon: How Colonizers’ Words Killed the World’s Largest Bird Population

October 23, 2020

By Jenna Turpin, Wildlife, Fish, and Conservation Biology ‘22

Author’s Note: I started this piece as an assignment for my undergraduate expository writing class under the guidance of my supportive professor Hillary Cheramie. Hillary urged me to take my writing beyond her course. In May, I had the wonderful opportunity to share this research at the 2020 UC Davis Annual Undergraduate Research Conference. I want to continue to share my work through publication. I wrote this piece with the intention of inspiring both students and teachers. From this paper, students can learn the parable of the passenger pigeon and teachers can come to understand why teaching about the passenger pigeon matters.

I learned of the passenger pigeon during my first week of college at UC Davis. One of my professors, Dr. Kelt, explained a brief history of the passenger pigeon to my first-year wildlife ecology and conservation class. The lesson was about wildlife-human interactions and the destruction humans can execute on the environment. The passenger pigeon’s story shook me to my core. It was a disturbing portrayal of how people sometimes negatively shape ecosystems. For me, it reinforced all of the reasons I decided to study wildlife conservation. I want people who read this piece to feel the emotions I felt when I first took in the parable of the passenger pigeon and come to the belief that humans have a responsibility to conserve species through management, policy, and education. The more people who hear this parable, the more people who hold sympathy for our wildlife. It should be built into schools’ science and history curriculums. A greater understanding of the passenger pigeon will save future species from extinction.

 

Abstract

Genre is the literary process through which people collectively communicate about a topic. Applied to a species, genre helps us understand how society communicates about that animal. Species’ genres change over time as different people interact with them. This influences human-wildlife interactions and thus plays a critical role in determining the fate of that species.  In the passenger pigeon’s (Ectopistes migratorius) prime, it was the most abundant bird species in existence but went extinct. The dynamics of human-wildlife interactions over time defined the progression of the passenger pigeon’s recorded history. These interactions varied based on how the dominant people in North America thought about the bird and the genre surrounding its existence. The parable of the passenger pigeon is a poignant example of why genre matters in preserving species and how this can go wrong. The analysis of the historical evolution of the passenger pigeon’s genre showed that the European colonization of North America is why these birds went extinct. I conducted a survey that showed that the passenger pigeon’s genre is fading among young people. Failing to spread the parable of the passenger pigeon is a threat to every currently endangered species and their respective genres.

 

Introduction

The passenger pigeon (Ectopistes migratorius) lived in North America and was described as having a “small head and neck, long tail, and beautiful plumage” [1]. In its prime, it had the largest population size of any bird species at the time but went extinct due to overexploitation and habitat loss caused by European settlers [1]. The dynamics of human-wildlife interactions over time defined the progression of the passenger pigeon’s recorded history. 

These interactions varied based on how the dominant people in North America thought about the bird and the genre surrounding its existence. Genre refers to “repeating rhetorical situations” to aid human interaction. In other words, it is the collection of how people refer to a specific topic. The definition of genre can be applied broadly. Genres are dynamic and develop over time, as people face new situations to apply them to. Every species has its own genre surrounding its existence. People participate in many genres on a daily basis, even if they do not know it. Genre is a “social action,” people shape genres and genres shape people [2].  The way groups of people collectively feel about anything is communicated through language. Thus, looking at the way people talked about passenger pigeons explains the processes that led to their downfall. The passenger pigeon is an effective ambassador for teaching youth about conservation because of the population’ rapid decline.

 

Historical Evolution

Indigenous People 

The passenger pigeon’s parable begins with Indigenous people who lived within the range of the bird, mostly covering only the Eastern half of America [1]. These Indigenous people were the first humans to interact with the passenger pigeon and create its genre. Simon Pokagon, a Potawatomi tribe member was interviewed about seeing them in flight: “When a young man I have stood for hours admiring the movements of these birds. I have seen them fly in unbroken lines from the horizon…” [3]. Under Indigenous peoples’ care, the passenger pigeon numbers rose beginning in the years 100 to 900 C.E. [4]. This was because Indigenous people and passenger pigeons had a well-balanced relationship that allowed both populations to thrive.

