Used‐habitat calibration plots: a new procedure for validating species distribution, resource selection, and step‐selection models

Fieberg, John; Forester, James D.; Street, Garrett M.; Johnson, Douglas H.; ArchMiller, Althea A.; Matthiopoulos, Jason

doi:10.1111/ecog.03123

Cited by 47 publications

(78 citation statements)

References 67 publications

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“…As a final step, new data can be simulated from fitted models. Simulations can be used to obtain estimates of space use (i.e., the utilization distribution), identify corridors of high use, asses the power of the model (testing how well parameters can be recovered as a function of sample size), or perform model validation (Fieberg et al., ). Many packages that fit models also provide methods to simulate from fitted models (e.g., ctmm or moveHMM).…”

Section: Functionalitymentioning

confidence: 99%

Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses

Signer

Fieberg

Avgar

2019

Ecology and Evolution

442

446

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Advances in tracking technology have led to an exponential increase in animal location data, greatly enhancing our ability to address interesting questions in movement ecology, but also presenting new challenges related to data management and analysis. Step‐selection functions (SSFs) are commonly used to link environmental covariates to animal location data collected at fine temporal resolution. SSFs are estimated by comparing observed steps connecting successive animal locations to random steps, using a likelihood equivalent of a Cox proportional hazards model. By using common statistical distributions to model step length and turn angle distributions, and including habitat‐ and movement‐related covariates (functions of distances between points, angular deviations), it is possible to make inference regarding habitat selection and movement processes or to control one process while investigating the other. The fitted model can also be used to estimate utilization distributions and mechanistic home ranges. Here, we present the R package (animal movement tools) that allows users to fit SSFs to data and to simulate space use of animals from fitted models. The package also provides tools for managing telemetry data. Using fisher (Pekania pennanti) data as a case study, we illustrate a four‐step approach to the analysis of animal movement data, consisting of data management, exploratory data analysis, fitting of models, and simulating from fitted models.

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Section: Functionalitymentioning

confidence: 99%

Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses

Signer

Fieberg

Avgar

2019

Ecology and Evolution

442

446

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“…Instrumental to this are novel approaches to model evaluation and validation (e.g., [75]) that are generally independent of model choice and response variable type.…”

Section: Box 3 Correlative Versus Mechanistic Modelsmentioning

confidence: 99%

Outstanding Challenges in the Transferability of Ecological Models

Yates

Bouchet

Caley

et al. 2018

Trends in Ecology & Evolution

466

456

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Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions. Predicting the UnknownPredictions facilitate the formulation of quantitative, testable hypotheses that can be refined and validated empirically [1]. Predictive models have thus become ubiquitous in numerous scientific disciplines, including ecology [2], where they provide means for mapping species distributions, explaining population trends, or quantifying the risks of biological invasions and disease outbreaks (e.g., [3,4]). The practical value of predictive models in supporting policy and decision making has therefore grown rapidly (Box 1) [5]. With that has come an increasing desire to predict (see Glossary) the state of ecological features (e.g., species, habitats) and our likely impacts upon them [5], prompting a shift from explanatory models to anticipatory predictions [2]. However, in many situations, severe data deficiencies preclude the development of specific models, and the collection of new data can be prohibitively costly or simply impossible [6]. It is in this context that interest in transferable models (i.e., those that can be legitimately projected beyond the spatial and temporal bounds of their underlying data [7]) has grown.Transferred models must balance the tradeoff between estimation and prediction bias and variance (homogenization versus nontransferability, sensu [8]). Ultimately, models that can Highlights Models transferred to novel conditions could provide predictions in data-poor scenarios, contributing to more informed management decisions.The determinants of ecological predictability are, however, still insufficiently understood.Predictions from transferred ecological models are affected by species' traits, sampling biases, biotic interactions, nonstationarity, and the degree of environmental dissimilarity between reference and target systems.We synthesize six technical and six fundamental challenges that, if resolved, will catalyze practical and conceptual advances in model transfers.We propose that the most immediate obstacle to improving understanding lies in the absence of a widely applicable set of metrics for assessing transferability, and that encouraging the development of models grounded in well-established mech...

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“…Testing indices of performance (based on classification or on predicted probability/ suitability) Li and Guo 2013, Rapacciuolo et al 2014, Fieberg et al 2018 • Several propositions have been made to base model performance metrics on the predicted probability of occurrence and on model calibration, rather than on the success to predict presences and absences. • Probabilistic VS studies have shown that metrics based on presence-absence classification rates are limited by the probabilistic nature of the distribution (e.g.…”

Section: Stagementioning

confidence: 99%

Testing methods in species distribution modelling using virtual species: what have we learnt and what are we missing?

2019

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Species distribution models (SDMs) have become one of the major predictive tools in ecology. However, multiple methodological choices are required during the modelling process, some of which may have a large impact on forecasting results. In this context, virtual species, i.e. the use of simulations involving a fictitious species for which we have perfect knowledge of its occurrence-environment relationships and other relevant characteristics, have become increasingly popular to test SDMs. This approach provides for a simple virtual ecologist framework under which to test model properties, as well as the effects of the different methodological choices, and allows teasing out the effects of targeted factors with great certainty. This simplification is therefore very useful in setting up modelling standards and best practice principles. As a result, numerous virtual species studies have been published over the last decade. The topics covered include differences in performance between statistical models, effects of sample size, choice of threshold values, methods to generate pseudo-absences for presence-only data, among many others. These simulations have therefore already made a great contribution to setting best modelling practices in SDMs. Recent software developments have greatly facilitated the simulation of virtual species, with at least three different packages published to that effect. However, the simulation procedure has not been homogeneous, which introduces some subtleties in the interpretation of results, as well as differences across simulation packages. Here we 1) review the main contributions of the virtual species approach in the SDM literature; 2) compare the major virtual species simulation approaches and software packages; and 3) propose a set of recommendations for best simulation practices in future virtual species studies in the context of SDMs.

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Used‐habitat calibration plots: a new procedure for validating species distribution, resource selection, and step‐selection models

Cited by 47 publications

References 67 publications

Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses

Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses

Outstanding Challenges in the Transferability of Ecological Models

Testing methods in species distribution modelling using virtual species: what have we learnt and what are we missing?

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