Looking for an optimal hierarchical approach for ecologically meaningful niche modelling

Mateo, Rubén G.; Aroca‐Fernández, María José; Gastón, Aitor; Gómez‐Rubio, Virgilio; Saura, Santiago; García-Viñas, Juan I.

doi:10.1016/j.ecolmodel.2019.108735

Cited by 21 publications

(25 citation statements)

References 75 publications

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“…2019, Mateo et al. 2019 a ). For instance, the simplest data integration method used here (data pooling; M2) presented a lower performance than the reference model (M1) at most truncation levels, despite being one of the most commonly used (Fletcher et al.…”

Section: Discussionmentioning

confidence: 99%

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Data integration methods to account for spatial niche truncation effects in regional projections of species distribution

Chevalier

Broennimann

Cornuault³

et al. 2021

Ecological Applications

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Many species distribution models (SDMs) are built with precise but geographically restricted presence–absence data sets (e.g., a country) where only a subset of the environmental conditions experienced by a species across its range is considered (i.e., spatial niche truncation). This type of truncation is worrisome because it can lead to incorrect predictions e.g., when projecting to future climatic conditions belonging to the species niche but unavailable in the calibration area. Data from citizen‐science programs, species range maps or atlases covering the full species range can be used to capture those parts of the species’ niche that are missing regionally. However, these data usually are too coarse or too biased to support regional management. Here, we aim to (1) demonstrate how varying degrees of spatial niche truncation affect SDMs projections when calibrated with climatically truncated regional data sets and (2) test the performance of different methods to harness information from larger‐scale data sets presenting different spatial resolutions to solve the spatial niche truncation problem. We used simulations to compare the performance of the different methods, and applied them to a real data set to predict the future distribution of a plant species (Potentilla aurea) in Switzerland. SDMs calibrated with geographically restricted data sets expectedly provided biased predictions when projected outside the calibration area or time period. Approaches integrating information from larger‐scale data sets using hierarchical data integration methods usually reduced this bias. However, their performance varied depending on the level of spatial niche truncation and how data were combined. Interestingly, while some methods (e.g., data pooling, downscaling) performed well on both simulated and real data, others (e.g., those based on a Poisson point process) performed better on real data, indicating a dependency of model performance on the simulation process (e.g., shape of simulated response curves). Based on our results, we recommend to use different data integration methods and, whenever possible, to make a choice depending on model performance. In any case, an ensemble modeling approach can be used to account for uncertainty in how niche truncation is accounted for and identify areas where similarities/dissimilarities exist across methods.

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Section: Discussionmentioning

confidence: 99%

“…2017, Mateo et al. 2019 a ), as long as they are adequate for the data, and to evaluate their performance using appropriate measures (e.g., RMSE, TSS). If a model presents a much higher performance than others do, this may be the preferred choice.…”

Section: Discussionmentioning

confidence: 99%

Data integration methods to account for spatial niche truncation effects in regional projections of species distribution

Chevalier

Broennimann

Cornuault³

et al. 2021

Ecological Applications

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“…Global data and models, however, can still be used in combination with local, regional or national models in a hierarchical way to account for the complete species climatic niche (Mateo, Aroca‐Fernández, et al., 2019; Mateo, Gastón, et al., 2019). This approach may pose technical challenges for studies modelling local species distributions at very high spatial resolutions.…”

Section: Discussionmentioning

confidence: 99%

National assessments of species vulnerability to climate change strongly depend on selected data sources

Scherrer

Esperón-Rodríguez

Beaumont

et al. 2021

Diversity and Distributions

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“…Spake et al 2019), or recently developed Bayesian approaches (e.g. Stuber et al 2017, particularly when sample sizes are low (Mateo et al 2019a)). Other areas for potential framework validation and refinement include the application of a simulation experiment with wellknown correlation structures to better test the framework's robustness (Meynard et al 2019) and exploring the application of statistical approaches to classify predictors into levels (e.g.…”

Section: Future Framework Refinementsmentioning

confidence: 99%

A sequential multi-level framework to improve habitat suitability modelling

et al. 2020

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Context Habitat suitability models (HSM) can improve our understanding of a species' ecology and are valuable tools for informing landscape-scale decisions. We can increase HSM predictive accuracy and derive more realistic conclusions by taking a multi-scale approach. However, this process is often statistically complex and computationally intensive. Objectives We provide an easily implemented, flexible framework for sequential multi-level, multi-scale HSM and compare it to two other commonly-applied approaches: single-level, multi-scale HSM and their post-hoc combinations. Methods Our framework implements scale optimisation and model tuning at each level in turn, from the highest (population range) to the lowest (e.g. foraging habitat) level, whilst incorporating output habitat suitability indices from a higher level as a predictor. We used MaxEnt and a species of conservation concern in Britain, the lesser horseshoe bat (Rhinolophus hipposideros), to demonstrate and compare multi-scale approaches. Results Integrating models across levels, either by applying our framework, or by multiplying singlelevel model predictions, improved predictive performance over single-level models. Moreover, differences in the importance and direction of the speciesenvironment associations highlight the potential for false inferences from single-level models or their posthoc combinations. The single-level summer range model incorrectly identified a positive influence of heathland cover, whereas sequential multi-level models made biological sense and underlined this species' requirement for extensive broadleaf woodland cover, hedgerows and access to buildings for roosting in rural areas. Conclusions We conclude that multi-level HSM appear superior to single-level, multi-scale approaches; models should be sequentially integrated across levels if information on species-environment relationships is of importance.

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Looking for an optimal hierarchical approach for ecologically meaningful niche modelling

Cited by 21 publications

References 75 publications

Data integration methods to account for spatial niche truncation effects in regional projections of species distribution

Data integration methods to account for spatial niche truncation effects in regional projections of species distribution

National assessments of species vulnerability to climate change strongly depend on selected data sources

A sequential multi-level framework to improve habitat suitability modelling

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