Indigenous people carefully interacted with the passenger pigeon because it was an important game bird to them, second only to the wild turkey [1]. The passenger pigeon was a staple food of the Seneca, who named the bird jah’gowa, meaning “big bread” [4]. Tribes followed specific procedures for hunting the birds. Almost all tribes had a strict policy—based in both religion and biology—against taking nesting adult passenger pigeons. This strategic wildlife management policy promoted chick survival by allowing parents to care for their young. The Sioux and the Iroquois League were among those known to enforce their rules on other hunters. Instead of hunting the birds during this time, they often used nests as an opportunity to closely observe the bird. Individual tribes also had additional policies. For the Ho Chunks, hunting of the passenger pigeon could only happen if the chief held a feast. When the birds returned in spring, they offered much needed seasonal food. Before the Seneca began the hunt, they monitored the nests until the chicks were two to three weeks old. The Seneca even went as far as managing the habitat of the passenger pigeon, for instance they did not allow the cutting of any tree a “chief” pigeon nested in [4]. Chief Pokagon of the Potawatomi tribe credits strategies such as this for not only allowing the pigeon to maintain its numbers but actually increasing them [1]. By thinking about the needs of the pigeons and adjusting behaviors to accommodate those needs rather than freely hunting them, the population was able to continue on as a reliable food resource for the tribes that used them.

Furthermore, the connection between the two species went beyond the typical predator and prey relationship. To many Indigenous people, the pigeons were not just food, they were a being. Passenger pigeons were included in the religion of some tribes through stories, song, and dance [1]. The Seneca believed that the pigeon gave its body to create their children. The passenger pigeon was so important to the Seneca that they termed albino ones “chief of all pigeons” and strictly forbade hunting them. The Cherokee and the Neutrals told similar stories of the bird as a guide to avoid starvation. The Seneca and the Iroquois opened their Maple Festival every year with a dance song about the bird. The Cherokee Green Corn Festival featured a dance mimicking a pigeon hawk in pursuit of a pigeon [4]. The pigeons held value in the lives of the people who benefited from them.

 

European Arrival

The Europeans recorded their first passenger pigeon on July 1, 1534 [1]. Right away, colonizers of every walk of life made note of the massive number of pigeons Indigenous people had maintained.  The average European enjoyed the sight, “…I was perfectly amazed to behold the air filled and the sun obscured by millions of pigeons…” [1]. Many accounts told the narrative of an undiminishable population. Schorger, a professional ornithologist confirmed this notion, stating that “no other species of bird, to the best of our knowledge, ever approached the passenger pigeon in numbers” [1]. More ornithologists like Alexander Wilson took records, “In the autumn of 1813…I observed the pigeons flying from northeast to southwest, in greater numbers than I thought I had ever seen them before…The light of the noon day was obscured as by an eclipse” [5]. Even Leopold described them as a “biological storm” that used the resources of the land to their advantage [6].

While everyone knew the birds to be copious, not everyone understood the science behind it. Ornithologists knew that the pigeons could thrive because they had ample food and habitat when the Europeans arrived. However, for the vast majority of Europeans who were not trained in biology, the flock of birds blocking out the sky was frightening and unexplainable. This is where the genre began to separate itself from Indigenous peoples’ understanding of the bird. Europeans constructed urban legends in an effort to explain what was unknown to them. When only one acorn was found in pigeons’ crop (food storage pouch), Europeans predicted death and sickness. The evidence they saw supported their beliefs, “It is a common observation in some parts of this state, that when the Pigeons continue with us all the winter, we shall have a sickly summer and autumn” [1].  

 

Diminishing

As colonizers made themselves more at home in North America, they encroached on passenger pigeon habitat and depleted their numbers. The colonizers did not take wildlife management into consideration while hunting. Instead, they killed far more than they took and failed to leave young and nesting birds alone [1]. Extinction seemed entirely impossible, they did not see a need to ensure the next generation of pigeons could continue, it was understood as a given. To compound this, the birds were generally not thought of highly in European cultures. The passenger pigeon was merely a thing to exploit rather than a being to feel for. They began to disappear from the places humans occupied, retreating into what wilderness remained [3]. 

People began to notice that the passenger pigeon populations were fading. Some states, like Ohio, actively avoided policies to protect the species, claiming “the passenger pigeon needs no protection. Wonderfully prolific, having the vast forests of the North as its breeding grounds, travelling hundreds of miles in search of food, it is here to-day, and elsewhere to-morrow, and no ordinary destruction can lessen them or be missed from the myriad that are yearly produced” [1]. Other states, particularly Wisconsin, wrote laws to protect the species: “It shall be unlawful for any person or persons to use any gun or guns or firearms, or in any manner to main, kill, destroy, or disturb any wild pigeon or pigeons at or within three miles of the place or places where they are gathered for the purpose of brooding their young, known as pigeon nestings”. Laws along these lines were enacted in several states but no efforts were made to actually enforce them. Much of this was due to pushback by settlers to the laws. Farmers in particular protested any enforcement, worrying that allowing the pigeons to thrive would mean crop destruction [1]. To them, the pigeons were pests to get rid of, not preserve.

