scholarly journals Bioacoustics for in situ validation of species distribution modelling: An example with bats in Brazil

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0248797
Author(s):  
Frederico Hintze ◽  
Ricardo B. Machado ◽  
Enrico Bernard
Keyword(s):  
Species Distribution ◽  
Large Scale ◽  
Species Distribution Modelling ◽  
Evaluation Metrics ◽  
Sampling Effort ◽  
International Treaties ◽  
Theoretical Evaluation ◽  
Distribution Modelling ◽  
Independent Field

Species distribution modelling (SDM) gained importance on biodiversity distribution and conservation studies worldwide, including prioritizing areas for public policies and international treaties. Useful for large-scale approaches and species distribution estimates, it is a plus considering that a minor fraction of the planet is adequately sampled. However, minimizing errors is challenging, but essential, considering the uses and consequences of such models. In situ validation of the SDM outputs should be a key-step—in some cases, urgent. Bioacoustics can be used to validate and refine those outputs, especially if the focal species’ vocalizations are conspicuous and species-specific. This is the case of echolocating bats. Here, we used extensive acoustic monitoring (>120 validation points over an area of >758,000 km2, and producing >300,000 sound files) to validate MaxEnt outputs for six neotropical bat species in a poorly-sampled region of Brazil. Based on in situ validation, we evaluated four threshold-dependent theoretical evaluation metrics’ ability in predicting models’ performance. We also assessed the performance of three widely used thresholds to convert continuous SDMs into presence/absence maps. We demonstrated that MaxEnt produces very different outputs, requiring a careful choice on thresholds and modeling parameters. Although all theoretical evaluation metrics studied were positively correlated with accuracy, we empirically demonstrated that metrics based on specificity-sensitivity and sensitivity-precision are better for testing models, considering that most SDMs are based on unbalanced data. Without independent field validation, we found that using an arbitrary threshold for modelling can be a precarious approach with many possible outcomes, even after getting good evaluation scores. Bioacoustics proved to be important for validating SDMs for the six bat species analyzed, allowing a better refinement of SDMs in large and under-sampled regions, with relatively low sampling effort. Regardless of the species assessing method used, our research highlighted the vital necessity of in situ validation for SDMs.

scholarly journals Bioacoustics for in situ validation of species distribution modelling: An example with bats in Brazil

2021 ◽  
Author(s):  
Frederico Hintze ◽  
Ricardo B. Machado ◽  
Enrico Bernard
Keyword(s):  
Species Distribution ◽  
Large Scale ◽  
Species Distribution Modelling ◽  
Evaluation Metrics ◽  
Sampling Effort ◽  
International Treaties ◽  
Theoretical Evaluation ◽  
Distribution Modelling ◽  
Independent Field

AbstractSpecies distribution modelling (SDM) gained importance on biodiversity distribution and conservation studies worldwide, including prioritizing areas for public policies and international treaties. Useful for large-scale approaches and estimates, is a plus considering that a minor fraction of the planet is adequately sampled. However, SDM needs to be as reliable as possible. Minimizing errors is challenging, but essential, considering the uses and consequences of such models. In situ validation of the SDM outputs should be a key-step – in some cases, urgent. Bioacoustics can be used to validate and refine those outputs, especially if the focal species’ vocalizations are conspicuous and species-specific. This is the case of echolocating bats. Here, we used extensive acoustic monitoring (>120 validation points, covering >758,000 km2, and >300,000 sound files) to validate MaxEnt outputs for six neotropical bat species in a poorly-sampled region of Brazil. Based on in situ validation, we evaluated four threshold-dependent theoretical evaluation metrics’ ability in predicting models’ performance. We also assessed the performance of three widely used thresholds to convert continuous SDMs into presence/absence maps. We demonstrated that MaxEnt produces very different outputs, requiring a careful choice on thresholds and modeling parameters. Although all theoretical evaluation metrics studied were positively correlated with accuracy, we empirically demonstrated that metrics based on specificity-sensitivity and sensitivity-precision are better for testing models, considering that most SDMs are based on unbalanced data. Without independent field validation, we found that using an arbitrary threshold for modelling can be a precarious approach with many possible outcomes, even after getting good evaluation scores. Bioacoustics proved to be important for validating SDMs for the six bat species analyzed, allowing a better refinement of SDMs in large and under-sampled regions, with relatively low sampling effort. Regardless of species assessing method used, our research highlighted the vital necessity of in situ validation for SDMs.

