How decisions about fitting species distribution models affect conservation outcomes

Species distribution models (SDMs) are increasingly used in conservation and land-use planning as inputs to describe biodiversity patterns. These models can be built in different ways, and decisions about data preparation, selection of predictor variables, model fitting, and evaluation all alter the resulting predictions. Commonly, the true distribution of species is unknown and independent data to verify which SDM variant to choose are lacking. Such model uncertainty is of concern to planners. We analyzed how 11 routine decisions about model complexity, predictors, bias treatment, and setting thresholds for predicted values altered conservation priority patterns across 25 species. Models were created with MaxEnt and run through Zonation to determine the priority rank of sites. Although all SDM variants performed well (area under the curve >0.7), they produced spatially different predictions for species and different conservation priority solutions. Priorities were most strongly altered by decisions to not address bias or to apply binary thresholds to predicted values; on average 40% and 35%, respectively, of all grid cells received an opposite priority ranking. Forcing high model complexity altered conservation solutions less than forcing simplicity (14% and 24% of cells with opposite rank values, respectively). Use of fewer species records to build models or choosing alternative bias treatments had intermediate effects (25% and 23%, respectively). Depending on modeling choices, priority areas overlapped as little as 10-20% with the baseline solution, affecting top and bottom priorities differently. Our results demonstrate the extent of model-based uncertainty and quantify the relative impacts of SDM building decisions. When it is uncertain what the best SDM approach and conservation plan is, solving uncertainty or considering alterative options is most important for those decisions that change plans the most.Peer reviewe

Effects of occurrence data density on conservation prioritization strategies

Place-prioritization analyses are a means by which researchers can translate information on the geographic distributions of species into quantitative prioritizations of areas for biodiversity conservation action. Although several robust algorithms are now available to support this sort of analysis, their vulnerability to biases deriving from incomplete and imbalanced distributional information is not well understood. In this contribution, we took a well-sampled group (i.e., Icteridae or New World blackbirds) in an intensively sampled region (the contiguous continental United States), and developed a set of pseudo-experimental manipulations of occurrence data density—in effect, we created situations in which data density was reduced 10- or 100-fold, and situations in which data density varied 100-fold from region to region. The effects were marked: priority areas for conservation shifted, appeared, and disappeared as a function of our manipulations. That is, differences in density of data can affect the position and complexity of areas of high conservation priority that are identified using distributional areas of species derived from ecological niche modeling. The effects of data density on prioritizations become more diffuse when considerations of existing protected areas and costs related to human intervention are taken into account, but changes are still manifested. Appropriate considerations of sampling density when constructing ecological niche models to identify distributional areas of species are key to preventing artifactual biases from entering into and affecting results of analyses of conservation priority

N‐SDM: a high‐performance computing pipeline for Nested Species Distribution Modelling

Predicting contemporary and future species distributions is relevant for science and decision making, yet the development of high‐resolution spatial predictions for numerous taxonomic groups and regions is limited by the scalability of available modelling tools. Uniting species distribution modelling (SDM) techniques into one high‐performance computing (HPC) pipeline, we developedN‐SDM, an SDM platform aimed at delivering reproducible outputs for standard biodiversity assessments.N‐SDMwas built around a spatially‐nested framework, intended at facilitating the combined use of species occurrence data retrieved from multiple sources and at various spatial scales.N‐SDMallows combining two models fitted with species and covariate data retrieved from global to regional scales, which is useful for addressing the issue of spatial niche truncation. The set of state‐of‐the‐art SDM features embodied inN‐SDMincludes a newly devised covariate selection procedure, five modelling algorithms, an algorithm‐specific hyperparameter grid search, and the ensemble of small‐models approach.N‐SDMis designed to be run on HPC environments, allowing the parallel processing of thousands of species at the same time. All the information required for installing and runningN‐SDMis openly available on the GitHub repositoryhttps://github.com/N‐SDM/N‐SDM

N‐SDM: a high‐performance computing pipeline for Nested Species Distribution Modelling

Predicting contemporary and future species distributions is relevant for science and decision making, yet the development of high-resolution spatial predictions for numerous taxonomic groups and regions is limited by the scalability of available modelling tools. Uniting species distribution modelling (SDM) techniques into one high-performance computing (HPC) pipeline, we developed N-SDM, an SDM platform aimed at delivering reproducible outputs for standard biodiversity assessments. N-SDM was built around a spatially-nested framework, intended at facilitating the combined use of species occurrence data retrieved from multiple sources and at various spatial scales. N-SDM allows combining two models fitted with species and covariate data retrieved from global to regional scales, which is useful for addressing the issue of spatial niche truncation. The set of state-of-the-art SDM features embodied in N-SDM includes a newly devised covariate selection procedure, five modelling algorithms, an algorithm-specific hyperparameter grid search, and the ensemble of small-models approach. N-SDM is designed to be run on HPC environments, allowing the parallel processing of thousands of species at the same time. All the information required for installing and running N-SDM is openly available on the GitHub repository https://github.com/N-SDM/N-SDM

