Biodiversity Modelling as Part of an Observation System

Ferrier, Simon; Jetz, Walter; Scharlemann, Jörn P. W.

doi:10.1007/978-3-319-27288-7_10

Cited by 18 publications

(12 citation statements)

References 69 publications

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“…They use species occurrence data and environmental data to produce a set of rules explaining the environmental space where species were collected or observed (Ferrier et al 2017). Species distribution models (SDMs) are a common tool used to study these relationships.…”

Section: Introductionmentioning

confidence: 99%

“…Species distribution models (SDMs) are a common tool used to study these relationships. They use species occurrence data and environmental data to produce a set of rules explaining the environmental space where species were collected or observed (Ferrier et al 2017). All applications of SDMs, however, assume that species occurrence data are largely free of spatial error.…”

Section: Introductionmentioning

confidence: 99%

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The effect of positional error on fine scale species distribution models increases for specialist species

et al. 2019

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Species occurrences inherently include positional error. Such error can be problematic for species distribution models (SDMs), especially those based on fine-resolution environmental data. It has been suggested that there could be a link between the influence of positional error and the width of the species ecological niche. Although positional errors in species occurrence data may imply serious limitations, especially for modelling species with narrow ecological niche, it has never been thoroughly explored. We used a virtual species approach to assess the effects of the positional error on fine-scale SDMs for species with environmental niches of different widths. We simulated three virtual species with varying niche breadth, from specialist to generalist. The true distribution of these virtual species was then altered by introducing different levels of positional error (from 5 to 500 m). We built generalized linear models and MaxEnt models using the distribution of the three virtual species (unaltered and altered) and a combination of environmental data at 5 m resolution. The models' performance and niche overlap were compared to assess the effect of positional error with varying niche breadth in the geographical and environmental space. The positional error negatively impacted performance and niche overlap metrics. The amplitude of the influence of positional error depended on the species niche, with models for specialist species being more affected than those for generalist species. The positional error had the same effect on both modelling techniques. Finally, increasing sample size did not mitigate the negative influence of positional error. We showed that fine-scale SDMs are considerably affected by positional error, even when such error is low. Therefore, where new surveys are undertaken, we recommend paying attention to data collection techniques to minimize the positional error in occurrence data and thus to avoid its negative effect on SDMs, especially when studying specialist species.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The effect of positional error on fine scale species distribution models increases for specialist species

et al. 2019

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“…These include: (1) follow-up of selected novel anecdotes to inform climate adaptation management, prioritized on the basis of potential impact on biodiversity, potential for amelioration through management, and relevance to land managers’ regional and social contexts [75]; (2) comparison of anecdotal data with modelled projections of climate change impacts to provide an independent line of evidence for model validation (e.g. using whole-of-biodiversity compositional turnover methods [76]); (3) further exploration of interactions between climate and land use change [11]; and (4) use of the anecdotal records to contribute to designing the focus and location of climate change monitoring networks [77].…”

Section: Discussionmentioning

confidence: 99%

Recent climate-driven ecological change across a continent as perceived through local ecological knowledge

et al. 2019

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Documenting effects of climate change is an important step towards designing mitigation and adaptation responses. Impacts of climate change on terrestrial biodiversity and ecosystems have been well-documented in the Northern Hemisphere, but long-term data to detect change in the Southern Hemisphere are limited, and some types of change are generally difficult to measure. Here we present a novel approach using local ecological knowledge to facilitate a continent-scale view of climate change impacts on terrestrial biodiversity and ecosystems that people have perceived in Australia. We sought local knowledge using a national web-based survey, targeting respondents with close links to the environment (e.g. farmers, ecologists), and using a custom-built mapping tool to ask respondents to describe and attribute recent changes they had observed within an area they knew well. Results drawn from 326 respondents showed that people are already perceiving simple and complex climate change impacts on hundreds of species and ecosystems across Australia, significantly extending the detail previously reported for the continent. While most perceived trends and attributions remain unsubstantiated, >35 reported anecdotes concurred with examples in the literature, and >20 were reported more than once. More generally, anecdotes were compatible with expectations from global climate change impact frameworks, including examples across the spectrum from organisms (e.g. increased mortality in >75 species), populations (e.g. changes in recruitment or abundance in >100 species, phenological change in >50 species), and species (e.g. >80 species newly arriving or disappearing), to communities and landscapes (e.g. >50 examples of altered ecological interactions). The overarching pattern indicated by the anecdotes suggests that people are more often noticing climate change losers (typically native species) than winners in their local areas, but with observations of potential ‘adaptation in action’ via compositional and phenological change and through arrivals and range shifts (particularly for native birds and exotic plants). A high proportion of climate change-related anecdotes also involved cumulative or interactive effects of land use. We conclude that targeted elicitation of local ecological knowledge about climate change impacts can provide a valuable complement to data-derived knowledge, substantially extending the volume of explicit examples and offering a foundation for further investigation.

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“…Global data repositories such as the Global Biodiversity Information Facility (GBIF) and increased efforts in data sharing and standardization further fuel the potential of these approaches (Jetz et al., 2019; Kissling et al, 2018). These advances allow for more cost‐effective and more spatio‐temporal representative monitoring of biodiversity change, providing data for increasingly realistic models that can generate future projections and support decision‐making (Ferrier et al, 2017). For instance, the Group on Earth Observations Biodiversity Observation Network (GEO BON) is developing open model‐based workflows for Essential Biodiversity Variables (H. M. Pereira et al., 2013), that is, a set of complementary measurements needed to detect and document biodiversity change across all levels of biodiversity across space and time (Fernández et al., 2020).…”

Section: Building On Scientific Advancesmentioning

confidence: 99%

Biodiversity post‐2020: Closing the gap between global targets and national‐level implementation

Perino

Pereira

Felipe‐Lucia

et al. 2021

CONSERVATION LETTERS

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National and local governments need to step up efforts to effectively implement the post‐2020 global biodiversity framework of the Convention on Biological Diversity to halt and reverse worsening biodiversity trends. Drawing on recent advances in interdisciplinary biodiversity science, we propose a framework for improved implementation by national and subnational governments. First, the identification of actions and the promotion of ownership across stakeholders need to recognize the multiple values of biodiversity and account for remote responsibility. Second, cross‐sectorial implementation and mainstreaming should adopt scalable and multifunctional ecosystem restoration approaches and target positive futures for nature and people. Third, assessment of progress and adaptive management can be informed by novel biodiversity monitoring and modeling approaches handling the multidimensionality of biodiversity change.

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Biodiversity Modelling as Part of an Observation System

Cited by 18 publications

References 69 publications

The effect of positional error on fine scale species distribution models increases for specialist species

The effect of positional error on fine scale species distribution models increases for specialist species

Recent climate-driven ecological change across a continent as perceived through local ecological knowledge

Biodiversity post‐2020: Closing the gap between global targets and national‐level implementation

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