Relative Selection Strength: Quantifying EffectSize in Habitat- and Step-Selection Inference

Habitat-selection analysis lacks an appropriate measure of the ecological significance of the statistical estimates-a practical interpretation of the magnitude of the selection coefficients. There is a need for a standard approach that allows relating the strength of selection to a change in habitat conditions across space, a quantification of the estimated effect size that can be compared both within and across studies. We offer a solution, based on the epidemiological risk ratio, which we term the relative selection strength (RSS). For a used-available design with an exponential selection function, the RSS provides an appropriate interpretation of the magnitude of the estimated selection coefficients, conditional on all other covariates being fixed. This is similar to the interpretation of the regression coefficients in any multivariable regression analysis. Although technically correct, the conditional interpretation may be inappropriate when attempting to predict habitat use across a given landscape. Hence, we also provide a simple graphical tool that communicates both the conditional and average effect of the change in one covariate. The average-effect plot answers the question: What is the average change in the space use probability as we change the covariate of interest, while averaging over possible values of other covariates? We illustrate an application of the average-effect plot for the average effect of distance to road on space use for elk (Cervus elaphus) during the hunting season. We provide a list of potentially useful RSS expressions and discuss the utility of the RSS in the context of common ecological applications

Resource Selection and Its Implications for Wide-Ranging Mammals of the Brazilian Cerrado

Conserving animals beyond protected areas is critical because even the largest reserves may be too small to maintain viable populations for many wide-ranging species. Identification of landscape features that will promote persistence of a diverse array of species is a high priority, particularly, for protected areas that reside in regions of otherwise extensive habitat loss. This is the case for Emas National Park, a small but important protected area located in the Brazilian Cerrado, the world's most biologically diverse savanna. Emas Park is a large-mammal global conservation priority area but is too small to protect wide-ranging mammals for the long-term and conserving these populations will depend on the landscape surrounding the park. We employed novel, noninvasive methods to determine the relative importance of resources found within the park, as well as identify landscape features that promote persistence of wide-ranging mammals outside reserve borders. We used scat detection dogs to survey for five large mammals of conservation concern: giant armadillo (Priodontes maximus), giant anteater (Myrmecophaga tridactyla), maned wolf (Chrysocyon brachyurus), jaguar (Panthera onca), and puma (Puma concolor). We estimated resource selection probability functions for each species from 1,572 scat locations and 434 giant armadillo burrow locations. Results indicate that giant armadillos and jaguars are highly selective of natural habitats, which makes both species sensitive to landscape change from agricultural development. Due to the high amount of such development outside of the Emas Park boundary, the park provides rare resource conditions that are particularly important for these two species. We also reveal that both woodland and forest vegetation remnants enable use of the agricultural landscape as a whole for maned wolves, pumas, and giant anteaters. We identify those features and their landscape compositions that should be prioritized for conservation, arguing that a multi-faceted approach is required to protect these species

Appendix A. An alternative derivation of Johnson et al. (2006) method and proof of nonidentifiability of the intercept parameter for categorical covariates.

An alternative derivation of Johnson et al. (2006) method and proof of nonidentifiability of the intercept parameter for categorical covariates

Supplement 1. Source code in R for estimating logistic resource selection probability function and the data set used in the paper.

<h2>File List</h2><blockquote> <p><a href="LogisticRSPFEstimation.txt">LogisticRSPFEstimation.txt</a> -- R source code<br> <a href="goatdata.txt">goatdata.txt</a> -- used points<br> <a href="goatavailable.txt">goatavailable.txt</a> -- random points from the available distribution</p> <p> </p> </blockquote><h2>Description</h2><blockquote> <p> The ‘LogisticRSPFEstimation.txt’ is an R program that estimates the regression coefficients for the Logistic resource selection probability function given the used and available data points. The ‘goatdata.txt’ provides the covariates associated with the used points for the goat data analysis described in the paper. The ‘goatavailable.txt’ provides the covariates associated with the random points obtained from the available distribution. These two files are input for the R program.</p> </blockquote

Appendix A. Parameter values and results for snowshoe hare resource selection probability function (RSPF) and a linear model relating the intensity of snowshoe hare tracks with snowshoe hare RSPF.

