How to quantify a distance‐dependent landscape effect on a biological response

Miguet, Paul; Fahrig, Lenore; Lavigne, Claire

doi:10.1111/2041-210x.12830

Cited by 48 publications

(76 citation statements)

References 20 publications

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“…The smoothing-parameter approach employed here has been relatively common in stream ecology research (Comeleo et al 1996, Peterson et al 2011), but remains rare in studies of terrestrial species-landscape relationships (Chandler and Hepinstall-Cymerman 2016). We suggest researchers investigating scale consider this method to avoid model bias and scale underestimation (Miguet et al 2017), especially when studying terrestrial vertebrates in landscapes with fine intrinsic spatial structure (i.e. patch sizes < 1000 m; Mertes and Jetz 2018).…”

Section: Discussionmentioning

confidence: 99%

“…1b). This approach can reduce model bias and lead to more accurate interpretations of species-landscape relationships (Aue et al 2012, Chandler and Hepinstall-Cymerman 2016, Miguet et al 2017). 2), which provides a more realistic depiction of many ecological processes (e.g.…”

Section: Evaluating the Scaling Of Urbanization Responsementioning

confidence: 99%

“…We used the equation above to calculate two scales of effect (following Miguet et al 2017). First, we calculated the scale at which urbanization had a maximal effect on the response variable (scale max ), which is the distance at which w m is maximal (Fig.…”

Section: Evaluating the Scaling Of Urbanization Responsementioning

confidence: 99%

“…2, middle column; Aue et al 2012). 2, third column; Miguet et al 2017). Second, we determined the scale that contained 90% of the effect of urbanization (scale 90 ) by calculating the distance at which a circular area around the focal site contained 90% of w m (Fig.…”

Section: Evaluating the Scaling Of Urbanization Responsementioning

confidence: 99%

“…Mismatch between the scale of analysis (defined by the researcher) and the scale of effect (defined by the true relationship) represents a version of the Modifiable Areal Unit Problem (Gehlke and Biehl 1934), whereby inference becomes an artifact of spatial sampling scheme rather than an ecological process (Jelinski and Wu 1996). Accurately identifying the scale of effect can aid in theorybuilding regarding species-landscape relationships (Weins 1989, Levin 1992) and species' conservation efforts by identifying the scales at which landscapes should be managed (Miguet et al 2017, Gallo et al 2018. Accurately identifying the scale of effect can aid in theorybuilding regarding species-landscape relationships (Weins 1989, Levin 1992) and species' conservation efforts by identifying the scales at which landscapes should be managed (Miguet et al 2017, Gallo et al 2018.…”

Section: Introductionmentioning

confidence: 99%

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At what spatial scale(s) do mammals respond to urbanization?

et al. 2019

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Spatial scale is fundamental in understanding species-landscape relationships because species' responses to landscape characteristics typically vary across scales. Nonetheless, such scales are often unidentified or unreliably predicted by theory. Many landscapes worldwide are urbanizing, yet the spatial scaling of species' responses to urbanization is poorly understood. We investigated the spatial scaling of urbanization effects on a community of 15 mammal species using ~60 000 wildlife detections collected from a constellation of 207 camera traps across an extensive urban park system. We embedded a bivariate Gaussian kernel in hierarchical multi-species models to determine two scales of effect (a scale of maximal effect and a broader scale of cumulative landscape effect) for two biological responses (occupancy and site visit frequency) across two seasons (winter and summer) for each species. We then assessed whether scales of effect varied according to theoretical predictions associated with biological responses and species traits (body size and mobility). Scales of effect ranged from < 50 m to > 9000 m and varied among species, but not as predicted by theory. Species' occupancy generally showed a weak response to urbanization and the scale of this effect was both highly uncertain and consistent across species. We did not detect any relationship between scales of effect and species' body size or mobility, nor was there any evident pattern of scaling across biological response or seasons. These results imply that 1) urbanization effects on mammals manifest across a very broad spectrum of spatial scales, and 2) current theories that a priori predict the scale at which urbanization affects mammals may be of limited use within a given system. Overall, this study suggests that developing general theory regarding the scaling of species-landscape relationships requires additional empirical work conducted across multiple species, systems and timescales.

