Mapping fine-scale distribution of the northern pika Ochotona hyperborea considering duality in microhabitat thermal conditions

Frontiers of Biogeography, Journal Year: 2025, Volume and Issue: 18

Published: March 20, 2025

Species distributions are frequently modeled using predictors that exceed the spatial scale experienced by focal species. Incorporating fine-scale environmental conditions is therefore expected to lead more realistic model predictions. However, importance of existing local heterogeneity on species distribution remains poorly assessed although can effectively utilize multiple microhabitats for behavioral adaptation withstand climate change impacts. Here, we developed a fine-resolution based ambient air northern pika ( Ochotona hyperborea ), small lagomorph found in rocky landforms, Hokkaido, Japan, first understand improvement performance from conventional coarse-resolution model. We then how predictions alter incorporating rock-interstice microclimates their habitats baseline (1981–2010) and future periods (2041–2100). The performed better overall predicted lower habitat suitability across study area than Incorporation microclimate increased markedly relative thermal conditions, which resulted predicting suitable areas (hotter) elevations remaining into future. This result suggests may negative impacts rising temperatures utilizing rock interstices via adaptation. Our findings highlight analyzing at fine scales considering heterogeneity, helps mitigate adverse change, conservation under change. use wide variety experience locally. In Complex topographical features locally buffer were increase enable persistence Local impact will be important conservation.

Language: Английский

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Disentangling the effect of the spatial scale and species spatial pattern on the abundance–suitability relationship

Ecography, Journal Year: 2025, Volume and Issue: unknown

Published: April 17, 2025

Knowledge about species abundance across broad spatial areas is crucial for unraveling ecological processes. Yet, estimation often demands extensive sampling effort associated with logistical challenges. Using suitability values obtained from distribution models (based on species' presence data) as a proxy has garnered interest during the last decades. Previous studies suggest triangular relationship between and suitability. Specifically, higher can display both low high abundances, while only abundances. This pattern arises because fail to consider limiting factors that drive abundance. In this study, we investigate effect of scale shaping relationship. We use simulation study case explore how these affect abundance–suitability The effects are represented by three model levels: 1) broad‐scale covariates, 2) intermediate 3) broad, local covariates. patterns characterized two different shapes: aggregated uniform. Our findings reveal integrating local‐scale covariates exhibiting more show stronger Additionally, observe an interaction scale. For species, benefits most notably addition intermediate‐scale contrast, uniform benefit remains consistent regardless whether intermediate‐ or added. results underscore importance considering methodological improve proxies derived models. highlight need information operating at make reliable inferences potential strategies doing it.

Language: Английский

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Harnessing Multiscale Topographic Environmental Variables for Regional Coral Species Distribution Models

Ecology and Evolution, Journal Year: 2025, Volume and Issue: 15(4)

Published: April 1, 2025

ABSTRACT Effective biodiversity conservation requires knowledge of species' distributions across large areas, yet prevalence data for marine sessile species is scarce, with traditional variables often unavailable at appropriate temporal and spatial resolutions. As organism generally depend on terrain heterogeneity, topographic derived from digital elevation models (DEMs) can be useful proxies in ecological modelling, given Here, we use three reef‐building Acropora coral the Great Barrier Reef, Australia, a case study to (1) assess high‐resolution bathymetry DEM sources accuracy, (2) harness their regional distribution (SDMs), (3) develop transferable framework produce, select integrate multi‐resolution into models. For this, obtained processed distinct bathymetric depth that treat as DEMs, which are available GBR extent: (i) Allen Coral Atlas (ACA) 10 m, (ii) DeepReef 30 m (iii) 100 m. We generalised DEMs multiple nested resolutions (15 m–120 m) same eight SDM sensitivity source resolution. The ACA shared similar vertical accuracies, each producing relevant SDMs. Slope vector ruggedness measure (VRM), capturing hydrodynamic movement shelter or exposure, were most SDMs all species. Interestingly, finest resolution not always accurate SDMs, optimal between 15 60 depending variable type Using provided nuanced insights multiscale drivers distributions. Drawing this study, provide practical facilitate adoption better‐informed management planning.

Language: Английский

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Direct and mediated impacts of mixed forests on Norway spruce infestation by European bark beetle Ips typographus

Forest Ecology and Management, Journal Year: 2024, Volume and Issue: 569, P. 122184 - 122184

Published: July 31, 2024

Language: Английский

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Comparison of three global canopy height maps and their applicability to biodiversity modeling: Accuracy issues revealed

Ecosphere, Journal Year: 2024, Volume and Issue: 15(10)

Published: Oct. 1, 2024

Abstract Global mapping of forest height is an extremely important task for estimating habitat quality and modeling biodiversity. Recently, three global canopy maps have been released, the map (GFCH), high‐resolution model Earth (HRCH), tree (GMTCH). Here, we assessed their accuracy usability biodiversity modeling. We examined by comparing them with reference models derived from airborne laser scanning (ALS). Our results show considerable differences between evaluated maps. The root mean square error ranged 10 18 m GFCH, 9–11 HRCH, 10–17 GMTCH, respectively. GFCH GMTCH consistently underestimated all canopies regardless height, while HRCH tended to overestimate low underestimate tall canopies. Biodiversity using predicted as input data are sufficient simple relationships species occurrence but use leads a decrease in discrimination ability mischaracterization niches where indices (e.g., heterogeneity) concerned. showed that heterogeneity considerably urge temperate areas rich ALS data, activities should concentrate on harmonizing rather than relying modeled products.