 

Gone

The last passenger pigeon, named Martha, was a resident of the Cincinnati Zoo up until her passing on September 1, 1914. She was named after First Lady Martha Washington and was housed with her companion named George. The pair never produced fertile eggs, the zoo’s captive breeding effort was too late to save the population [1]. The end of the species “removed more individual birds than did all the other 129 [previously recorded bird] extinctions put together”. They went extinct because of introduced species, chains of extinction, overexploitation, and habitat loss—all four of these were human-driven factors. Captive breeding, regulating hunting, and habitat protection could have saved them. However, these efforts were seldom made and not done early enough in the population’s decline [3]. The passenger pigeon was lost because of the genre Europeans created for it during the time it was still around.

The majority of non-indigenous Americans only appreciated the passenger pigeon and shifted their genre once they were no longer around. People now found a soft spot for the birds in their memories, “Alas, the pigeons and the frosty morning hunts and the delectable pigeon-pie are gone, no more return”. Artists incorporated these fond memories into their paintings, poems, and music. Monuments were erected around the United States inscribed with laments such as “this species became extinct through the avarice and thoughtlessness of Man” and “the conservationist’s voice was heard too late” [3]. People regretted the fact that future generations would not get to see the bird in the sky so they attempted to etch the passenger pigeon into everyone’s minds [6]. 

Making an effort to remember the passenger pigeon is important because the species’ story functions as a lesson and a guide for the future. However, in the past decade, the passenger pigeon is being forgotten. Many high school students are not taught about the population’s time on Earth and why they are now gone, as shown by a case study in Pennsylvania [7]. If people are no longer talking about it then the same mistake will be made again. At the same time, there are also organizations, like the Project Passenger Pigeon founded in 2014, working to tell the parable “through a documentary film, a new book, their website, social media, curricula, and a wide range of exhibits and programming for people of all ages” [8]. However, a small group of thoughtful individuals will not be enough to save the next species from human destruction if the story of the passenger pigeon does not make it into enough of the right hands.

 

Experiment

Over the period of one month during March 2019, I surveyed teenagers regarding their passenger pigeon knowledge. At the time of the survey, the teenagers were high school or college students in the United States. The overall purpose of my survey was to investigate if young people are talking about the passenger pigeon in contemporary society. Of the fifty-one responses, three (6%) subjects spoke about the passenger pigeon accurately. Furthermore, eight (16%) subjects believed to know the true story of the passenger pigeon, all of those eight falsely stated that the passenger pigeon was used to carry messages. Eight (16%) subjects even claimed to have seen a live passenger pigeon since 2000. 

My survey found that very few teenagers have heard the parable of the passenger pigeon’s extinction. This group of people has gone through a large amount of schooling in their lives so far without being taught about the passenger pigeon despite its intertwining with significant historical events. The convenient fact about passenger pigeons is that they feel familiar to people since most have seen today’s common pigeon, the rock dove. It is easy for the uninitiated to imagine what a passenger pigeon was like based off of what they know about rock doves. The parable of the passenger pigeons can be taught in any classroom—science, history, art, and more. 

The experiment shows that the genre is not being passed on. This is exactly why the way contemporary society talks about this species and its genre matters. Education that advocates for proper wildlife management and policy is the key to saving species from extinction. 

 

Conclusion

The passenger pigeon species went from the world’s largest bird population to complete extinction, due to mistreatment from European colonizers. My survey of high school teenagers shows that people are not learning from this parable. This species makes itself an ideal candidate because of the rapid severity of its decline. It is increasingly important that we care about our environment before it is too late to take action. The clock is ticking, the passenger pigeon told us so. If we can learn to mourn a bird we never met, we will not have the opportunity to mourn the birds we know.

 

Works Cited

  1. Schorger, A.W. The Passenger Pigeon: Its Natural History and Extinction. Wisconsin, University of Wisconsin Press, 1955.
  2. Dirk, Kerry. “Navigating Genres”. Writing Spaces: Readings on Writing, edited by Charles Lowe and Pavel Zemliansky, vol. 1, Parlor Press, 2010.
  3. Avery, Mark. A Message from Martha. London, Bloomsbury Publishing, 2014.
  4. Greenberg, Joel. A Feathered River Across the Sky: The Passenger Pigeon’s Flight to Extinction. New York, Bloomsbury USA, 2014.
  5. Wilson, Alexander. Wilson’s American Ornithology. Boston, Otis Broader and Company, 1853.
  6. Leopold, Aldo. A Sand County Almanac. New York, Oxford University Press, 1954.
  7. Soll, David. “Resurrecting the Story of the Passenger Pigeon in Pennsylvania.” Pennsylvania History: A Journal of Mid-Atlantic Studies, vol. 79, no. 4, 2012, pp. 507–519. JSTOR, www.jstor.org/stable/10.5325/pennhistory.79.4.0507.
  8. Project Passenger Pigeon. The Chicago Academy of Sciences and its Peggy Notebaert Nature Museum, 2012, passengerpigeon.org. Accessed 19 February 2019.

Post navigation

← Older posts