Multi-scale habitat selection modeling: introduction to the special issue

McGarigal, Kevin; Zeller, Katherine A.; Cushman, Samuel A.

doi:10.1007/s10980-016-0388-4

Cited by 26 publications

(12 citation statements)

References 12 publications

(16 reference statements)

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“…Assessing the functional grain of analysis, or the grain at which the organism is responding to the landscape, for connectivity studies has been useful to create resistance maps of habitat preferences (25). Raster spatial grain is a problem in the multi-scale paradigm that is often not considered within multi-level, multi-scale studies for resource selection (26). Although similar in technique to expanding distance buffers and averaging the pixels around a point or line.…”

Section: Raster Grainmentioning

confidence: 99%

Multi-Level, Multi-Scale Modeling and Predictive Mapping for Jaguars in the Brazilian Pantanal.

Bohnett

Hoctor

Hulse

et al. 2020

Preprint

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Background Machine learning (ML) methods and remote sensing data were used to build multi-level multi-scale resource selection models and predictive maps onto the extended landscape for jaguars (Panthera onca) in the Brazilian Pantanal. Objectives were to compare multiple predictive modeling and exploratory modeling approaches. Included in the analysis, multi-scale raster grains (30m, 90m, 180m, 360m, 720m, 1440m), GPS collaring temporal levels (point, path, and step) and model data structural levels (group, individual, case-control).Methods Multi-scale multi-level data subsets were fit with explanatory and predictive statistical methods. Conditional logistic regression, generalized additive modeling (GAM), and classification regression trees, such as random forests (RF) and gradient boosted regression tree (GBM) were compared for their utility to the study. Model evaluation, using training and testing data in a k-fold cross-validation approach, determined the AUC, Kappa, and TSS for model evaluation and comparison. · Results Results indicated that the multi-level, multi-scale techniques improved model outputs. Overall, larger level models and those that used multi-scale raster grains showed the best model evaluation. The highest-ranked model was the multi-scale path selection function GBM and was one of the broadest levels of data. ·Conclusions Results indicated that multi-level, multi-scale models produced mixed results of applicability across models and levels. The identification of the appropriate temporal scale and statistical model needs careful consideration in predictive mapping efforts.

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Section: Raster Grainmentioning

confidence: 99%

Multi-Level, Multi-Scale Modeling and Predictive Mapping for Jaguars in the Brazilian Pantanal.

Bohnett

Hoctor

Hulse

et al. 2020

Preprint

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“…Ecologists have generally of interest. the spatial context, or spatial contingency; Fortin et al 2012) quantifies the average environmental value within buffers of various radii (Holland et al 2004, Weaver et al 2012, Zuckerberg et al 2012, McGarigal et al 2016, and then repeats a statistical analysis using the environmental covariate at each spatial scale (Fig. The most commonly used approach for determining spatial scale of response to environmental gradients (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…This multi-scale analysis approach has been successful in elucidating species and community responses (McGarigal et al 2016). By considering whole landscapes, it has helped quantify the benefits of small forest fragments to biological communities, and related ecosystem services (Karp et al 2013, Mendenhall et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Features of the landscape beyond the local scale often affect the processes that give rise to patterns of community composition (Wiens 1989, Levin 1992, Kneitel and Chase 2004, Dray et al 2012, Fortin et al 2012, McGarigal et al 2016. Ecologists have thus sought to quantify what landscape features explain the presence and abundance of species, and at what spatial scales.…”

Section: Introductionmentioning

confidence: 99%

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Integrating over uncertainty in spatial scale of response within multispecies occupancy models yields more accurate assessments of community composition

2019

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Species abundance and community composition are affected not only by the local environment, but also by broader landscape and regional context. Yet, determining the spatial scales at which landscapes affect species remains a persistent challenge, hindering our ability to understand how environmental gradients shape communities. This problem is amplified by rare species and imperfect species detection. Here, we present a Bayesian framework that allows uncertainty surrounding the 'true' spatial scale of species' responses (i.e. changes in presence/absence) to be integrated directly into a community hierarchical model. This scale-selecting multispecies occupancy model (ssMSOM) estimates the scale of response, and shows high accuracy and correct levels of uncertainty in parameter estimates across a broad range of simulation conditions. An ssMSOM can be run in a matter of minutes, as opposed to the many hours required to run normal multispecies occupancy models at all queried spatial scales, and then conduct model selection -a problem that up to now has prohibited scale of response from being rigorously evaluated in an occupancy framework. Alternatives to the ssMSOM, such as GLM-based approaches frequently fail to detect the correct spatial scale and magnitude of response, and are often falsely confident by favoring the incorrect parameter estimates, especially as species' detection probabilities deviate from perfect. We further show how trait information can be leveraged to understand how individual species' scales of response vary within communities. Integrating spatial scale selection directly into hierarchical community models provides a means of formally testing hypotheses regarding spatial scales of response, and more accurately determining the environmental drivers that shape communities.

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“…Correlating properties of soil and vegetation were hypothesized to vary in space within the constraints imposed by combination of landforms in some neighborhood. As McGarigal et al (2016) wrote, determining the right neighborhood size is a major focus of current multiscale habitat selection modeling. We argue that the statistically significant relations within a set of correlating soil-vegetation properties as well as the spatial patterns of the higher-order geosystems may indicate the present-day or former-time processes that govern spatial heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Organization of Landscape Structure in the Middle Taiga of European Russia

Хорошев¹

2019

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Dominant landscape-ecological models either focus on the hierarchical organization of a single phenomenon or describe relations at a single hierarchical level. We proposed the tool MALS (Multiscale Analysis of Landscape Structure) to reveal multiple independent hierarchies based on the interactions between properties of relief, soils and vegetation and tested it on the example of the middle-taiga landscape in European Russia. Morphological properties of soils and abundance of plant species were measured in operational territorial units. Multidimensional scaling was used to reveal ecological drivers. Combinations of landforms from DEM were used to describe spatial heterogeneity in the higher-order geosystems. Response surface regression was applied to relate soils and vegetation to each other and to relief of several hypothetic higher-order geosystems. Spatial extent of a higher-order geosystem was determined from the series of equations. Then we compared contributions of external (inter-level) and internal (intra-level) interactions to spatial variability of soils and vegetation. Herbs, low shrubs, and morphologic soil properties turned out to be controlled mainly by the geosystems with the linear size 1200 m, while trees, shrubs, and sediments – by the geosystems with size 2000 m. From 2 to 5 levels of the higher-order geosystems should be considered in order to obtain the proper explanation of spatial heterogeneity.

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Multi-scale habitat selection modeling: introduction to the special issue

Cited by 26 publications

References 12 publications

Multi-Level, Multi-Scale Modeling and Predictive Mapping for Jaguars in the Brazilian Pantanal.

Multi-Level, Multi-Scale Modeling and Predictive Mapping for Jaguars in the Brazilian Pantanal.

Integrating over uncertainty in spatial scale of response within multispecies occupancy models yields more accurate assessments of community composition

Multiscale Organization of Landscape Structure in the Middle Taiga of European Russia

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