Species distribution models for a migratory bird based on citizen science and satellite tracking data

Coxen, Christopher L.; Frey, Jennifer K.; Carleton, Scott A.; Collins, Daniel P.

doi:10.1016/j.gecco.2017.08.001

Cited by 86 publications

(77 citation statements)

References 72 publications

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“…To limit bias due to spatial clustering, we used a block‐partitioned variation on k‐fold cross‐validation using the package “ ENMEVAL ” in program R to obtain AUC values (Muscarella et al, ). We used TSS values from Landis and Koch () to evaluate performance as almost perfect (>0.80), substantial (0.61–0.80), moderate (0.41–0.60), fair (0.21–0.40), slight (0–0.20) and poor (<0.00) (Allouche, Tsoar, & Kadmon, ; Coxen, Frey, Carleton, & Collins, ).…”

Section: Methodsmentioning

confidence: 99%

Species distribution modelling supports “nectar corridor” hypothesis for migratory nectarivorous bats and conservation of tropical dry forest

Burke

Frey

Ganguli

et al. 2019

Diversity and Distributions

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Aim:The Mexican long-tongued bat (Choeronycteris mexicana), Mexican long-nosed bat (Leptonycteris nivalis) and lesser long-nosed bat (Leptonycteris yerbabuenae) (Phyllostomidae: Glossophaginae) undertake long-distance migrations from southcentral Mexico to the south-western United States. It is proposed that these bats migrate along a nectar corridor of columnar cacti and Agave species, but this has not been tested with independent data and the spatiotemporal nature of this relationship is poorly understood. Our goal was to test this nectar corridor hypothesis and determine the relative importance of food plant and abiotic variables to the distribution and seasonal movements of these migratory nectarivores.Location: Mexico and the south-western United States. Methods:We generated species distribution models (SDMs) of documented food plants for these bats. We then created SDMs for each bat following a model selection approach, using food plant and abiotic predictor variables. We modelled migration pathways for C. mexicana and L. yerbabuenae using circuit theory and seasonal SDMs based on seasonally available food plants. Main conclusions: Food plants were more important than climatic and topographic variables in shaping the distribution of these bats. The most important predictors of distribution were Agave, columnar cacti and species richness of food plants. Species richness of food plants was the most consistently important variable, but the components of this diversity varied by bat species: Choeronycteris mexicana was influenced by Agave and cacti; Leptonycteris nivalis was influenced solely by Agave; Leptonycteris yerbabuenae was influenced more generally by cacti, Agave and C3 plants. Migration models for C. mexicana and L. yerbabuenae provided independent support for the nectar corridor hypothesis and indicate shifts in relative importance of specific food plants throughout the year. These results suggest that conservation of these bats should focus more broadly on management for species richness of food plants, especially in tropical dry forests.

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Section: Methodsmentioning

confidence: 99%

Species distribution modelling supports “nectar corridor” hypothesis for migratory nectarivorous bats and conservation of tropical dry forest

Burke

Frey

Ganguli

et al. 2019

Diversity and Distributions

Self Cite

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show abstract

“…We used MaxEnt, a statistical learning technique that can be used to model environmental suitability across a geographic extent (Elith et al, ). Widely used in species distribution modeling (for recent examples, see Berthon, Esperon‐Rodriguez, Beaumont, Carnegie, & Leishman, ; Clemente et al, ; Coxen, Frey, Carleton, & Collins, ; Zhang, Yao, Meng, & Tao, ), we applied this approach to examine the spatial distribution of the risk of illegal activity across the study area because this approach produces robust predictions and is one of the most user‐friendly techniques with regards to both model construction and interpretation (Merow, Smith, & Silander, ). The illegal activity data is “presence only” data, as there are no direct observations of “absence.” This is a common situation, particularly in the context of species distribution modeling (Elith & Leathwick, ).…”

Section: Methodsmentioning

confidence: 99%

Predicting the risk of illegal activity and evaluating law enforcement interventions in the western Serengeti

