Similarity in spatial utilization distributions measured by the earth mover's distance

Kranstauber, Bart; Smolla, Marco; Safi, Kamran

doi:10.1111/2041-210x.12649

Cited by 54 publications

(60 citation statements)

References 18 publications

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“…In contrast, we use in this study a more general null model, comparing the observed migrations to alternative simulated migrations. Our result that the observed thermal distance across seasons (computed using the Earth Mover's Distance; Kranstauber et al 2016) appears to perform well when compared the alternative options (Fig. 2B) thus provide evidence for tracking of temperature conditions via migration.…”

Section: Discussionmentioning

confidence: 57%

“…For each species, we then measured the inter-seasonal thermal distance by computing the Earth Mover's Distance (EMD) between the breeding and non-breeding probability distribution landscapes in the general temperature space. The EMD is an algorithm that was originally developed for image comparison (Rubner et al 2000) and has been recently adapted to quantify similarity between spatial utilization distributions (Kranstauber et al 2016). It provides a distance measure by calculating the effort it takes to shape one probability distribution landscape into another.…”

Section: Measuring Inter-seasonal Thermal Distancementioning

confidence: 99%

“…The EMD has important advantages over other standard niche distance measures, such as overlap (as in Boucher-Lalonde et al 2014) or distance between centroids. Indeed, it can provide a reliable distance measure between a pair of two-dimensional probability distributions even if the overlap between them is 0 or near 0, is less sensitive than other measures to variation in area of the two distributions (Kranstauber et al 2016), and takes into account variations in density within each probability distribution.…”

Section: Measuring Inter-seasonal Thermal Distancementioning

confidence: 99%

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Where the wild birds go: explaining the differences in migratory destinations across terrestrial bird species

2018

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Given their large movement capacities, migratory birds have in principle a wide range of possible geographical locations for their breeding and non‐breeding destinations, yet each species migrates between consistent breeding and non‐breeding ranges. In this study, we use a macroecological approach to search for the general factors explaining the location of the seasonal ranges of migratory bird species across the globe. We develop a null model to test the hypotheses that access to resources, geographical distance, tracking of temperature, and habitat conditions (separately as well as considered together) have a major influence in the location of species’ migratory destinations, once each species’ geographical constraints are taken into account. Our results provide evidence for a trade‐off between costs associated with distance travelled and gains in terms of better access to resources. We also provide strong support to the hypotheses that all factors tested, with the exception of habitat, have a strong and additive effect on the global geography of bird migration. Indeed, our results indicate that species’ contemporary migratory destinations (i.e. the combination of their breeding and non‐breeding ranges) are such that they allow species to track a temperature regime throughout the year, to escape local competition and reach areas with better access to resources, and to minimise the spatial distance travelled, within the limitations imposed by the geographical location of each species. Our study thus sheds light on the mechanisms underpinning bird migration and provides a strong basis for predicting how migratory species will respond to future change.

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Section: Discussionmentioning

confidence: 57%

Section: Measuring Inter-seasonal Thermal Distancementioning

confidence: 99%

Section: Measuring Inter-seasonal Thermal Distancementioning

confidence: 99%

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Where the wild birds go: explaining the differences in migratory destinations across terrestrial bird species

2018

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“…A more recently developed method, earth mover's distance, builds on previous methods to consider the spatial proximity of utilization distributions, rather than the amount of overlap exclusively (Kranstauber et al. ).…”

Section: Moving Forward: Developing Quantitative Assessment Of Large mentioning

confidence: 99%

Toward a community ecology of landscapes: predicting multiple predator–prey interactions across geographic space

Schmitz

Miller

Trainor

et al. 2017

Ecology

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Community ecology was traditionally an integrative science devoted to studying interactions between species and their abiotic environments in order to predict species' geographic distributions and abundances. Yet for philosophical and methodological reasons, it has become divided into two enterprises: one devoted to local experimentation on species interactions to predict community dynamics; the other devoted to statistical analyses of abiotic and biotic information to describe geographic distribution. Our goal here is to instigate thinking about ways to reconnect the two enterprises and thereby return to a tradition to do integrative science. We focus specifically on the community ecology of predators and prey, which is ripe for integration. This is because there is active, simultaneous interest in experimentally resolving the nature and strength of predator-prey interactions as well as explaining patterns across landscapes and seascapes. We begin by describing a conceptual theory rooted in classical analyses of non-spatial food web modules used to predict species interactions. We show how such modules can be extended to consideration of spatial context using the concept of habitat domain. Habitat domain describes the spatial extent of habitat space that predators and prey use while foraging, which differs from home range, the spatial extent used by an animal to meet all of its daily needs. This conceptual theory can be used to predict how different spatial relations of predators and prey could lead to different emergent multiple predator-prey interactions such as whether predator consumptive or non-consumptive effects should dominate, and whether intraguild predation, predator interference or predator complementarity are expected. We then review the literature on studies of large predator-prey interactions that make conclusions about the nature of multiple predator-prey interactions. This analysis reveals that while many studies provide sufficient information about predator or prey spatial locations, and thus meet necessary conditions of the habitat domain conceptual theory for drawing conclusions about the nature of the predator-prey interactions, several studies do not. We therefore elaborate how modern technology and statistical approaches for animal movement analysis could be used to test the conceptual theory, using experimental or quasi-experimental analyses at landscape scales.

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“…) and patterns of animal space use and movement (Shamoun‐Baranes et al . ; Kranstauber, Smolla & Safi ). Closer to the current work, they have also been used to compare trait distributions between populations (Gregorius, Gillet & Ziehe ) and to measure the convergence of trait distributions under selection (Rudnicki & Zwoleński ; Zwoleński ).…”

Section: Glossary Of Termsmentioning

confidence: 99%

A unified measure of linear and nonlinear selection on quantitative traits

Henshaw

Zemel

2016

Methods Ecol Evol

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Summary Lande and Arnold's approach to quantifying natural selection has become a standard tool in evolutionary biology due to its simplicity and generality. It treats linear and nonlinear selection in two separate frameworks, generating coefficients of selection (e.g. linear and quadratic selection gradients) that are not directly comparable. Due to this somewhat artificial division, the Lande–Arnold approach lacks an integrated measure of the strength of selection that applies across qualitatively different selection regimes (e.g. directional, stabilizing or disruptive selection). We define a unified measure of selection, the distributional selection differential (DSD), which includes both linear and nonlinear selection. The DSD quantifies total selection on a trait, regardless of the underlying selection regime. The DSD can be partitioned into a directional component, representing selection on the trait mean, and a non‐directional component, representing selection on the shape of the trait distribution (e.g. variance, skew or the number of modes). When multiple traits are measured, the DSD can also be separated into direct and correlated effects, analogously to linear selection gradients. As with linear selection differentials, the DSD on a standardized trait is limited in magnitude by the opportunity for selection. The DSD is a general‐purpose measure of the total strength of selection. It is particularly valuable where traditional analyses provide limited insight, such as in comparative studies where the shape of selection is variable. Partitioning the DSD into directional and non‐directional selection allows biologists to assess whether selection acts consistently in one direction, or in opposing directions over different parts of the trait range.

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Similarity in spatial utilization distributions measured by the earth mover's distance

Cited by 54 publications

References 18 publications

Where the wild birds go: explaining the differences in migratory destinations across terrestrial bird species

Where the wild birds go: explaining the differences in migratory destinations across terrestrial bird species

Toward a community ecology of landscapes: predicting multiple predator–prey interactions across geographic space

A unified measure of linear and nonlinear selection on quantitative traits

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