Insights from Ecological Theory on Temporal Dynamics and Species Distribution Modeling

Fletcher, Robert J.; Young, Jock S.; Hutto, Richard L.; Noson, Anna C.; Rota, Christopher T.

doi:10.1007/978-1-4419-7390-0_6

Cited by 7 publications

(4 citation statements)

References 46 publications

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“…fashion (Faaborg et al, 2010a,b) across regions containing different land-use histories and management mandates (La Sorte et al, 2015). Quantifying species' land cover associations within a temporally dynamic framework allows for a more comprehensive evaluation of how the environment influences species' distributions and future conservation planning (Franklin, 2010;Fletcher et al, 2011;Runge et al, 2015). For example, the impact of habitat loss on bird populations often focuses on the breeding period because this represents a critical time for attracting mates, selecting nest sites, and producing and rearing of young (Debinski & Holt, 2000;Fahrig, 2003).…”

Section: Discussionmentioning

confidence: 99%

Novel seasonal land cover associations for eastern North American forest birds identified through dynamic species distribution modelling

Zuckerberg

Fink

Sorte

et al. 2016

Diversity and Distributions

116

114

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Aim Plasticity in migratory and foraging behaviour allows species to exploit dynamic and novel habitats. This is especially important during seasonal transitions as species track shifting environmental resources and potentially associate with a diversity of habitats. Although land cover associations are thought to vary across seasons for many species, the prevalence of these dynamic relationships across species' distributions are unknown. Our goal was to quantify the extent to which flexibility in seasonal land cover associations exists among forest breeding birds with differing migratory and foraging strategies.Location Eastern United States.Methods We used data on bird occurrence from eBird in conjunction with dynamic species distribution modelling to quantify seasonal plasticity in species land cover associations for 43 forest breeding bird species. We employed a multi-scaled approach relying on adaptive regression models to quantify spatiotemporally varying associations between species' occurrences and land cover diversity and composition. We estimated how these associations varied from spring to autumn and across multiple regions.Results Species demonstrated seasonal shifts in land cover associations and, despite being forest dependent species, were more likely to occur in humanmodified landscapes during seasonal transitions. From spring to autumn, Neotropical migrants were more likely to occur in landscapes of lower land cover diversity, but showed the highest seasonal plasticity in land cover associations. Residents and temperate migrants occurred in landscapes with a higher diversity of land cover, but were less variable in their seasonal land cover associations. Following summer, migratory and insectivorous birds took advantage of a wider array of land cover ranging from open to developed landscapes.Main conclusions Species move across landscapes in a seasonally dynamic fashion, and yet concepts of the ecological niche and species-environmental relationships are often considered static. Dynamic species distribution modelling can uncover seasonally complex species-environment relationships, and identify novel aspects of habitat associations critical for supporting full life cycle research and conservation efforts.

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Section: Discussionmentioning

confidence: 99%

Novel seasonal land cover associations for eastern North American forest birds identified through dynamic species distribution modelling

Zuckerberg

Fink

Sorte

et al. 2016

Diversity and Distributions

116

114

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“…We again used PCA to reduce the number of canopy cover variables to two. In both cases, one principal component reflected a linear gradient of canopy cover or DBH, whereas the other component reflected a non-linear gradient (high factor loadings on intermediate categories; see also Fletcher et al 2010). The original R1VMP Life-Form layer describes the relative canopy cover of several vegetative communities in each cell.…”

Section: Habitat and Detection Covariatesmentioning

confidence: 99%

Does accounting for imperfect detection improve species distribution models?

Rota

Fletcher²,

Evans³

et al. 2011

Ecography

Self Cite

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Models of species distributions are increasingly being used to address a variety of problems in conservation biology. In many applications, perfect or constant detectability of species, given presence, is assumed. While this problem has been acknowledged and addressed through the development of occupancy models, we still know little regarding whether addressing the potential for imperfect detection improves the predictive performance of species distribution models in nature. Here, we contrast logistic regression models of species occurrence that do not correct for detectability to hierarchical occupancy models that explicitly estimate and adjust for detectability, and maximum entropy models that attempt to circumvent the detectability problem by using data from known presence locations only. We use a large‐scale, long‐term monitoring database across western Montana and northern Idaho to contrast these models for nine landbird species that cover a broad spectrum in detectability. Overall, occupancy models were similar to or better than other approaches in terms of predictive accuracy, as measured by the Area Under the ROC Curve (AUC) and Kappa, with maximum entropy tending to provide the lowest predictive accuracy. Models varied in the types of errors associated with predictions, such that some model approaches may be preferred over others in certain situations. As expected, predictive performance varied across a gradient in species detectability, with logistic regression providing lower relative performance for less detectable species and Maxent providing lower performance for highly detectable species. We conclude by discussing the advantages and limitations to each approach for developing large‐scale species distribution models.

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“…Clearly, suitable habitat (either formatted as continuous potential maps (raster) or polygons or polylines of similar suitability) may play a key role in movement patterns of species as it may attract migrants while at the same time act as a source of outmigration if the habitat becomes densely populated [44]. While Fattebert et al [45] suggest that habitat suitability may be an appropriate proxy for movement preference during dispersal for some species, there are many pitfalls to using proxies of movement that do not rely on actually measured movement [46].…”

Section: Data Space and Time Scale For Movementmentioning

confidence: 99%

Contribution of Connectivity Assessments to Green Infrastructure (GI)

Bolliger

Silbernagel

2020

IJGI

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A major goal of green infrastructure (GI) is to provide functional networks of habitats and ecosystems to maintain biodiversity long-term, while at the same time optimizing landscape and ecosystem functions and services to meet human needs. Traditionally, connectivity studies are informed by movement ecology with species-specific attributes of the type and timing of movement (e.g., dispersal, foraging, mating) and movement distances, while spatial environmental data help delineate movement pathways across landscapes. To date, a range of methods and approaches are available that (a) are relevant across any organism and movement type independent of time and space scales, (b) are ready-to-use as standalone freeware or custom GIS implementation, and (c) produce appealing visual outputs that facilitate communication with land managers. However, to enhance the robustness of connectivity assessments and ensure that current trends in connectivity modeling contribute to GI with their full potential, common denominators on which to ground planning and design strategies are required. Likewise, comparable, repeatable connectivity assessments will be needed to put results of these scientific tools into practice for multi-functional GI plans and implementation. In this paper, we discuss use and limitations of state-of-the-art connectivity methods in contributing to GI implementation.

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Insights from Ecological Theory on Temporal Dynamics and Species Distribution Modeling

Cited by 7 publications

References 46 publications

Novel seasonal land cover associations for eastern North American forest birds identified through dynamic species distribution modelling

Novel seasonal land cover associations for eastern North American forest birds identified through dynamic species distribution modelling

Does accounting for imperfect detection improve species distribution models?

Contribution of Connectivity Assessments to Green Infrastructure (GI)

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