Modeling current and future species distribution of breeding birds as regional essential biodiversity variables (SD EBVs): A bird perspective in Swiss Alps

Tehrani, Nasrin Amini; Naimi, Babak; Jaboyedoff, Michel

doi:10.1016/j.gecco.2021.e01596

Cited by 14 publications

(9 citation statements)

References 132 publications

(192 reference statements)

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“…This is worrying because analysts frequently use sample size as the sole criterion when deciding whether or not to fit SDMs for a given species (e.g. Amini Tehrani et al, 2021; Hoveka et al, 2020, 2022; Spiers et al, 2018; Zellmer et al, 2019). We agree with Santini et al (2021), who noted that, of the studies making methodological recommendations in the SDM literature, those making convenient recommendations (e.g.…”

Section: Discussionmentioning

confidence: 99%

Causal inference and large‐scale expert validation shed light on the drivers of SDM accuracy and variance

Boyd

Harvey

Roy

et al. 2023

Diversity and Distributions

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Aim: To develop a causal understanding of the drivers of Species distribution model (SDM) performance. Location: United Kingdom (UK). Methods: We measured the accuracy and variance of SDMs fitted for 518 species of invertebrate and plant in the UK. Our measure of variance reflects variation among replicate model fits, and taxon experts assessed model accuracy. Using directed acyclic graphs, we developed a causal model depicting plausible effects of explanatory variables (e.g. species' prevalence, sample size) on SDM accuracy and variance and quantified those effects using a multilevel piecewise path model. Results: According to our model, sample size and niche completeness (proportion of a species' niche covered by sampling) directly affect SDM accuracy and variance. Prevalence and range completeness have indirect effects mediated by sample size.Challenging conventional wisdom, we found that the effect of prevalence on SDM accuracy is positive. This reflects the facts that sample size has a positive effect on accuracy and larger sample sizes are possible for widespread species. It is possible, however, that the omission of an unobserved confounder biased this effect. Previous studies, which reported negative correlations between prevalence and SDM accuracy, conditioned on sample size.Main conclusions: Our model explicates the causal basis of previously reported correlations between SDM performance and species/data characteristics. It also suggests that niche completeness has similarly large effects on SDM accuracy and variance as sample size. Analysts should consider niche completeness, or proxies thereof, in addition to sample size when deciding whether modelling is worthwhile.

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Section: Discussionmentioning

confidence: 99%

Causal inference and large‐scale expert validation shed light on the drivers of SDM accuracy and variance

Boyd

Harvey

Roy

et al. 2023

Diversity and Distributions

View full text Add to dashboard Cite

show abstract

“…First, our product pertains to >5,000 species, much more than most. Second, the majority of the first generation EBVs were constructed using correlative or deductive species distribution models (SDMs) that lack any temporal component (Amini Tehrani et al, 2021;Fernández et al, 2020;Velásquez-Tibatá et al, 2018; also see e.g., https://portal.geobon.org/ebv-detail?id=5 and https://mol.org/indicators/habitat). In contrast, our occupancy models are temporally explicit.…”

Section: Comparison With the First Generation Of Species Distribution...mentioning

confidence: 99%

“…Hence, modelling is required. Several types of model might be considered: correlative habitat suitability models (Amini Tehrani et al, 2021); deductive habitat suitability models, which are based on expert advice about habitat associations (e.g., https://mol.org/indicators/habitat); or models with a temporal component that estimate changes in species' occupancy (Outhwaite et al, 2020). These models vary in their suitability for the populating the spatial and temporal axes of the species-space-time cube, and the optimal choice is not always clear.…”

Section: Introductionmentioning

confidence: 99%

An operational workflow for producing periodic estimates of species occupancy at large scales