Guide francophone pour la modélisation de niches écologiques

(english) Correlational ecological niche modeling (ENM) is a popular group of methods in the field of distributional ecology and is employed for a variety of applications. Although the conceptual and methodological framework of ENM has been widely described in the literature, there is still no exhaustive synthesis of it in the French language. In this article, theoretical bases of ENM are exposed through a history of the concept of ecological niche as well as its implications for the study of species macroscale distributions. Then, the different steps of ENM are described, emphasizing on the importance of controlling the quality of input data. Various recommendations concerning algorithm choice, model calibration and evaluation as well post-modeling analyses, such as niche comparison and transfer to other periods/regions, are presented. Particular emphasis is placed on 1/ the operation of Maxent and the need for parameter tuning prior modeling, 2/ the importance of the choice of the M calibration area, 3/ the need to take into account accessible environments (M) for model transfer and comparison, and 4/ the importance of evaluating and presenting the variability of models resulting from methodological choices at different stages (occurrence data partitioning, choice of a climate model, choice of algorithm, choice of the calibration area, etc.). To conclude, contextualizing any ENM study in a clear and explicit theoretical and methodological framework is paramount to ensure the pertinence of subsequent interpretations. Key-words: ecological niche modeling ; good practices ; conceptual framework ; model calibration and evaluation ; model transfer ; model comparison (french) La modélisation corrélationnelle de niches écologiques (ENM) est un ensemble de méthodes populaire dans le champ de l’écologie de la distribution d’espèces et est employée pour une multitude d’applications. Si le cadre conceptuel et méthodologique de l’ENM a été largement décrit dans la littérature, il n’existe pas de synthèse exhaustive en langue française. Dans cet article, nous exposons les bases théoriques de l’ENM à travers un historique du concept de niche écologique et ses implications pour l’étude de la distribution macro-géographique des espèces. Nous décrivons ensuite les différentes étapes d’une étude ENM, en insistant tout d’abord sur l’importance de contrôler la qualité des données d’entrées. Différentes préconisations concernant le choix des algorithmes, la calibration et l’évaluation des modèles ainsi que les analyses postérieures, telles que les comparaisons de niches ou le transfert à d’autres périodes/régions, sont présentées. Nous insistons en particulier sur 1/ le fonctionnement de l’algorithme Maxent et la nécessité d’un processus de réglage de ses paramètres, 2/ l’importance du choix de l’aire de calibration M, 3/ la nécessité de prendre en compte les environnements accessibles (M) dans le transfert et la comparaison des modèles, et 4/ l’importance d’évaluer et de présenter la variabilité des résultats en fonction de choix méthodologiques à différentes étapes (partitionnement des données d’occurrences, choix d’un modèle climatique, choix de l’algorithme, choix de l’aire de calibration, etc.). En conclusion, nous rappelons l’importance d’ancrer toute étude employant l’ENM dans un cadre théorique et méthodologique clair et explicite afin de garantir la pertinence des interprétations ultérieures. Mots-clés : modélisation de niches écologiques ; bonnes pratiques ; cadre conceptuel ; calibration et évaluation des modèles ; transfert de modèles ; comparaison de modèle

Modelagem de nicho e mudanças climáticas : perspectivas para abelhas e seus grupos funcionais