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Section: Discussionmentioning

confidence: 99%

Section: Evaluating the Scaling Of Urbanization Responsementioning

confidence: 99%

Section: Evaluating the Scaling Of Urbanization Responsementioning

confidence: 99%

Section: Evaluating the Scaling Of Urbanization Responsementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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At what spatial scale(s) do mammals respond to urbanization?

et al. 2019

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Support for an area–heterogeneity tradeoff for biodiversity in croplands

Khan

Fahrig

Martin

2023

Ecological Applications

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Rapid expansion of the human population poses a challenge for wildlife conservation in agricultural landscapes. One proposal for addressing this challenge is to increase biodiversity in such landscapes by increasing crop diversity. However, studies report both positive and negative effects of crop diversity on biodiversity. One possible explanation, derived from the "area-heterogeneity tradeoff hypothesis," is that the effect of crop diversity on biodiversity depends on a tradeoff between increasing the number of crop types in a landscape and decreasing the amount of each single crop type. This should cause positive effects of increasing crop diversity at low to intermediate crop diversity and negative effects at intermediate to high crop diversity. We also propose two factors that could change the point at which the effect of increasing crop diversity shifts from positive to negative. First, we predicted that this shift would occur at a lower crop diversity when the surrounding landscape contains less semi-natural habitat and at a higher crop diversity when the landscape contains more semi-natural habitat. This should increase the likelihood of detecting negative effects of crop diversity when semi-natural cover is low and positive effects when it is high. Second, we predicted that the shift from a positive to negative effect would occur at a lower crop diversity when it is measured locally than when it is measured at greater distances from the site, making detection of negative crop diversity effects more likely when measurements are at local extents. We tested these predictions using data on the biodiversity of herbaceous plants, butterflies, syrphid flies, woody plants, bees, carabid beetles, spiders, and birds at 221 crop field edges in Eastern Ontario, Canada. We found support for an area-crop diversity tradeoff. Semi-natural cover and measurement extent influenced the biodiversity-crop diversity relationship, with positive effects when semi-natural cover was high and negative effects when semi-natural cover was low and when crop diversity was measured at local extents. The results suggest that policies/guidelines designed to increase crop diversity will not benefit biodiversity in the landscapes where conservation action is

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Predicting species distributions: unifying model selection and scale optimization for multi‐scale occupancy models

Stevens

Conway

2019

Ecosphere

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Geographic distributions are a basic component of a species’ ecology, and predicting distributions is a fundamental task of conservation and resource management. Reliable prediction depends on identification of appropriate scales of effect for environmental data, and scale‐optimization techniques are thus desirable to identify optimal scales for predictor variables. Recent statistical developments have also advanced methods of model selection based explicitly on predictive ability, which differ from commonly used methods that regulate model structures via anticipated predictive performance. Such methods are beginning to permeate into species distribution models (SDMs), yet there remains no consensus methodology for developing optimally predictive multi‐scale SDMs when covariate data are collected over a range of scales. Thus, we compared the performance of common approaches for scale optimization and model selection in terms of their ability to produce optimally predictive multi‐scale Bayesian occupancy models for predicting a species distribution, using models of the breeding distribution for King Rails (Rallus elegans) as a case study. Our results demonstrate sizable gains in predictive performance for hierarchical occupancy models selected via their ability to predict out‐of‐sample data using the logarithmic scoring rule, as compared to models selected using information criteria (deviance information criteria [DIC] and Watanabe information criteria [WAIC]). Information criteria commonly selected individual covariates, as well as scales of effect for those covariates, with suboptimal predictive performance. Performance of models selected using the logarithmic scoring rule was also robust across the method of scale optimization, which was not true for models selected using DIC and WAIC. Thus, we demonstrate empirical benefits of study designs and statistical tools that enable covariate and scale selection based explicitly on predictive ability. Our results also imply that more careful consideration of what constitutes an optimal scale is warranted in many ecological applications, as the meaning of optimal is not independent of the technique used for scale selection.

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How to quantify a distance‐dependent landscape effect on a biological response

Cited by 48 publications

References 20 publications

At what spatial scale(s) do mammals respond to urbanization?

At what spatial scale(s) do mammals respond to urbanization?

Support for an area–heterogeneity tradeoff for biodiversity in croplands

Predicting species distributions: unifying model selection and scale optimization for multi‐scale occupancy models

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