Snyder

Mneney²,

Wittemyer

2019

Conservat Sci and Prac

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Illegal activity within protected areas is a primary driver of species decline and threatens conservation efforts. In the western corridor of the Serengeti Ecosystem of northern Tanzania, the Grumeti and Ikorongo Game Reserves and Ikona Wildlife Management Area provide an important buffer between permanent settlements and Serengeti National Park, while simultaneously maintaining critical wildlife habitat. Understanding the spatial distribution and environmental drivers of illegal activity is critical to optimize biodiversity protection efforts in this important ecosystem. We examined a rare dataset containing detailed records of reserve game scout patrol effort and occurrences of illegal activity between 2013 and 2016. We used presence only data to construct predictive models of five categories of illegal activity. We derived spatial predictions of the likelihood of different activities and identified the environmental variables predictive of risk for each activity. The highest risk areas were located along reserve edges, further from roads and scout camps, suggesting avoidance of enforcement presence. Activities associated with wildlife offtake were the most widely distributed in the study area and extended into the national park. Permanent scout camps were more effective deterrents of all illegal activities than observation posts, but their limited spatial influence demonstrated additional enforcement strategies are required. K E Y W O R D SAfrica, bushmeat, predictive modeling, protected areas, risk assessment, snare

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“…Using presence-only data is now commonplace in biogeography, and its use should be encouraged when robust datasets can be generated from citizen-science data (Beck et al 2013;Fournier et al 2017a,b). For example, a comparison of model predictive performance from both satellite tracking and eBird data-derived SDMs for the Band-tailed Pigeon Patagioenas fasciata, demonstrated that an SDM using presence-only occurrence data was just as effective at predicting species distributions as one using satellite tracking data (Coxen et al 2017). However, we recognise there are important methodological issues regarding spatial bias in presence-only occurrences from digital databases (Beck et al 2014).…”

Section: Occurrence Data and Model Limitationsmentioning

confidence: 99%

Climatic niche of the Saker FalconFalco cherrug: predicted new areas to direct population surveys in Central Asia

Sutton

Puschendorf

2018

Ibis

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Accurate species distribution data across remote and extensive geographical areas are difficult to obtain. Here, we use bioclimatic envelope models to determine climatic constraints on the distribution of the migratory Saker Falcon Falco cherrug to identify areas in data‐deficient regions that may contain unidentified populations. Sakers live at low densities across large ranges in remote regions, making distribution status difficult to assess. Using presence‐background data and eight bioclimatic variables within a species distribution modelling framework, we applied MaxEnt to construct models for both breeding and wintering ranges. Occurrence data were spatially filtered and climatic variables tested for multicollinearity before selecting best fit models using the Akaike information criterion by tuning MaxEnt parameters. Model predictive performance tested using the continuous Boyce index (B) was high for both breeding (BTEST = 0.921) and wintering models (BTEST = 0.735), with low omission rates and minimal overfitting. The Saker climatic niche was defined by precipitation in the warmest quarter in the breeding range model, and mean temperature in the wettest quarter in the wintering range model. Our models accurately predicted areas of highest climate suitability and defined the climatic constraints on a wide‐ranging rare species, suggesting that climate is a key determinant of Saker distribution across macro‐scales. We recommend targeted population surveys for the Saker based on model predictions to areas of highest climatic suitability in key regions with distribution knowledge gaps, in particular the Qinghai‐Tibet plateau in western China. Further applications of our models could identify protected areas and reintroduction sites, inform development conflicts, and assess the impact of climate change on distributions.

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Species distribution models for a migratory bird based on citizen science and satellite tracking data

Cited by 86 publications

References 72 publications

Species distribution modelling supports “nectar corridor” hypothesis for migratory nectarivorous bats and conservation of tropical dry forest

Species distribution modelling supports “nectar corridor” hypothesis for migratory nectarivorous bats and conservation of tropical dry forest

Predicting the risk of illegal activity and evaluating law enforcement interventions in the western Serengeti

Climatic niche of the Saker FalconFalco cherrug: predicted new areas to direct population surveys in Central Asia

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