Evaluating the Utility of Species Distribution Models in Informing Climate Change-Resilient Grassland Restoration Strategy

Lyon, Nicholas J.; Debinski, Diane M.; Rangwala, Imtiaz

doi:10.3389/fevo.2019.00033

Cited by 17 publications

(11 citation statements)

References 25 publications

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“…Additionally, when CTmin evolution was constrained in the PVE model, there remained a ~6-fold increase (RCP 4.5: 1,227,002 km 2 ; RCP 8.5: 1,334,314 km 2 ) in the geographic range compared to current day. These differences in the predicted distributions underline the significance of incorporating empirical evolutionary data into SDMs 5,21,22 , and in particular the need to consider behaviour in addition to physiology when predicting range shifts 23 .…”

Section: Discussionmentioning

confidence: 97%

“…Additionally, when CTmin was allowed to evolve at a rate scaled by the percent of the trait variance that is explained by individual loci (reflective of a scenario where selection acted solely on those loci), there was a substantial increase in the area of the Normal Behaviour envelope. These changes to the projected species distributions under climate change underscore the importance of incorporating behavioural as well as physiological data into SDMs (Sunday, Bates, & Dulvy, 2012), as well as the key role that thermal trait evolution could play in range shifts (Buckley et al, 2010; Evans, Diamond, & Kelly, 2015; Lyon, Debinski, & Rangwala, 2019).…”

Section: Discussionmentioning

confidence: 99%

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Evolution of thermal physiology alters the projected range of threespine stickleback under climate change

Mogensen

Barry

et al. 2021

Preprint

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Species distribution models (SDMs) are widely adopted to predict range shifts but can be unreliable under climate change scenarios because they do not account for evolution. The thermal physiology of a species is a key determinant of range but the impact of thermal trait evolution on SDMs has not been addressed. We identified a genetic basis for physiological traits that evolve in response to temperature change in threespine stickleback. Using these data, we created geographic range projections under two climate change scenarios where trait data was either static (‘no evolution’ model), allowed to evolve in agreement with published evolutionary rates for the trait (‘evolution’ model), or allowed to evolve with the rate of evolution scaled in association with the variance that is explained by QTL (‘PVE’ model). Here, we show that incorporating these traits and their evolution into SDMs substantially altered the predicted ranges for a widespread panmictic marine population, with increases in area of over 7-fold. Evolution-informed SDMs should therefore improve the precision of forecasting range dynamics under climate change, thereby aiding in their application to management and the protection of biodiversity.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Evolution of thermal physiology alters the projected range of threespine stickleback under climate change

Mogensen

Barry

et al. 2021

Preprint

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“…This is particularly relevant for many rare and at-risk species targeted by SSAs, whose known habitat requirements are mostly limited to descriptive habitat associations. Further, SDM development could also function in a predictive context (future status) by incorporating climate and management scenarios, enhancing the conservation value of SDMs (Wiens et al 2009;Porfirio et al 2014;Lyon et al 2019).…”

Section: Case Studiesmentioning

confidence: 99%

Embracing Ensemble Species Distribution Models to Inform At-Risk Species Status Assessments

Ramirez‐Reyes

Nazeri

Street

et al. 2021

Journal of Fish and Wildlife Management

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Conservation planning depends on reliable information regarding the geographic distribution of species. However, our knowledge of species' distributions is often incomplete, especially when species are cryptic, difficult to survey, or rare. The use of species distribution models has increased in recent years and proven a valuable tool to evaluate habitat suitability for species. However, practitioners have yet to fully adopt the potential of species distribution models to inform conservation efforts for information-limited species. Here, we describe a species distribution modeling approach for at-risk species that could better inform U.S. Fish and Wildlife Service’s species status assessments and help facilitate conservation decisions. We applied four modeling techniques (generalized additive, maximum entropy, generalized boosted, and weighted ensemble) to occurrence data for four at-risk species proposed for listing under the U.S. Endangered Species Act (Papaipema eryngii, Macbridea caroliniana, Scutellaria ocmulgee and Balduina atropurpurea) in the Southeastern U.S. The use of ensemble models reduced uncertainty caused by differences among modeling techniques, with a consequent improvement of predictive accuracy of fitted models. Incorporating an ensemble modeling approach into species status assessments and similar frameworks is likely to benefit survey efforts, inform recovery activities, and provide more robust status assessments for at-risk species. We emphasize that co-producing species distribution models in close collaboration with species experts has the potential to provide better calibration data and model refinements, which could ultimately improve reliance and use of model outputs.

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“…This information is often of interest across a broad range of spatial and temporal scales, from high-resolution information that is more relevant for research on habitat selection (Matthiopoulos et al 2011) or needed to inform management objectives (Zipkin et al 2010) to larger-scale inferences that are useful to address broader questions (e.g. potential range-shifts with changing climatic conditions; Lyon et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Integrating citizen science data with expert surveys increases accuracy and spatial extent of species distribution models

Robinson

Ruiz‐Gutiérrez

Reynolds³

et al. 2019

Preprint

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AbstractInformation on species’ habitat associations and distributions, across a wide range of spatial and temporal scales, are a fundamental source of ecological knowledge. However, collecting biological information at relevant scales if often cost prohibitive, although it is essential for framing the broader context of more focused research and conservation efforts. Citizen-science data has been signaled as an increasingly important source of biological information needed to fill in data gaps needed to make more comprehensive and robust inferences on species distributions. However, there are perceived trade-offs of combining highly structured, scientific survey data with largely unstructured, citizen-science data. As a result, the focus of most methodological advances to combine these sources of information has been on treating these sources as independent. The degree to which each source of information is allowed to directly inform a common underlying process (e.g. species distribution) depends on the perceived quality of the data. In this paper, we explore these trade-offs by applying a simplified approach of filtering citizen-science data to resemble structured survey data, and analyze both sources of data under a common framework. To accomplish this, we explored ways of integrating high-resolution survey data on shorebirds in the northern Central Valley of California with observations in eBird for the entire region that were filtered to improve their quality. The integration of survey data with the filtered citizen-science data in eBird resulted in improved inference and predictive ability, and increased the extent and accuracy of inferences on shorebirds for the Central Valley. The structured surveys were found to improve the overall accuracy of ecological inference based only on citizen-science data, by increasing the representation of data collected from high quality habitats for shorebirds (e.g. rice fields). The practical approach we have shown for data integration can be also be used to improve the efficiency of designing biological surveys in the context of larger, citizen-science monitoring efforts, ultimately reducing the financial and time expenditures typically required of monitoring programs and focused research. The simple processing and filtering method we present can be used to integrate other types of data (e.g. camera traps) with more localized efforts (e.g. research projects), ultimately improving our ecological knowledge on the distribution and habitat associations of species of conservation concern worldwide.

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Evaluating the Utility of Species Distribution Models in Informing Climate Change-Resilient Grassland Restoration Strategy

Cited by 17 publications

References 25 publications

Evolution of thermal physiology alters the projected range of threespine stickleback under climate change

Evolution of thermal physiology alters the projected range of threespine stickleback under climate change

Embracing Ensemble Species Distribution Models to Inform At-Risk Species Status Assessments

Integrating citizen science data with expert surveys increases accuracy and spatial extent of species distribution models

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