Boyd¹,

August²,

Cooke³

et al. 2022

Preprint

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Policy makers require high-level summaries of biodiversity change. However, deriving such summaries from raw biodiversity data is a complex process involving several intermediary stages. In this paper, we describe a workflow for generating annual estimates of species’ occupancy at national scales from raw species occurrence data, which can be used to construct a range of policy-relevant biodiversity indicators. We describe the workflow in detail: from data acquisition, data assessment and data manipulation, through modelling, model evaluation, application and dissemination. At each stage, we draw on our experience developing and applying the workflow for almost a decade to outline the challenges that analysts might face. These challenges span many areas of ecology, taxonomy, data science, computing and statistics. In our case, a key output of the workflow is annual estimates of occupancy, with measures of uncertainty, for over 5,000 species in each of several defined “regions” (e.g., countries, protected areas, etc.) of the United Kingdom from 1970-2019. This product corresponds closely to the notion of a species distribution “Essential Biodiversity Variable” (EBV). Throughout the paper, we note where the workflow can be adapted to other situations (e.g., geographic regions or data types). We also highlight areas where the workflow can be improved; in particular, we suggest incorporation of methods to diagnose biases in the species occurrence data, to understand whether and to what extent these bias downstream products, and to mitigate them if needed. Finally, we compare the data products generated using our workflow to the first generation of species distribution EBVs and the “idealized” product as defined by others. Going forward, we hope that this paper can act as a template for research groups around the world seeking to develop similar data products.

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“…Hence, modelling is required to infer information on species' occurrences in cells with no data, and to correct for measurement error in those cells for which data are available. Several types of model might be considered: correlative habitat suitability models (Amini Tehrani, Naimi & Jaboyedoff, 2021); deductive habitat suitability models, which are based on expert advice about habitat associations (e.g. https://mol.org/indicators/habitat; Jetz, McPherson & Guralnick, 2012); or models with a temporal component that estimate changes in species' distributions (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Amini Tehrani et al . (2021) constructed an EBV for 14 species of bird in Switzerland using an ensemble of habitat suitability models. At the time of writing, there are two species distribution EBVs available on the GEO BON EBV data portal (https://portal.geobon.org/home), both of which were derived using habitat suitability models.…”

Section: Introductionmentioning

confidence: 99%

An operational workflow for producing periodic estimates of species occupancy at national scales

et al. 2023

View full text Add to dashboard Cite

Policy makers require high‐level summaries of biodiversity change. However, deriving such summaries from raw biodiversity data is a complex process involving several intermediary stages. In this paper, we describe an operational workflow for generating annual estimates of species occupancy at national scales from raw species occurrence data, which can be used to construct a range of policy‐relevant biodiversity indicators. We describe the workflow in detail: from data acquisition, data assessment and data manipulation, through modelling, model evaluation, application and dissemination. At each stage, we draw on our experience developing and applying the workflow for almost a decade to outline the challenges that analysts might face. These challenges span many areas of ecology, taxonomy, data science, computing and statistics. In our case, the principal output of the workflow is annual estimates of occupancy, with measures of uncertainty, for over 5000 species in each of several defined ‘regions’ (e.g. countries, protected areas, etc.) of the UK from 1970 to 2019. This data product corresponds closely to the notion of a species distribution Essential Biodiversity Variable (EBV). Throughout the paper, we highlight methodologies that might not be applicable outside of the UK and suggest alternatives. We also highlight areas where the workflow can be improved; in particular, methods are needed to mitigate and communicate the risk of bias arising from the lack of representativeness that is typical of biodiversity data. Finally, we revisit the ‘ideal’ and ‘minimal’ criteria for species distribution EBVs laid out in previous contributions and pose some outstanding questions that should be addressed as a matter of priority. Going forward, we hope that this paper acts as a template for research groups around the world seeking to develop similar data products.

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Modeling current and future species distribution of breeding birds as regional essential biodiversity variables (SD EBVs): A bird perspective in Swiss Alps

Cited by 14 publications

References 132 publications

Causal inference and large‐scale expert validation shed light on the drivers of SDM accuracy and variance

Causal inference and large‐scale expert validation shed light on the drivers of SDM accuracy and variance

An operational workflow for producing periodic estimates of species occupancy at large scales

An operational workflow for producing periodic estimates of species occupancy at national scales

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