Hierarchical species distribution models in support of vegetation conservation at the landscape scale

Mateo, Rubén G.; Gastón, Aitor; Aroca‐Fernández, María José; Broennimann, Olivier; Guisan, Antoine; Saura, Santiago; García-Viñas, Juan I.

doi:10.1111/jvs.12726

Cited by 39 publications

(41 citation statements)

References 77 publications

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“…The evaluation presented here is meant to stimulate further methodological and empirical research to better predict biodiversity at different spatial and temporal scales and levels of organization. A promising approach for this purpose is the hierarchical modeling of species distributions (Mateo et al, 2019;Petitpierre et al, 2016). H-SDMs allow the simultaneous modeling spatial patterns of biodiversity at ecological and regional scales.…”

Section: Discussionmentioning

confidence: 99%

Predicting species distributions and community composition using remote sensing products

Pinto‐Ledezma

Cavender‐Bares

2020

Preprint

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AbstractAccurate predictions of species composition and diversity are critical to the development of conservation actions and management strategies. In this paper using oak assemblages distributed across the conterminous United States as study model, we assessed the performance of stacked species distribution models (S-SDMs) and remote sensing products in building the next-generation of biodiversity models. This study represents the first attempt to evaluate the integrated predictions of biodiversity models—including assemblage diversity and composition—obtained by stacking next-generation SDMs. We found three main results. First, environmental predictors derived entirely from remote sensing products represent adequate covariates for biodiversity modeling. Second, applying constraints to assemblage predictions, such as imposing the probability ranking rule, results in more accurate species diversity predictions. Third, independent of the stacking procedure (bS-SDM versus cS-SDM), biodiversity models do not recover the observed species composition with high spatial resolution, i.e., correct species identities at the scale of individual plots. However, they do return reasonable predictions at macroecological scales (1 km). Our results provide insights for the prediction of assemblage diversity and composition at different spatial scales. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models to accurately predict and monitor ecological assemblages through time and space.

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Section: Discussionmentioning

confidence: 99%

Predicting species distributions and community composition using remote sensing products

Pinto‐Ledezma

Cavender‐Bares

2020

Preprint

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“…Our use of pseudo-optimised scale selection (sensu McGarigal et al 2016), whereby univariate models are used to identify the scale at which a predictor best fits the response data at each level, improves upon previous multi-level studies that focus on a single scale at each level (e.g. Pearson et al 2004;Mateo et al 2019b). However, the number of scales tested at each level was limited to two and testing for scale effects in isolation (disregarding covariates and potential interactions) can inflate residual variance and alter scale selection (Bradter et al 2013;Spake et al 2019).…”

Section: Future Framework Refinementsmentioning

confidence: 99%

“…Distributions at higher levels are shaped by factors that vary slowly across space, such as the influence of climate on population ranges; more local scale, patchy predictors influencing a species' mobility, resource distribution or biotic interactions are important at lower levels (Pearson and Dawson 2003;Pearson et al 2004;Vicente et al 2014;Razgour et al 2014). HSMs that integrate drivers at their scale of effect across multiple levels, and that encompass the full range of conditions experienced across the population range, provide a more complete characterisation of a species' niche and prevent truncation of modelled speciesenvironment relationships (Barbet-Massin et al 2010;DeCesare et al 2012;Fournier et al 2017;Heisler et al 2017;Bauder et al 2018;Mateo et al 2019b).…”

Section: Introductionmentioning

confidence: 99%

“…We hypothesised that, by integrating information on drivers operating across all levels, the multi-level approaches would provide more accurate predictions compared to single-level models (Pearson et al 2004;Mateo et al 2019b). Moreover, by allowing a species' response to features of the lower level, local environment to vary according to wider, regional conditions set at higher levels, we expected that our sequential multi-level framework would provide more realistic and nuanced information on species-environment relationships and higher predictive accuracy compared to the post-hoc combinations of single-level models.…”

Section: Introductionmentioning

confidence: 99%

“…Enabling these 'cross-scale' interactions should help to account for local adaptation and improve our ability to provide effective recommendations (Swihart et al 2003;Whittingham et al 2007;Lindenmayer et al 2008;Scholes et al 2013;Oliver and Morecroft 2014;Spake et al 2019). Our methods build on the sequential multi-level models trialled by Pearson et al (2004) and Mateo et al (2019b) by incorporating multiple scales at each level; additionally we outline steps for predictor and scale selection at each level, as recommended by McGarigal et al (2016).…”

Section: Introductionmentioning

confidence: 99%

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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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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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Hierarchical species distribution models in support of vegetation conservation at the landscape scale

Cited by 39 publications

References 77 publications

Predicting species distributions and community composition using remote sensing products

Predicting species distributions and community composition using remote sensing products

A sequential multi-level framework to improve habitat suitability modelling

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

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