Data characteristics and species traits are expected to influence the accuracy with which species' distributions can be modeled and predicted. We compare 10 modeling techniques in terms of predictive ...power and sensitivity to location error, change in map resolution, and sample size, and assess whether some species traits can explain variation in model performance. We focused on 30 native tree species in Switzerland and used presence-only data to model current distribution, which we evaluated against independent presence—absence data. While there are important differences between the predictive performance of modeling methods, the variance in model performance is greater among species than among techniques. Within the range of data perturbations in this study, some extrinsic parameters of data affect model performance more than others: location error and sample size reduced performance of many techniques, whereas grain had little effect on most techniques. No technique can rescue species that are difficult to predict. The predictive power of species-distribution models can partly be predicted from a series of species characteristics and traits based on growth rate, elevational distribution range, and maximum elevation. Slow-growing species or species with narrow and specialized niches tend to be better modeled. The Swiss presence-only tree data produce models that are reliable enough to be useful in planning and management applications.
Aim: To disentangle the effects of environmental and geographical processes driving phylogenetic distances among clades of maritime pine (Pinus pinaster). To assess the implications for conservation ...management of combining molecular information with species distribution models (SDMs; which predict species distribution based on known occurrence records and on environmental variables). Location: Western Mediterranean Basin and European Atlantic coast. Methods: We undertook two cluster analyses for eight genetically defined pine clades based on climatic niche and genetic similarities. We assessed niche similarity by means of a principal component analysis and Schoener's D metric. To calculate genetic similarity, we used the unweighted pair group method with arithmetic mean based on Nei's distance using 266 single nucleotide polymorphisms. We then assessed the contribution of environmental and geographical distances to phylogenetic distance by means of Mantel regression with variance partitioning. Finally, we compared the projection obtained from SDMs fitted from the species level (SDMsp) and composed from the eight clade-level models (SDMcm). Results: Genetically and environmentally defined clusters were identical. Environmental and geographical distances explained 12.6% of the phylogenetic distance variation and, overall, geographical and environmental overlap among clades was low. Large differences were detected between SDMsp and SDMcm (57.75% of disagreement in the areas predicted as suitable). Main conclusions: The genetic structure within the maritime pine subspecies complex is primarily a consequence of its demographic history, as seen by the high proportion of unexplained variation in phylogenetic distances. Nevertheless, our results highlight the contribution of local environmental adaptation in shaping the lower-order, phylogeographical distribution patterns and spatial genetic structure of maritime pine: (1) genetically and environmentally defined clusters are consistent, and (2) environment, rather than geography, explained a higher proportion of variation in phylogenetic distance. SDMs, key tools in conservation management, better characterize the fundamental niche of the species when they include molecular information.
Aim: Species distribution models (SDMs) based on current species ranges underestimate the potential distribution when projected in time and/or space. A multitemporal model calibration approach has ...been suggested as an alternative, and we evaluate this using 13,000 years of data. Location: Europe. Methods: We used fossil-based records of presence for Picea abies, Abies alba and Fagus sylvatica and six climatic variables for the period 13,000 to 1000 yr BP. To measure the contribution of each 1000-year time step to the total niche of each species (the niche measured by pooling all the data), we employed a principal components analysis (PCA) calibrated with data over the entire range of possible climates. Then we projected both the total niche and the partial niches from single time frames into the PCA space, and tested if the partial niches were more similar to the total niche than random. Using an ensemble forecasting approach, we calibrated SDMs for each time frame and for the pooled database. We projected each model to current climate and evaluated the results against current pollen data. We also projected all models into the future. Results: Niche similarity between the partial and the total-SDMs was almost always statistically significant and increased through time. SDMs calibrated from single time frames gave different results when projected to current climate, providing evidence of a change in the species realized niches through time. Moreover, they predicted limited climate suitability when compared with the total-SDMs. The same results were obtained when projected to future climates. Main conclusions: The realized climatic niche of species differed for current and future climates when SDMs were calibrated considering different past climates. Building the niche as an ensemble through time represents a way forward to a better understanding of a species' range and its ecology in a changing climate.