Parameter values and results for snowshoe hare resource selection probability function (RSPF) and a linear model relating the intensity of snowshoe hare tracks with snowshoe hare RSPF

Data from: Quantifying past and present connectivity illuminates a rapidly changing landscape for the African elephant

There is widespread concern about impacts of land-use change on connectivity among animal and plant populations, but those impacts are difficult to quantify. Moreover, lack of knowledge regarding ecosystems before fragmentation may obscure appropriate conservation targets. We use occurrence and population genetic data to contrast connectivity for a long-lived mega-herbivore over historical and contemporary time frames. We test whether (i) historical gene flow is predicted by persistent landscape features rather than human settlement, (ii) contemporary connectivity is most affected by human settlement and (iii) recent gene flow estimates show the effects of both factors. We used 16 microsatellite loci to estimate historical and recent gene flow among African elephant (Loxodonta africana) populations in seven protected areas in Tanzania, East Africa. We used historical gene flow (FST and G'ST) to test and optimize models of historical landscape resistance to movement. We inferred contemporary landscape resistance from elephant resource selection, assessed via walking surveys across ~15 400 km2 of protected and unprotected lands. We used assignment-based recent gene flow estimates to optimize and test the contemporary resistance model, and to test a combined historical and contemporary model. We detected striking changes in connectivity. Historical connectivity among elephant populations was strongly influenced by slope but not human settlement, whereas contemporary connectivity was influenced most by human settlement. Recent gene flow was strongly influenced by slope but was also correlated with contemporary resistance. Inferences across multiple timescales can better inform conservation efforts on large and complex landscapes, while mitigating the fundamental problem of shifting baselines in conservation

Data from: Estimating the intensity of use by interacting predators and prey using camera traps

Understanding how organisms distribute themselves in response to interacting species, ecosystems, climate, human development and time is fundamental to ecological study and practice. A measure to quantify the relationship among organisms and their environments is intensity of use: the rate of use of a specific resource in a defined unit of time. Estimating the intensity of use differs from estimating probabilities of occupancy or selection, which can remain constant even when the intensity of use varies. We describe a method to evaluate the intensity of use across conditions that vary in both space and time. We demonstrate its application on a large mammal community where linear developments and human activity are conjectured to influence the interactions between white‐tailed deer (Odocoileus virginianus) and wolves (Canis lupus) with possible consequences on threatened woodland caribou (Rangifer tarandus caribou). We collect and quantify intensity of use data for multiple, interacting species with the goal of assessing management efficacy, including a habitat restoration strategy for linear developments. We test whether blocking linear developments by spreading logs across a 200‐m interval can be applied as an immediate mitigation to reduce the intensities of use by humans, predator and prey species in a boreal caribou range. We deployed camera traps on linear developments with and without restoration treatments in a landscape exposed to both timber and oil development. We collected a three‐year dataset and employed spatial recurrent event models to analyse intensity of use by an interacting human and large mammal community across a range of environmental and climatic conditions. Spatial recurrent event models revealed that intensity of use by humans influenced the intensity of use by all five large mammal species evaluated, and the intensities of use by wolves and deer were inextricably linked in space and time. Conditions that resist travel on linear developments had a strong negative effect on the intensity of human and large mammal use. Mitigation strategies that resist, or redirect, animal travel on linear developments can reduce the effects of resource development on interacting human and predator–prey interactions. Our approach is easily applied to other continuous time point‐based survey methodologies and shows that measuring the intensity of use within animal communities can help scientists monitor, mitigate and understand ecological states and processes

Papilio sp.

Slope (in degrees), untransformed (i.e., slope^1); Albers Equal Area projection (Africa