Orientador: Prof. Dr. Rodrigo B. GonçalvesCoorientador: Prof. Dr. Mauricio O. MouraDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Biológicas, Programa de Pós-Graduação em Ciências Biológicas (Entomologia). Defesa : Curitiba, 25/08/2023Inclui referênciasÁrea de concentração: EntomologiaResumo: A iminência das alterações desencadeadas pelas mudanças climáticas antropogênicas ameaça a biodiversidade como um todo, com perspectivas preocupantes para as próximas décadas. A preocupação se estende para as abelhas, grupo reconhecido pelo protagonismo na polinização em ambientes naturais e agrícolas. O presente trabalho teve como objetivo investigar os possíveis impactos das mudanças climáticas na distribuição de abelhas, em especial o deslocamento para maiores latitudes ao sul, tendo como enfoque uma das regiões de maior diversidade de abelhas no mundo - sul e sudeste da América do Sul. Para isso, utilizou-se modelagem de nicho ecológico para acessar as áreas de adequabilidade no presente e em dois cenários de mudanças climáticas (um otimista e um pessimista) para 2050. Foram selecionadas 20 espécies (19 gêneros) de diferentes grupos taxonômicos e funcionais, com representantes das cinco subfamílias de abelhas que ocorrem no continente. Para seis dos gêneros selecionados, foram adicionadas outras seis espécies, porém com distribuição mais ao norte, para comparações par-a-par quanto às mudanças de distribuição e sobreposição geográfica entre pares de espécies evolutivamente próximas. Para as 20 espécies da região de interesse, foram quantificadas as áreas de estabilidade, perdas e ganhos de adequabilidade nos cenários futuros. As mudanças nas áreas de distribuição foram classificadas em (i) deslocada. (ii) reduzida, (iii) expandida e (iv) inalterada. No geral, os modelos apontam para predominância nas áreas estáveis de adequabilidade (~60%), entretanto com valores bastante expressivos de perdas (~20%) e um pouco menores de ganhos (~16%) de adequabilidade. O deslocamento das áreas de distribuição foi a resposta mais frequente para as espécies (35%), seguidas por reduções (30- 35%) e expansões (25%) das áreas de adequabilidade. Além disso, os resultados indicam perdas drásticas de riqueza no sul da América do Sul nos cenários futuros. Quanto aos grupos funcionais, foi possível identificar maiores suscetibilidades à perda de adequabilidade para as espécies solitárias, as com nidificação em solo e as especialistas em recursos florais. Por outro lado, espécies eussociais, com nidificação acima do solo e generalistas demonstraram-se menos sensíveis à perda de adequabilidade, inclusive com maiores propensões a deslocamento ou expansão nas áreas de distribuição. Em relação aos pares de espécies de mesmo gênero, são preditas reduções nas áreas de coocorrências no futuro, com tendência a deslocamento da sobreposição em direção ao sul. Para cinco dos seis gêneros (Augochlora, Bombus, Paroxystoglossa, Tetraglossula e Thectochlora), as espécies distribuídas mais ao norte demonstraram-se mais sensíveis a perdas de adequabilidade do que as distribuídas ao sul. Diferentemente, para Rhinocorynura foi apontada a expansão da espécie ocorrendo mais ao norte e redução da espécie com distribuição ao sul. Por fim, destaca-se o interesse em considerar espécies de diferentes grupos taxonômicos e funcionais em esforços de mensurar impactos das mudanças climáticas na distribuição de espécies, de forma a melhor avaliar as diferentes respostas potenciais e dar suporte a tomadas de decisão robustas para mitigar os impactos na biodiversidade.Abstract: The imminence of the impacts of anthropogenic climate change threatens biodiversity as a whole, with worrying prospects for the coming decades. The concern extends to the bees, a group recognized for their role in pollination in natural and agricultural environments. The main aim of this study was to investigate the potential impacts of climate change on the distribution of bees, especially the range-shift to the south, focusing on one of the most diverse regions in the world - South and Southeast South America. For this purpose, ecological niche modeling (ENM) has been used to access the areas of suitability in the present and in two climate change scenarios (one optimistic and one pessimistic) for 2050. A sample of 20 bee species (19 genera) was selected from different taxonomic and functional groups, with representatives of the five subfamilies that occur on the continent. For six selected genera, another six close-related species (of the same genus, but with a more northern distribution) were selected for pairwise comparisons of changes in distribution and geographical overlapping. For the 20 southern species, the projections were quantified in terms of suitable stability, losses and gains of suitability. Also, the projections were classified into range-shift classes: (i) displaced. (ii) reduced, (iii) expanded and (iv) unchanged. Overall, the models point to predominance in suitable stable areas (~60%), however with rather expressive values of losses (~20%) and slightly lower gains (~16%). Displaced distributions were the most effect for the species in the future scenarios (35%), followed by reduced (30-35%) and expanded (25%) distributions. In addition, the models indicate drastic richness declines in the region in climate change scenarios. For the functional groups, it was possible to identify higher susceptibility to losses of suitable areas for the solitary species, those with below ground nesting and the floral resources specialists. On the other hand, eusocial species, those with above ground nesting and generalists tended to be less susceptible, with more frequent tendencies to displacement or expansion of distribution range. The models predicted reductions in the overlapping areas of species from the same genus, with a tendency to shift the overlapping areas towards the south. For five of the six genera (Augochlora, Bombus, Paroxystoglossa, Tetraglossula, and Thectochlora), species with more Northern distribution tend to be more susceptible to loss of suitability than those with Southern distribution. In contrast, for Rhinocorynura was predicted the expansion of the northern species and reduction of the southern species. Finally, it is highlighted the importance of considering species from different taxonomic and functional groups in efforts to measure impacts of climate change on species distribution in order to better evaluate the different potential responses and provide support for robust decision-making to mitigate impacts on biodiversity