Global measurements of atmospheric methane (CH4) concentrations continue to show large interannual variability whose origin is only partly understood. Here we quantify the influence of the El ...Niño–Southern Oscillation (ENSO) on wetland CH4 emissions, which are thought to be the dominant contributor to interannual variability of the CH4 sources. We use a simple wetland CH4 model that captures variability in wetland extent and soil carbon to model the spatial and temporal dynamics of wetland CH4 emissions from 1950–2005 and compare these results to an ENSO index. We are able to explain a large fraction of the global and tropical variability in wetland CH4 emissions through correlation with the ENSO index. We find that repeated El Niño events throughout the 1980s and 1990s were a contributing factor towards reducing CH4 emissions and stabilizing atmospheric CH4 concentrations. An increase in emissions from the boreal region would likely strengthen the feedback between ENSO and interannual variability in global wetland CH4 emissions. Our analysis emphasizes that climate variability has a significant impact on wetland CH4 emissions, which should be taken into account when considering future trends in CH4 sources.
Key Points
ENSO‐wetland interactions helped stabilize atmospheric CH4 in recent decades
ENSO events explain a large portion of wetland CH4 interannual variability
Increased variability from boreal zone would strengthen ENSO‐wetland feedback
1. Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters ...rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits. 2. We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics. 3. More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species. 4. Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The coresatellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species. 5. Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change.
The sensitivity of global carbon and water cycling to climate variability is coupled directly to land cover and the distribution of vegetation. To investigate biogeochemistry-climate interactions, ...earth system models require a representation of vegetation distributions that are either prescribed from remote sensing data or simulated via biogeography models. However, the abstraction of earth system state variables in models means that data products derived from remote sensing need to be post-processed for model-data assimilation. Dynamic global vegetation models (DGVM) rely on the concept of plant functional types (PFT) to group shared traits of thousands of plant species into usually only 10–20 classes. Available databases of observed PFT distributions must be relevant to existing satellite sensors and their derived products, and to the present day distribution of managed lands. Here, we develop four PFT datasets based on land-cover information from three satellite sensors (EOS-MODIS 1 km and 0.5 km, SPOT4-VEGETATION 1 km, and ENVISAT-MERIS 0.3 km spatial resolution) that are merged with spatially-consistent Köppen-Geiger climate zones. Using a beta (ß) diversity metric to assess reclassification similarity, we find that the greatest uncertainty in PFT classifications occur most frequently between cropland and grassland categories, and in dryland systems between shrubland, grassland and forest categories because of differences in the minimum threshold required for forest cover. The biogeography-biogeochemistry DGVM, LPJmL, is used in diagnostic mode with the four PFT datasets prescribed to quantify the effect of land-cover uncertainty on climatic sensitivity of gross primary productivity (GPP) and transpiration fluxes. Our results show that land-cover uncertainty has large effects in arid regions, contributing up to 30% (20%) uncertainty in the sensitivity of GPP (transpiration) to precipitation. The availability of PFT datasets that are consistent with current satellite products and adapted for earth system models is an important component for reducing the uncertainty of terrestrial biogeochemistry to climate variability.
Demographic processes and demographic data are increasingly being included in models of the spatio–temporal dynamics of species' ranges. In this special issue, we explore how the integration of ...demographic processes further the conceptual understanding and prediction of species' range dynamics. The 12 papers originate from two workshops entitled ‘Advancing concepts and models of species range dynamics: understanding and disentangling processes across scales’. The papers combine theoretical and empirical evidence for the interplay between environmental conditions, species interactions, demographic processes (births, deaths, dispersal), physiology, and evolution, and they point out promising avenues towards a better understanding and prediction of species' range dynamics.