scholarly journals Species Distribution Modelling Using Deep Learning

2019 ◽  
Vol 3 ◽  
Author(s):  
Rutger Vos ◽  
Mark Rademaker ◽  
Laurens Hogeweg
Keyword(s):  
Deep Learning ◽  
Species Distribution ◽  
Large Scale ◽  
Species Distribution Modelling ◽  
Climate Change Scenarios ◽  
Niche Modelling ◽  
Distribution Modelling ◽  
Biodiversity Science ◽  
Learning Techniques ◽  
Correlative Models

Species distribution modelling, or ecological niche modelling, is a collection of techniques for the construction of correlative models based on the combination of species occurrences and GIS data. Using such models, a variety of research questions in biodiversity science can be investigated, among which are the assessment of habitat suitability around the globe (e.g. in the case of invasive species), the response of species to alternative climatic regimes (e.g. by forecasting climate change scenarios, or by hindcasting into palaeoclimates), and the overlap of species in niche space. The algorithms used for the construction of such models include maximum entropy, neural networks, and random forests. Recent advances both in computing power and in algorithm development raise the possibility that deep learning techniques will provide valuable additions to these existing approaches. Here, we present our recent findings in the development of workflows to apply deep learning to species distribution modelling, and discuss the prospects for the large-scale application of deep learning in web service infrastructures to analyze the growing corpus of species occurrence data in biodiversity information facilities.

scholarly journals EXPLICITLY ACCOUNTING FOR UNCERTAINTY IN CROWDSOURCED DATA FOR SPECIES DISTRIBUTION MODELLING

2015 ◽  
Vol II-3/W5 ◽  
pp. 333-337 ◽  
Author(s):  
D. Rocchini ◽  
A. Comber ◽  
C. X. Garzon-Lopez ◽  
M. Neteler ◽  
A. M. Barbosa ◽  
...  
Keyword(s):  
Species Distribution ◽  
Statistical Modelling ◽  
Species Distribution Modelling ◽  
Sampling Effort ◽  
Data Sampling ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Crowdsourced Data ◽  
Important Approach ◽  
Modelling Techniques

Species distribution models represent an important approach to map the spread of plant and animal species over space (and time). As all the statistical modelling techniques related to data from the field, they are prone to uncertainty. In this study we explicitly dealt with uncertainty deriving from field data sampling; in particular we propose i) methods to map sampling effort bias and ii) methods to map semantic bias.

scholarly journals Using species distribution modelling to determine opportunities for trophic rewilding under future scenarios of climate change

2018 ◽  
Vol 373 (1761) ◽  
pp. 20170446 ◽  
Author(s):  
Scott Jarvie ◽  
Jens-Christian Svenning
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Species Distribution Modelling ◽  
Climatic Conditions ◽  
First Century ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Major Barrier ◽  
Methodological Choices ◽  
Biodiversity And Ecosystem Function

Trophic rewilding, the (re)introduction of species to promote self-regulating biodiverse ecosystems, is a future-oriented approach to ecological restoration. In the twenty-first century and beyond, human-mediated climate change looms as a major threat to global biodiversity and ecosystem function. A critical aspect in planning trophic rewilding projects is the selection of suitable sites that match the needs of the focal species under both current and future climates. Species distribution models (SDMs) are currently the main tools to derive spatially explicit predictions of environmental suitability for species, but the extent of their adoption for trophic rewilding projects has been limited. Here, we provide an overview of applications of SDMs to trophic rewilding projects, outline methodological choices and issues, and provide a synthesis and outlook. We then predict the potential distribution of 17 large-bodied taxa proposed as trophic rewilding candidates and which represent different continents and habitats. We identified widespread climatic suitability for these species in the discussed (re)introduction regions under current climates. Climatic conditions generally remain suitable in the future, although some species will experience reduced suitability in parts of these regions. We conclude that climate change is not a major barrier to trophic rewilding as currently discussed in the literature.This article is part of the theme issue ‘Trophic rewilding: consequences for ecosystems under global change’.

Sign in / Sign up

Export Citation Format

Share Document