Accounting for niche truncation to improve spatial and temporal predictions of species distributions

Frontiers in Ecology and Evolution, Journal Year: 2022, Volume and Issue: 10

Published: Aug. 4, 2022

Species Distribution Models (SDMs) are essential tools for predicting climate change impact on species’ distributions and commonly employed as an informative tool which to base management conservation actions. Focusing only a part of the entire distribution species fitting SDMs is common approach. Yet, geographically restricting their range can result in considering subset ecological niche (i.e., truncation) could lead biased spatial predictions future effects, particularly if conditions belong those parts that have been excluded model fitting. The integration large-scale data encompassing whole with more regional improve but comes along challenges owing broader scale and/or lower quality usually associated these data. Here, we compare obtained from traditional SDM fitted dataset (Switzerland) methods combine European datasets several bird breeding Switzerland. Three models were fitted: based thus not accounting truncation, pooling where two merged without differences extent or resolution, downscaling hierarchical approach accounts resolution. Results show leads much larger predicted changes (either positively negatively) under than both methods. also identified different variables main drivers compared data-integration models. Differences between regarding outside existing when implied extrapolation). In conclusion, showed (i) calibrated restricted provide markedly (ii) at least partly explained by truncation. This suggests using accurate nuanced through better characterization realized niches.

Language: Английский

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Population density estimates for terrestrial mammal species

Global Ecology and Biogeography, Journal Year: 2022, Volume and Issue: 31(5), P. 978 - 994

Published: March 8, 2022

Abstract Aim Population density is a key parameter in ecology and conservation, estimates of population are required for wide variety applications fundamental applied ecology. Yet, terrestrial mammals these data available only minority species, their availability taxonomically geographically biased. Here, we provide the most plausible predictions average density, natural variability statistical uncertainty 4,925 mammal species. Location Global. Time period 1970–2021. Major taxa studied Terrestrial mammals. Methods We fitted an additive mixed‐effect model accounting spatial phylogenetic autocorrelation on dataset including 5,412 737 Average was modelled as function body mass, diet, locomotor habits environmental conditions. validated using taxonomic block cross‐validation used estimated error to quantify around Results Small size, fossorial behaviour herbivorous diets were associated with highest densities, whereas large aerial carnivorous related lowest densities. Species non‐seasonal environments yielded higher densities than species high precipitation seasonality. Empirical vary by about four times within same statistically independent majority deviate five from observed values, indicating that prediction errors similar Main conclusions Our open up number macroecology conservation biogeography, biomass estimation, setting targets assessments planning, supporting Red List assessments. The methodology can be replicated easily other groups representative sample georeferenced estimates.

Language: Английский

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Including imprecisely georeferenced specimens improves accuracy of species distribution models and estimates of niche breadth

Global Ecology and Biogeography, Journal Year: 2023, Volume and Issue: 32(3), P. 342 - 355

Published: Jan. 8, 2023

Abstract Aim Museum and herbarium specimen records are frequently used to assess the conservation status of species their responses climate change. Typically, occurrences with imprecise geolocality information discarded because they cannot be matched confidently environmental conditions thus expected increase uncertainty in downstream analyses. However, using only precisely georeferenced risks undersampling geographical distributions species. We present two related methods allow use imprecisely biogeographical analysis. Innovation Our procedures assign (1) locations or (2) climates that closest centroid precise a For virtual species, including alongside improved accuracy ecological niche models projected future, especially for c . 20 fewer occurrences. Using underestimated loss suitable habitat overestimated amount both future. Including also improves estimates breadth extent occurrence. An analysis 44 North American Asclepias (Apocynaceae) yielded similar results. Main conclusions Existing studies examining effects spatial imprecision typically compare outcomes based on against same error added them. real‐world cases, analysts possess mix must decide whether retain discard latter. Discarding can undersample lead mis‐estimation past future method, which we provide software implementation enmSdmX package R, is simple help leverage large number deemed “unusable” geolocation.

Language: Английский

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Scale mismatches between predictor and response variables in species distribution modelling: A review of practices for appropriate grain selection

Progress in Physical Geography Earth and Environment, Journal Year: 2023, Volume and Issue: 47(3), P. 467 - 482

Published: Feb. 21, 2023

There is a lack of guidance on the choice spatial grain predictor and response variables in species distribution models (SDM). This review summarizes current state art with regard to following points: (i) effects changing resolution model performance; (ii) effect conducting multi-grain versus single-grain analysis (iii) role land cover type autocorrelation selecting appropriate size. In reviewed literature, we found that coarsening variable typically leads declining performance. Therefore, recommend aiming for finer resolutions unless there reason do otherwise (e.g. expert knowledge ecological scale). We also so far, improvements performance reported have been relatively low useful predictions can be generated even from single-scale models. addition, use high-resolution predictors improves however, only limited evidence whether this applies coarser-resolution 100 km 2 coarser). Low-resolution are usually sufficient associated fairly common environmental conditions but not less ones vs rare category). because reduces variability within heterogeneous underrepresentation environments, which lead decrease Thus, assessing at multiple grains provide insights into impacts their Overall, observed studies examining simultaneous manipulation variables. stress need explicitly report all

Language: Английский

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Modeling the rarest of the rare: a comparison between multi‐species distribution models, ensembles of small models, and single‐species models at extremely low sample sizes

Ecography, Journal Year: 2023, Volume and Issue: 2023(6)