Understanding the drivers of population divergence, speciation and species persistence is of great interest to molecular ecology, especially for species‐rich radiations inhabiting the world's ...biodiversity hotspots. The toolbox of population genomics holds great promise for addressing these key issues, especially if genomic data are analysed within a spatially and ecologically explicit context. We have studied the earliest stages of the divergence continuum in the Restionaceae, a species‐rich and ecologically important plant family of the Cape Floristic Region (CFR) of South Africa, using the widespread CFR endemic Restio capensis (L.) H.P. Linder & C.R. Hardy as an example. We studied diverging populations of this morphotaxon for plastid DNA sequences and >14 400 nuclear DNA polymorphisms from Restriction site Associated DNA (RAD) sequencing and analysed the results jointly with spatial, climatic and phytogeographic data, using a Bayesian generalized linear mixed modelling (GLMM) approach. The results indicate that population divergence across the extreme environmental mosaic of the CFR is mostly driven by isolation by environment (IBE) rather than isolation by distance (IBD) for both neutral and non‐neutral markers, consistent with genome hitchhiking or coupling effects during early stages of divergence. Mixed modelling of plastid DNA and single divergent outlier loci from a Bayesian genome scan confirmed the predominant role of climate and pointed to additional drivers of divergence, such as drift and ecological agents of selection captured by phytogeographic zones. Our study demonstrates the usefulness of population genomics for disentangling the effects of IBD and IBE along the divergence continuum often found in species radiations across heterogeneous ecological landscapes.
This review presents an overview of cardiac A
2A
-adenosine receptors The localization of A
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-AR in the various cell types that encompass the heart and the role they play in force regulation in ...various mammalian species are depicted. The putative signal transduction systems of A
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-AR in cells in the living heart, as well as the known interactions of A
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-AR with membrane-bound receptors, will be addressed. The possible role that the receptors play in some relevant cardiac pathologies, such as persistent or transient ischemia, hypoxia, sepsis, hypertension, cardiac hypertrophy, and arrhythmias, will be reviewed. Moreover, the cardiac utility of A
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-AR as therapeutic targets for agonistic and antagonistic drugs will be discussed. Gaps in our knowledge about the cardiac function of A
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-AR and future research needs will be identified and formulated.
Terrestrial and oceanic carbon cycle processes remove ~55 % of global carbon emissions, with the remaining 45 %, known as the "airborne fraction", accumulating in the atmosphere. The long-term ...dynamics of the component fluxes contributing to the airborne fraction are challenging to interpret, but important for informing fossil-fuel emission targets and for monitoring the trends of biospheric carbon fluxes. Climate and land-cover forcing data for terrestrial ecosystem models are a largely unexplored source of uncertainty in terms of their contribution to understanding airborne fraction dynamics. Here we present results using a single dynamic global vegetation model forced by an ensemble experiment of climate (CRU, ERA-Interim, NCEP-DOE II), and diagnostic land-cover datasets (GLC2000, GlobCover, MODIS). For the averaging period 1996–2005, forcing uncertainties resulted in a large range of simulated global carbon fluxes, up to 13 % for net primary production (52.4 to 60.2 Pg C a−1) and 19 % for soil respiration (44.2 to 54.8 Pg C a−1). The sensitivity of contemporary global terrestrial carbon fluxes to climate strongly depends on forcing data (1.2–5.9 Pg C K−1 or 0.5 to 2.7 ppmv CO2 K−1), but weakening carbon sinks in sub-tropical regions and strengthening carbon sinks in northern latitudes are found to be robust. The climate and land-cover combination that best correlate to the inferred carbon sink, and with the lowest residuals, is from observational data (CRU) rather than reanalysis climate data and with land-cover categories that have more stringent criteria for forest cover (MODIS). Since 1998, an increasing positive trend in residual error from bottom-up accounting of global sinks and sources (from 0.03 (1989–2005) to 0.23 Pg C a−1 (1998–2005)) suggests that either modeled drought sensitivity of carbon fluxes is too high, or that carbon emissions from net land-cover change is too large.
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