Species Distribution Models and Impact Factor Growth in Environmental Journals: Methodological Fashion or the Attraction of Global Change Science

Brotons, Lluı́s

doi:10.1371/journal.pone.0111996

Cited by 23 publications

(21 citation statements)

References 16 publications

(18 reference statements)

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“…Forecasting the potential effects of environmental changes on species populations has been one of the central applications of SDMs for avian and non-avian species (Brotons 2014). Typical approaches to conservation include forecasting range shifts according to climate change and the efficiency of the current networks of protected areas, from local (Veloz et al 2013) to continental scales (Hole et al 2011, Table 1).…”

Section: Assessing the Potential Impact Of Environmental Changesmentioning

confidence: 99%

Avian SDMs: current state, challenges, and opportunities

Engler

Stiels

Schidelko

et al. 2017

Journal of Avian Biology

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Quantifying species distributions using species distribution models (SDMs) has emerged as a central method in modern biogeography. These empirical models link species occurrence data with spatial environmental information. Since their emergence in the 1990s, thousands of scientific papers have used SDMs to study organisms across the entire tree of life, with birds commanding considerable attention. Here, we review the current state of avian SDMs and point to challenges and future opportunities for specific applications, ranging from conservation biology, invasive species and predicting seabird distributions, to more general topics such as modeling avian diversity, niche evolution and seasonal distributions at a biogeographic scale. While SDMs have been criticized for being phenomenological in nature, and for their inability to explicitly account for a variety of processes affecting populations, we conclude that they remain a powerful tool to learn about past, current, and future species distributions -at least when their limitations and assumptions are recognized and addressed. We close our review by providing an outlook on prospects and synergies with other disciplines in which avian SDMs can play an important role.

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Section: Assessing the Potential Impact Of Environmental Changesmentioning

confidence: 99%

Avian SDMs: current state, challenges, and opportunities

Engler

Stiels

Schidelko

et al. 2017

Journal of Avian Biology

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“…Some techniques (e.g., generalized additive models) require data on both the presence and absence of species within an area, while other techniques (e.g., ecological niche factor analysis) require only data on the presence of a species (Guillera‐Arroita et al, ). Evidence suggests that methods based on presence–absence data may more accurately predict species distributions than methods based only on presence data, particularly for wide‐ranging species (Brotons, ). However, predictions of species distributions based on presence–absence data may not be reliable if there is bias in the selection of absence points (Phillips et al, ).…”

Section: Measuring Species Range Shifts In Practicementioning

confidence: 99%

Diversity and suitability of existing methods and metrics for quantifying species range shifts

Yalcin

Leroux

2017

Global Ecol. Biogeogr.

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Aim: The quantification of species range shifts is critical for developing effective plans to conserve biodiversity. There are numerous methods and metrics for quantifying species range shifts, but we currently lack a comprehensive review of existing approaches used in species range shift studies. Location: Global.Time period: 2013 -2014.Major taxa studied: All taxa. Methods:We conducted a quantitative literature review to first identify the methods currently used for defining a species' range over a particular time and then to identify metrics used for measuring changes in species ranges over time. We provide a roadmap for the selection of methods and metrics for measuring species ranges and species range shifts by discussing opportunities, assumptions and constraints of the different approaches.Results: Our literature review revealed six main methods for defining species ranges: observational studies, grid-based mapping, convex hull, kriging, species distribution modelling and hybrid methods. These methods are used with three main metric classes to measure species range shifts: changes in range limit, size and the probability of species occurrences or suitability. Most methods for defining species ranges and subsequent range shifts can be applied to different spatial extents and resolutions and taxa. However, only species distribution models (SDMs) and hybrid methods allow for the exploration of the relationship between species occurrence and environmental variables, and only these methods can be used for forecasting species ranges into future environments.Likewise, the inclusion of ecological processes in range shift calculations requires researchers to use hybrid methods or mechanistic models.Main conclusions: Our review revealed a high diversity of methods and metrics used to quantify species range shifts. As these methods and metrics underlie many of the conservation strategies proposed for climate change mitigation (e.g., protection of refugia), we urge the conservation community to evaluate underlying approaches for defining species ranges and measuring species range shifts with an equal level of scrutiny as the conservation strategies that these methods and metrics enable. K E Y W O R D Sclimate change, global change, grid-based map, hybrid method, mechanistic model, meta-population model, range contraction, range expansion, SDM, species distribution Prentice, 2011). It is therefore surprising that we lack a comprehensive review of the most common methods and metrics used to define species ranges and species range shifts.Studies of ranges shifts necessarily begin by defining a species' range, followed by quantifying changes in this species' range over time. | METHODS A ND METRICS F OR ME A S U R I N G C H A N G E S I N S P E C I E S RAN GES OVE R TIME AN D S PACEThe use of time as a comparative parameter requires a reference measurement of a species' range to be defined for each period before calculating the range shift between multiple periods. We conducted a literature search to identify key...

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“…Knowledge of where species are is important for developing robust wildlife management strategies to address issues of conservation, epidemiology and human-wildlife conflict. In recent years species distribution models (SDMs) have become a popular and potentially powerful tool to inform such investigations (Brotons 2014). These models assess known occurrence against various environmental predictors to establish the ecological niches in which a given species can survive Hijmans & Elith 2013).…”

Section: Introductionmentioning

confidence: 99%

Structuring the unstructured: estimating species-specific absence from multi-species presence data to inform pseudo-absence selection in species distribution models

Croft

Smith

2019

Preprint

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1. Species distribution models (SDMs) are an increasingly popular tool in ecology which, together with a vast wealth of data from citizen science projects, have the potential to dramatically improve our understanding of species behaviour for applications such as conservation and wildlife management. However, many of the best performing models require information regarding survey effort, specifically absence, which is typically lacking in opportunistic datasets. To facilitate the use of such models, pseudo-absences from locations without recorded presence must be assumed. Several studies have suggested that survey effort, and hence likely absence, could be estimated from presence-only data by considering records across "target groups" of species defined according to taxonomy.2. We performed a probabilistic analysis, computing the conditional probability of recording a species given a particular set of species are also recorded, to test the validity of defining target groups by taxonomic order and to explore other potential groupings. Based on this quantification of recording associations we outline a new method to inform pseudo-absence selection comparing predictive performance, measured the area under curve (AUC) statistic, against the standard method of selection across a series of SDMs.3. Our findings show some support for target grouping classification based on taxonomy but indicate that an alternative classification using survey method may be more appropriate for informing effort and consequently absence. Across 49 terrestrial mammal species, pseudo-absence selection using our proposed method outperformed that of the standard method showing an improvement in the predictive performance of presenceabsence models for 17 out of 22 with sufficient data to elicit a significant difference. Based on our method we also observed a substantial improvement in the performance of presence-absence models compared to that of presence-only models (MaxEnt) with a higher AUC for all 22 species showing a significant difference between approaches.4. We conclude that our method produces sensible robust pseudo-absences which either compliment patterns in known presences or, where conflicts occur, are explainable in terms of ecological variables potentially improving our understanding of species behaviour. Furthermore, we suggest that presence-absence models using these pseudo-absences provide a viable alternative to MaxEnt when modelling using presence-only data.

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Species Distribution Models and Impact Factor Growth in Environmental Journals: Methodological Fashion or the Attraction of Global Change Science

Cited by 23 publications

References 16 publications

Avian SDMs: current state, challenges, and opportunities

Avian SDMs: current state, challenges, and opportunities

Diversity and suitability of existing methods and metrics for quantifying species range shifts

Structuring the unstructured: estimating species-specific absence from multi-species presence data to inform pseudo-absence selection in species distribution models

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