Published: April 10, 2023

Species distribution models are useful for estimating the and environmental preferences of rare species, but these same species challenging to model on account sparse data. We contrast a traditional single‐species approach (generalized linear models, GLMs) with two promising frameworks modeling species: ensembles small (ESMs), which average across simple models; multi‐species (MSDMs), allow rarer benefit from statistical ‘borrowing strength' more common species. Using virtual within community real we evaluated how accuracy was influenced by number occurrences (N = 2–64), niche breadth, similarity numerous species' niches. For discriminating between presence absence, ESMs just terms (ESM‐L) performed best N ≤ 4, whereas GLMs polynomial (ESM‐P) were ≥ 8. calibrating response influential variables, MSDM hierarchical communities (HMSC) ESM‐P niches similar those other dissimilar niches, did 8, no well calibrated smaller sample sizes. identifying uninfluential ESM‐L archetype (SAMs), type MSDM, Models narrow others had highest discrimination capacity compared generalist and/or ‘Borrowing in MSDMs can assist some inference tasks, does not necessarily improve predictions species; simpler, may be better at given task. The algorithm depends goal (discrimination versus calibration), size, breadth similarity. Keywords: borrowing strength, calibration, data‐deficient discrimination, presence–absence,

Language: Английский

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Ecological Niche Models using MaxEnt in Google Earth Engine: Evaluation, guidelines and recommendations

Ecological Informatics, Journal Year: 2023, Volume and Issue: 76, P. 102147 - 102147

Published: May 29, 2023

Language: Английский

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Shortfalls in our understanding of the causes and consequences of functional and phylogenetic variation of freshwater communities across continents

Biological Conservation, Journal Year: 2023, Volume and Issue: 282, P. 110082 - 110082

Published: April 23, 2023

Freshwater ecosystems harbour a disproportionately high biodiversity relative to their area, being also one of the most threatened ecosystem types worldwide. However, our capacity design evidence-based conservation plans for this realm is restricted by all shortfalls that have been recognized so far. In context, paucity comparable field data and information on traits phylogenies freshwater organisms should be emphasized. Here, we highlight how increased knowledge could gained where aim at in research functional phylogenetic features communities. First, attempts combine datasets from different sources pay careful attention harmonization. Second, more effort focused natural history observations species habitats life histories, providing backbone multi-trait databases. Third, fully resolved would required deciphering evolutionary relationships organisms. Provided these three hurdles can overcome, conducting studies local communities across continental spatial extents pave way mapping functionally important evolutionarily valuable areas habitats.

Language: Английский

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Spatial resolution impacts projected plant responses to climate change on topographically complex islands

Diversity and Distributions, Journal Year: 2023, Volume and Issue: 29(10), P. 1245 - 1262

Published: July 27, 2023

Abstract Aim Understanding how grain size affects our ability to characterize species responses ongoing climate change is of crucial importance in the context an increasing awareness for substantial difference that exists between coarse spatial resolution macroclimatic data sets and microclimate actually experienced by organisms. Climate impacts on biodiversity are expected peak mountain areas, wherein differences macro microclimates precisely largest. Based a newly generated fine‐scale environmental Canary Islands, we assessed whether at 100 m able provide more accurate predictions than available 1 km resolution. We also analysed future suitability island endemic bryophytes differ depending grids. Location Islands. Time period Present (1979–2013) late‐century (2071–2100). Taxa Bryophytes. Methods compared accuracy using ensemble small models 14 Macaronesian bryophyte species. used two sets: CHELSA v1.2 (~1 km) CanaryClim v1.0 (100 m), downscaled version latter utilizing from local weather stations. encompasses five individual model intercomparison projects three warming shared socio‐economic pathways. Results Species distribution exhibited similar accuracy, but predicted buffered trends mid‐elevation ridges. consistently returned higher proportions suitable pixels (8%–28%) (0%–3%). Consequently, proportion occupy uncertain was with (3–8 species) (0–2 species). Main conclusions The impacted rather performance models. Our results highlight role fine‐resolution can play predicting potential both microrefugia new range under climate.

Language: Английский

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Optimising occurrence data in species distribution models: sample size, positional uncertainty, and sampling bias matter

Ecography, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 2, 2024

Species distribution models (SDMs) have proven valuable in filling gaps our knowledge of species occurrences. However, despite their broad applicability, SDMs exhibit critical shortcomings due to limitations occurrence data. These include, particular, issues related sample size, positional uncertainty, and sampling bias. In addition, it is widely recognised that the quality as well approaches used mitigate impact aforementioned data depend on ecology. While numerous studies evaluated effects these SDM performance, a synthesis results lacking. without comprehensive understanding individual combined effects, ability predict influence modelled species–environment associations remains largely uncertain, limiting value model outputs. this paper, we review bias, ecology We build upon findings provide recommendations for assessment intended use SDMs.

Language: Английский

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Species distribution models and island biogeography: Challenges and prospects

Global Ecology and Conservation, Journal Year: 2024, Volume and Issue: 51, P. e02943 - e02943

Published: April 8, 2024

Species distribution models (SDMs) are the primary tools used to model and predict changes species' ranges, often provide a quantitative baseline for conservation measures. However, most SDM methods frameworks have been primarily designed use with species relatively large amounts of occurrence data covering broad continental ranges. Here, we undertake systematic review literature (224 published studies) assess appropriate SDMs in island biogeography, specifically focusing on marine islands. We divide into different insular categories (i.e., chorotypes: single island/archipelago endemics, non-endemic natives, non-natives) order chorotype-specific recommendations. highlight how navigate three fundamental considerations related application environments. 1) Response variables, issue small sample sizes many species. 2) Predictor including (i) selection relevant environmental predictors at spatial grains, (ii) addressing truncation extent across entire range, especially 3) Model building, particularly, context limited species, approach uncertainty choice modelling method, avoid overfitting. also examine sources studies, finding that there strong geographical biases study location. Alongside this, evaluate potential GBIF database – comprehensive global occurrences research. find has potentially underutilised studies so far, represents useful resource filling gaps several taxa going forward. Based insights obtained from our review, propose set recommendations tailored

Language: Английский

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