Location‐only and use‐availability data: analysis methods converge

McDonald, Lyman L.; Manly, Bryan F. J.; Huettmann, Falk; Thogmartin, Wayne E.

doi:10.1111/1365-2656.12145

Cited by 25 publications

(29 citation statements)

References 53 publications

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“…In contrast, the coefficients of apparent suitability (the gammas) will vary as functions of habitat availability and population density. This is the important distinction that is missing from methods such as environmental niche factor analysis (Hirzel et al 2002) and related multivariate methods, also known as ''profile'' methods (McDonald et al 2013). The dependence of the c's on habitat availability has been previously modeled ), so we focus here on the dependence of apparent habitat suitability on prevailing population density.…”

Section: Connecting Habitat Use To Density-dependent Population Growthmentioning

confidence: 99%

“…attention (Elith and Leathwick 2009, Sobero´n and Nakamura 2009, Peterson et al 2011, Warren 2012, 2013, McDonald et al 2013, McInerny and Etienne 2013. Their sensitivity on arbitrary scale decisions made by the analyst (Austin 1999, Beyer et al 2010) and instability in changing environments (Randin et al 2006, Zurell et al 2009, McLoughlin et al 2010, Sinclair et al 2010, Wenger and Olden 2012 has alerted practitioners to the dangers of their widespread and unvalidated application.…”

Section: Introductionmentioning

confidence: 99%

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Establishing the link between habitat selection and animal population dynamics

Matthiopoulos

Fieberg

Aarts³

et al. 2015

Ecological Monographs

128

200

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Abstract. Although classical ecological theory (e.g., on ideal free consumers) recognizes the potential effect of population density on the spatial distribution of animals, empirical species distribution models assume that species-habitat relationships remain unchanged across a range of population sizes. Conversely, even though ecological models and experiments have demonstrated the importance of spatial heterogeneity for the rate of population change, we still have no practical method for making the connection between the makeup of real environments, the expected distribution and fitness of their occupants, and the long-term implications of fitness for population growth. Here, we synthesize several conceptual advances into a mathematical framework using a measure of fitness to link habitat availability/selection to (density-dependent) population growth in mobile animal species. A key feature of this approach is that it distinguishes between apparent habitat suitability and the true, underlying contribution of a habitat to fitness, allowing the statistical coefficients of both to be estimated from multiple observation instances of the species in different environments and stages of numerical growth. Hence, it leverages data from both historical population time series and snapshots of species distribution to predict population performance under environmental change. We propose this framework as a foundation for building more realistic connections between a population's use of space and its subsequent dynamics (and hence a contribution to the ongoing efforts to estimate a species' critical habitat and fundamental niche). We therefore detail its associated definitions and simplifying assumptions, because they point to the framework's future extensions. We show how the model can be fit to data on species distributions and population dynamics, using standard statistical methods, and we illustrate its application with an individual-based simulation. When contrasted with nonspatial population models, our approach is better at fitting and predicting population growth rates and carrying capacities. Our approach can be generalized to include a diverse range of biological considerations. We discuss these possible extensions and applications to real data.

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Section: Connecting Habitat Use To Density-dependent Population Growthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Establishing the link between habitat selection and animal population dynamics

Matthiopoulos

Fieberg

Aarts³

et al. 2015

Ecological Monographs

128

200

View full text Add to dashboard Cite

show abstract

“…; Warton & Shepherd ) and estimation of resource selection functions (Aarts, Fieberg & Matthiopoulos ; McDonald et al . ). PPMs enjoy particular benefits in interpretation and implementation.…”

Section: Introductionmentioning

confidence: 97%

Point process models for presence‐only analysis

et al. 2015

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Summary1. Presence-only data are widely used for species distribution modelling, and point process regression models are a flexible tool that has considerable potential for this problem, when data arise as point events. 2. In this paper, we review point process models, some of their advantages and some common methods of fitting them to presence-only data. 3. Advantages include (and are not limited to) clarification of what the response variable is that is modelled; a framework for choosing the number and location of quadrature points (commonly referred to as pseudoabsences or 'background points') objectively; clarity of model assumptions and tools for checking them; models to handle spatial dependence between points when it is present; and ways forward regarding difficult issues such as accounting for sampling bias. 4. Point process models are related to some common approaches to presence-only species distribution modelling, which means that a variety of different software tools can be used to fit these models, including MAXENT or generalised linear modelling software.

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“…Analyses employing space use functions (e.g., resource utilization distribution [UD], resource selection function [RSF], step selection function [SSF]), or some mix of these models have attempted to address resource use for many years (Van Winkle , Manly et al , Fortin et al , Forester et al , McDonald et al , Thurfjell et al , Avgar et al ). The functions attempt to determine what habitats or resources animals are using, given what is available, and how environmental predictor covariates influence that selection.…”

Section: Discussionmentioning

confidence: 99%

Avoiding the scale sampling problem: A consilient solution

Bissonette

2016

J Wildl Manag

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What if there was a way to solve one of the most complex and important scale challenges that ecologists face: the selection of the appropriate sampling scale resolution and extent when conducting studies. To date, the investigator conducting any study has been tasked with this challenge. Qualitatively different patterns arise as the scale resolution or extent of the observation is changed. Likewise, different patterns arise as the scales of the sampling are changed. Too often, data at one resolution are aggregated or interpretations of patterns at smaller scale resolutions and extents are extrapolated to larger extents. When this is done, either modifiable areal unit problems or extrapolation errors arise. I argue that consilient developments in movement ecology, graph (network) theory and analyses, and important technical advances in biologging can be linked synergistically to directly address the sampling scale problem, particularly in studies where understanding animal response to changing landscapes is the goal. The essence of the solution is that rather than having to decide the spatial extent and resolution to use, the issue is avoided by allowing the telemetered animal to show the relevant scales it uses as it moves during its daily, seasonal, and annual activities. The consilient result is that big data at the appropriate scales can be extracted rather than aggregated as is now the rule. Without data aggregation, no modifiable areal unit problems are involved because the dynamics are network based. Additionally, use of first passage time methodology and measurement of area‐restricted movements allow assessments of the scales of activity and the effects of environmental covariates on animal movement. I begin with a brief history of the concept of scale, describing what is considered to be the essence of the problem. I follow with a brief explanation of the developments in movement ecology, graph theory and analysis, and the technical advancements in biologging with examples from studies of wildlife species that are using these new developments. I then describe how their consilient integration can provide a different way of thinking about scale resolution and extent in ecology and suggest how ecologists can avoid the observer sampling scale complication by making a critical scale distinction and by using first passage time analyses. © 2016 The Wildlife Society.

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Location‐only and use‐availability data: analysis methods converge

Cited by 25 publications

References 53 publications

Establishing the link between habitat selection and animal population dynamics

Establishing the link between habitat selection and animal population dynamics

Point process models for presence‐only analysis

Avoiding the scale sampling problem: A consilient solution

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