Browsing by Subject "SPECIES DISTRIBUTION"

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  • Woolley, Skipton; Bax, Nicolas; Currie, Jock; Dunn, Daniel; Hansen, Cecilie; Hill, Nicole; O'Hara, Timothy; Ovaskainen, Otso; Sayre, Roger; Vanhatalo, Jarno; Dunstan, Piers (2020)
    Bioregions are important tools for understanding and managing natural resources. Bioregions should describe locations of relatively homogenous assemblages of species occur, enabling managers to better regulate activities that might affect these assemblages. Many existing bioregionalization approaches, which rely on expert-derived, Delphic comparisons or environmental surrogates, do not explicitly include observed biological data in such analyses. We highlight that, for bioregionalizations to be useful and reliable for systems scientists and managers, the bioregionalizations need to be based on biological data; to include an easily understood assessment of uncertainty, preferably in a spatial format matching the bioregions; and to be scientifically transparent and reproducible. Statistical models provide a scientifically robust, transparent, and interpretable approach for ensuring that bioregions are formed on the basis of observed biological and physical data. Using statistically derived bioregions provides a repeatable framework for the spatial representation of biodiversity at multiple spatial scales. This results in better-informed management decisions and biodiversity conservation outcomes.
  • Kemppinen, Julia; Niittynen, Pekka; Riihimaki, Henri; Luoto, Miska (2018)
    Soil moisture has a pronounced effect on earth surface processes. Global soil moisture is strongly driven by climate, whereas at finer scales, the role of non-climatic drivers becomes more important. We provide insights into the significance of soil and land surface properties in landscape-scale soil moisture variation by utilizing high-resolution light detection and ranging (LiDAR) data and extensive field investigations. The data consist of 1200 study plots located in a high-latitude landscape of mountain tundra in north-western Finland. We measured the plots three times during growing season 2016 with a hand-held time-domain reflectometry sensor. To model soil moisture and its temporal variation, we used four statistical modelling methods: generalized linear models, generalized additive models, boosted regression trees, and random forests. The model fit of the soil moisture models were R-2 = 0.60 and root mean square error (RMSE) 8.04 VWC% on average, while the temporal variation models showed a lower fit of R-2 = 0.25 and RMSE 13.11 CV%. The predictive performances for the former were R-2 = 0.47 and RMSE 9.34 VWC%, and for the latter R-2 = 0.01 and RMSE 15.29 CV%. Results were similar across the modelling methods, demonstrating a consistent pattern. Soil moisture and its temporal variation showed strong heterogeneity over short distances; therefore, soil moisture modelling benefits from high-resolution predictors, such as LiDAR based variables. In the soil moisture models, the strongest predictor was SAGA (System for Automated Geoscientific Analyses) wetness index (SWI), based on a 1m(2) digital terrain model derived from LiDAR data, which outperformed soil predictors. Thus, our study supports the use of LiDAR based SWI in explaining fine-scale soil moisture variation. In the temporal variation models, the strongest predictor was the field-quantified organic layer depth variable. Our results show that spatial soil moisture predictions can be based on soil and land surface properties, yet the temporal models require further investigation. Copyright (c) 2017 John Wiley & Sons, Ltd.
  • Mod, Heidi K.; Buri, Aline; Yashiro, Erika; Guex, Nicolas; Malard, Lucie; Pinto-Figueroa, Eric; Pagni, Marco; Niculita-Hirzel, Helene; van der Meer, Jan Roelof; Guisan, Antoine (2021)
    Soil bacteria are largely missing from future biodiversity assessments hindering comprehensive forecasts of ecosystem changes. Soil bacterial communities are expected to be more strongly driven by pH and less by other edaphic and climatic factors. Thus, alkalinisation or acidification along with climate change may influence soil bacteria, with subsequent influences for example on nutrient cycling and vegetation. Future forecasts of soil bacteria are therefore needed. We applied species distribution modelling (SDM) to quantify the roles of environmental factors in governing spatial abundance distribution of soil bacterial OTUs and to predict how future changes in these factors may change bacterial communities in a temperate mountain area. Models indicated that factors related to soil (especially pH), climate and/or topography explain and predict part of the abundance distribution of most OTUs. This supports the expectations that microorganisms have specific environmental requirements (i.e., niches/envelopes) and that they should accordingly respond to environmental changes. Our predictions indicate a stronger role of pH over other predictors (e.g. climate) in governing distributions of bacteria, yet the predicted future changes in bacteria communities are smaller than their current variation across space. The extent of bacterial community change predictions varies as a function of elevation, but in general, deviations from neutral soil pH are expected to decrease abundances and diversity of bacteria. Our findings highlight the need to account for edaphic changes, along with climate changes, in future forecasts of soil bacteria.
