Assessing the uncertainty arising from standard land‐cover mapping procedures when modelling species distributions

Cánibe, Miguel; Titeux, Nicolas; Domínguez, Jesús; Regos, Adrián

doi:10.1111/ddi.13456

Cited by 9 publications

(3 citation statements)

References 78 publications

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“…First, many of the pre-processing steps required to create LULC maps (e.g., geometric, radiometric, solar, and atmospheric corrections and orthometric rectification applied to satellite imagery followed by supervised or unsupervised classification) [46][47][48][49] add their own type and level of variability and error that impact the qualitative and quantitative value of the final product(s) [50]. This can impact their suitability for use in SDM-type applications [51]. Second, LULC maps are invariably products of how humans see the world, and evaluating predictor variables less affected by this bias was of great interest to us [52].…”

Section: Plos Onementioning

confidence: 99%

Modelling Red–Crowned Parrot (Psittaciformes: Amazona viridigenalis [Cassin, 1853]) distributions in the Rio Grande Valley of Texas using elevation and vegetation indices and their derivatives

Voltura,

Tracy,

Heatley

et al. 2023

PLoS ONE

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Texas Rio Grande Valley Red–crowned Parrots (Psittaciformes: Amazona viridigenalis [Cassin, 1853]) primarily occupy vegetated urban rather than natural areas. We investigated the utility of raw vegetation indices and their derivatives as well as elevation in modelling the Red–crowned parrot’s general use, nest site, and roost site habitat distributions. A feature selection algorithm was employed to create and select an ensemble of fine–scale, top–ranked MaxEnt models from optimally–sized, decorrelated subsets of four to seven of 199 potential variables. Variables were ranked post hoc by frequency of appearance and mean permutation importance in top–ranked models. Our ensemble models accurately predicted the three distributions of interest (x¯ Area Under the Curve [AUC] = 0.904–0.969). Top–ranked variables for different habitat distribution models included: (a) general use–percent cover of preferred ranges of entropy texture of Normalized Difference Vegetation Index (NDVI) values, entropy and contrast textures of NDVI, and elevation; (b) nest site–entropy textures of NDVI and Green–Blue NDVI, and percent cover of preferred range of entropy texture of NDVI values; (c) roost site–percent cover of preferred ranges of entropy texture of NDVI values, contrast texture of NDVI, and entropy texture of Green–Red Normalized Difference Index. Texas Rio Grande Valley Red–crowned Parrot presence was associated with urban areas with high heterogeneity and randomness in the distribution of vegetation and/or its characteristics (e.g., arrangement, type, structure). Maintaining existing preferred vegetation types and incorporating them into new developments should support the persistence of Red–crowned Parrots in southern Texas.

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Section: Plos Onementioning

confidence: 99%

Modelling Red–Crowned Parrot (Psittaciformes: Amazona viridigenalis [Cassin, 1853]) distributions in the Rio Grande Valley of Texas using elevation and vegetation indices and their derivatives

Voltura,

Tracy,

Heatley

et al. 2023

PLoS ONE

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“…These compositional variables are often used as covariates (also known as independent, explanatory, or predictor variables) when attempting to understand different types of phenomena. For example, LULC change is the major driver of biodiversity decline and ecosystem integrity loss throughout the world (Díaz et al, 2019; Tilman et al, 2017) and, as a result, there has been considerable interest in understanding how the surrounding LULC influences a wide range of phenomena, such as animal behavior (e.g., Gallo et al, 2022; Giroux et al, 2023; Noonan et al, 2022; Paviolo et al, 2018; Zeller et al, 2016), occurrence/abundance of species (e.g., Canibe et al, 2022; Fink et al, 2020; Miller et al, 2019; Valle et al, 2022), water quality and pollutant concentration (Hoek et al, 2008; Piffer et al, 2021), perception of environmental problems (Suchy et al, 2023), and disease incidence (e.g., Machado et al, 2023; Valle & Tucker Lima, 2014).…”

Section: Introductionmentioning

confidence: 99%

Estimation and interpretation problems and solutions when using proportion covariates in linear regression models