Language: Английский

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Integrating very high resolution environmental proxies in genotype–environment association studies

Evolutionary Applications, Journal Year: 2024, Volume and Issue: 17(7)

Published: June 28, 2024

Abstract Landscape genomic analyses associating genetic variation with environmental variables are powerful tools for studying molecular signatures of species' local adaptation and detecting candidate genes under selection. The development landscape genomics over the past decade has been spurred by improvements in resolutions datasets, allegedly increasing power to identify putative underlying non‐model organisms. Although these associations have successfully applied numerous species across a diverse array taxa, spatial scale predictor largely overlooked, potentially limiting conclusions be reached methods. To address this knowledge gap, we systematically evaluated performances genotype–environment association (GEA) models using at multiple resolutions. Specifically, used multivariate redundancy associate whole‐genome sequence data from plant Arabis alpina L. collected four neighboring valleys western Swiss Alps, very high‐resolution topographic derived digital elevation grain sizes between 0.5 m 16 m. These comparisons highlight sensitivity resolution, where optimal were specific variable type, terrain characteristics, study extent. assist selecting appropriate resolutions, demonstrate practical approach produce, select, integrate multiscale into GEA models. After generalizing fine‐grained forward selection procedure is retain only most relevant particular context. Depending on relevance studies calls integrating scales By carefully considering more realistic range pressures can detected downstream analyses, important implications experimental research conservation management natural populations.

Language: Английский

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A theoretical framework for upscaling species distribution models

Methods in Ecology and Evolution, Journal Year: 2023, Volume and Issue: 14(11), P. 2888 - 2899

Published: Sept. 25, 2023

Abstract Species distribution models (SDM) have become one of the most popular predictive tools in ecology. With advent new computation and remote sensing technology, high‐resolution environmental data sets are becoming more common predictors these modelling efforts. Understanding how scaling affects their outputs is therefore fundamental to understand applicability. Here, we develop a theoretical basis consequences aggregating occurrence at different resolutions. We provide framework, along with numerical simulations real‐world case study, show rules influence outputs. that properties environment–occurrence relationships change when aggregated: mean probability species prevalence increases, optimal values shift classification rates increase coarser resolutions up certain level. Furthermore, contrary widespread expectation would produce better predictions, here model performance may using resolution rather than inverse. Finally, also depends not only on relationship but interaction between this geography available environment. This framework helps understanding previously incoherent results regarding SDM upscaling performance, illustrates empirical can important feedbacks advance issues macroecology. The shape environment effects explain why some difficult transfer regions. Most importantly, argue there conceptual choices related fitting require expert knowledge further explorations theory practice

Language: Английский

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Mapping fine-scale distribution of the northern pika Ochotona hyperborea considering duality in microhabitat thermal conditions

Published: July 15, 2024

Species distributions are frequently modeled using predictors that exceed the spatial scale experienced by focal species. Incorporating fine-scale environmental conditions is therefore expected to lead more realistic model predictions. However, importance of variety in existing on species distribution remains poorly assessed although can effectively utilize multiple microhabitats for behavioral adaptation withstand climate change impacts. Here, we developed a based ambient air northern pika ( Ochotona hyperborea ), small lagomorph found rocky landforms, first understand improvement performance from conventional coarse-scale model. We then how predictions alter incorporating rock-interstice microclimates their habitats baseline (1981–2010) and future periods (2041–2100). The performed better overall predicted lower habitat suitability across study area than Incorporation microclimate increased markedly relative conditions, which resulted predicting suitable areas elevations remaining into future. This result suggests may negative impacts rising temperatures utilizing rock interstices via adaptation. Our findings highlight analyzing at fine scales considering local heterogeneity, helps mitigate adverse change, conservation under change.

Language: Английский

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Optimising Species Distribution Models: Sample size, positional error, and sampling bias matter

Published: Dec. 4, 2023

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 error, and sampling bias. In addition, it is widely recognized that the quality as well approaches used mitigate impact aforementioned data are dependent on ecology. While numerous studies experimentally 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 integrate findings into step-by-step guide for assessment spatial intended use SDMs.

Language: Английский

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Data stochasticity and model parametrisation impact the performance of species distribution models: insights from a simulation study

Peer Community Journal, Journal Year: 2023, Volume and Issue: 3

Published: April 7, 2023

Species distribution models (SDM) are widely used to describe and explain how species relate their environment predict spatial distributions. As such, they the cornerstone of most planning efforts worldwide. SDM can be implemented with a wide array data types (presence-only, presence-absence, count...), which either point- or areal-based, use environmental conditions as predictor variables. The choice sampling type well resolution recognized crucial importance, yet we lack any quantification effects these decisions may have on reliability. In present work, fill this gap an unprecedented simulation procedure. We simulated 100 possible distributions two different virtual in regions. were modelled using segment- areal-based five resolutions conditions. performances inspected by statistical metrics, model composition, shapes relationships prediction quality. provided clear evidence stochasticity modelling process (particularly relationships): dataset from same survey, region could yield results. Sampling had stronger than final relevance. effect coarsening was directly related resistance features changes scale: failed adequately identify when targeted diluted coarsening. These results important implications for community, backing up some commonly accepted choices, but also highlighting up-to-now unexpected (stochasticity). whole, work calls carefully weighted implementing models, caution interpreting

Language: Английский

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