  • Dallas, Tad A; Saastamoinen, Marjo; Schulz, Torsti; Ovaskainen, Otso (2020)
    Metapopulation dynamics - patch occupancy, colonization and extinction - are the result of complex processes at both local (e.g. environmental conditions) and regional (e.g. spatial arrangement of habitat patches) scales. A large body of work has focused on habitat patch area and connectivity (area-isolation paradigm). However, these approaches often do not incorporate local environmental conditions or fully address how the spatial arrangement of habitat patches (and resulting connectivity) can influence metapopulation dynamics. Here, we utilize long-term data on a classic metapopulation system - the Glanville fritillary butterfly occupying a set of dry meadows and pastures in the angstrom land islands - to investigate the relative roles of local environmental conditions, geographic space and connectivity in capturing patch occupancy, colonization and extinction. We defined connectivity using traditional measures as well as graph-theoretic measures of centrality. Using boosted regression tree models, we find roughly comparable model performance among models trained on environmental conditions, geographic space or patch centrality. In models containing all of the covariates, we find strong and consistent evidence for the roles of resource abundance, longitude and centrality (i.e. connectivity) in predicting habitat patch occupancy and colonization, while patch centrality (connectivity) was relatively unimportant for predicting extinction. Relative variable importance did not change when geographic coordinates were not considered and models underwent spatially stratified cross-validation. Together, this suggests that the combination of regional-scale connectivity measures and local-scale environmental conditions is important for predicting metapopulation dynamics and that a stronger integration of ideas from network theory may provide insight into metapopulation processes.
  • Burner, Ryan C.; Stephan, Jorg G.; Drag, Lukas; Birkemoe, Tone; Muller, Joerg; Snäll, Tord; Ovaskainen, Otso; Potterf, Maria; Siitonen, Juha; Skarpaas, Olav; Doerfler, Inken; Gossner, Martin M.; Schall, Peter; Weisser, Wolfgang W.; Sverdrup-Thygeson, Anne (2021)
    Aim The aim of this study was to investigate the role of traits in beetle community assembly and test for consistency in these effects among several bioclimatic regions. We asked (1) whether traits predicted species' responses to environmental gradients (i.e. their niches), (2) whether these same traits could predict co-occurrence patterns and (3) how consistent were niches and the role of traits among study regions. Location Boreal forests in Norway and Finland, temperate forests in Germany. Taxon Wood-living (saproxylic) beetles. Methods We compiled capture records of 468 wood-living beetle species from the three regions, along with nine morphological and ecological species traits. Eight climatic and forest covariates were also collected. We used Bayesian hierarchical joint species distribution models to estimate the influence of traits and phylogeny on species' niches. We also tested for correlations between species associations and trait similarity. Finally, we compared species niches and the effects of traits among study regions. Results Traits explained some of the variability in species' niches, but their effects differed among study regions. However, substantial phylogenetic signal in species niches implies that unmeasured but phylogenetically structured traits have a stronger effect. Degree of trait similarity was correlated with species associations but depended idiosyncratically on the trait and region. Species niches were much more consistent-widespread taxa often responded similarly to an environmental gradient in each region. Main conclusions The inconsistent effects of traits among regions limit their current use in understanding beetle community assembly. Phylogenetic signal in niches, however, implies that better predictive traits can eventually be identified. Consistency of species niches among regions means niches may remain relatively stable under future climate and land use changes; this lends credibility to predictive distribution models based on future climate projections but may imply that species' scope for short-term adaptation is limited.