Valle,

Mintz,

Brack

2024

Ecology

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Proportion variables, also known as compositional data, are very common in ecology. Unfortunately, few scientists are aware of how compositional data, when used as covariates, can adversely impact statistical analysis. We describe here how proportion covariates result in multicollinearity and parameter identifiability problems. Using simulated data on bird species richness as a function of land use, we show how these problems manifest when fitting a wide range of models in R, both in a frequentist and Bayesian framework. In particular, we show that similar models can often generate substantially different parameter estimates, leading to very different conclusions. Dropping a covariate or the intercept from the model can solve the multicollinearity and parameter identifiability problems. Unfortunately, these solutions do not fix the inherent challenges associated with interpreting parameter estimates. To this end, we propose focusing the interpretation on the difference of slope parameters to avoid the inherent unidentifiability of individual parameters. We also propose conditional plots with two x‐axes and marginal plots as visualization techniques that can help users better interpret their modeling results. We illustrate these problems and proposed solutions using empirical data from the North American Breeding Bird Survey. The practical and straightforward approaches suggested in this article will help the fitting of linear models and interpretation of its results when some of the covariates are proportions.

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“…However, the land use classes are only represented at a relatively coarse level of detail (e.g., three classes for forest). As the assumption behind HSM requires that environmental variables reflect the ecological niche of the target species (Araújo & Guisan, 2006 ), this level of detail is likely to be insufficient for precise modeling of habitat suitability for most species (e.g., Bradley & Fleishman, 2008 ; Cánibe et al., 2022 ; Gottwald et al., 2017 ; Mortelliti et al., 2007 ). Hence, HSM might require the utilization of more targeted classes in contrast to relying on widely available and therefore commonly utilized large‐scale land cover datasets.…”

Section: Introductionmentioning

confidence: 99%

The devil is in the detail: Environmental variables frequently used for habitat suitability modeling lack information for forest‐dwelling bats in Germany

Bald,

Gottwald,

Hillen

et al. 2024

Ecology and Evolution

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In response to the pressing challenges of the ongoing biodiversity crisis, the protection of endangered species and their habitats, as well as the monitoring of invasive species are crucial. Habitat suitability modeling (HSM) is often treated as the silver bullet to address these challenges, commonly relying on generic variables sourced from widely available datasets. However, for species with high habitat requirements, or for modeling the suitability of habitats within the geographic range of a species, variables at a coarse level of detail may fall short. Consequently, there is potential value in considering the incorporation of more targeted data, which may extend beyond readily available land cover and climate datasets. In this study, we investigate the impact of incorporating targeted land cover variables (specifically tree species composition) and vertical structure information (derived from LiDAR data) on HSM outcomes for three forest specialist bat species (Barbastella barbastellus, Myotis bechsteinii, and Plecotus auritus) in Rhineland‐Palatinate, Germany, compared to commonly utilized environmental variables, such as generic land‐cover classifications (e.g., Corine Land Cover) and climate variables (e.g., Bioclim). The integration of targeted variables enhanced the performance of habitat suitability models for all three bat species. Furthermore, our results showed a high difference in the distribution maps that resulted from using different levels of detail in environmental variables. This underscores the importance of making the effort to generate the appropriate variables, rather than simply relying on commonly used ones, and the necessity of exercising caution when using habitat models as a tool to inform conservation strategies and spatial planning efforts.

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Assessing the uncertainty arising from standard land‐cover mapping procedures when modelling species distributions

Abstract: This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 9 publications

References 78 publications

Modelling Red–Crowned Parrot (Psittaciformes: Amazona viridigenalis [Cassin, 1853]) distributions in the Rio Grande Valley of Texas using elevation and vegetation indices and their derivatives

Modelling Red–Crowned Parrot (Psittaciformes: Amazona viridigenalis [Cassin, 1853]) distributions in the Rio Grande Valley of Texas using elevation and vegetation indices and their derivatives

Estimation and interpretation problems and solutions when using proportion covariates in linear regression models

The devil is in the detail: Environmental variables frequently used for habitat suitability modeling lack information for forest‐dwelling bats in Germany

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