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16 results for “biome change”
Current and future global distribution of potential biomes under climate change scenarios
<p>Probability and uncertainty maps showing the potential current and future natural vegetation on a global scale under three different climate change scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) predicted using ensemble machine learning. Current (2022 - 2023) conditions are calculated on historical long term averages (1979 - 2013), while future projections cover two different epochs: 2040 - 2060 and 2061 - 2080.</p> <p>Files are named according to the following naming convention, e.g.:</p> <ul> <li>biomes_graminoid.and.forb.tundra.rcp85_p_1km_a_20610101_20801231_go_epsg.4326_v20230410</li> </ul> <p>with the following fields:</p> <ul> <li>generic theme: <strong>biomes</strong>,</li> <li>variable name: <strong>graminoid.and.forb.tundra.rcp85</strong>,</li> <li>variable type, e.g. probability ("<strong>p</strong>"), hard class ("<strong>c</strong>"), model deviation ("<strong>md</strong>")</li> <li>spatial resolution: <strong>1km</strong>,</li> <li>depth reference, e.g. below ("<strong>b</strong>"), above ("<strong>a</strong>") ground or at surface ("<strong>s</strong>"),</li> <li>begin time (YYYYMMDD): <strong>20610101</strong>,</li> <li>end time: <strong>20801231</strong>,</li> <li>bounding box, e.g. global land without Antarctica ("<strong>go</strong>"),</li> <li>EPSG code: <strong>epsg.4326</strong>,</li> <li>version code, e.g. creation date: <strong>v20230410</strong>.</li> </ul> <p>We provide probability and hard class layers using a revised classification system of the <a href="https://www.jstor.org/stable/2846196">BIOME 6000 project</a> explained in the work of <a href="https://doi.org/10.7717/peerj.5457">Hengl et al. (2018)</a>. The 20 classes from this classification system have then been aggregated in 6 biome classes following the <a href="https://global-ecosystems.org/page/typology">IUCN Global Ecosystem Typology</a> classification system.</p> <p>For probability layers, the uncertainty (model deviation: <strong>md</strong>) is calculated as the standard deviation of the predicted values of the base learners of the ensemble model. The higher the standard deviation the more uncertain the model is regarding the right value to assign to the pixel.</p> <p>For hard class layers the uncertainty is calculated using the margin of victory (<a href="https://doi.org/10.1016/j.rse.2020.112148">Calderón-Loor et al., 2021</a>) defined as the difference between the first and the second highest class probability value in a given pixel. High values would be measures of low uncertainty, while low values would indicate a high uncertainty. It is highly recommended to use the <strong>md </strong>layers to properly interpret the results of the map.</p> <p>Styling files are provided in both <em><strong>.SLD</strong></em> and <em><strong>.QML</strong></em> format; two different styling files are provided for the uncertainty of the probability layers and the hard classes due to the different interpretation of the chosen uncertainty metrics.</p> <p>The R scripts and a tutorial will be uploaded to the <a href="https://github.com/Envirometrix/PNVmaps">PNVmaps Github repository</a>, where previous versions of the biomes maps from <a href="https://doi.org/10.7717/peerj.5457">Hengl et al. (2018)</a> is currently hosted. To cite the maps and the methodology, it is possible to refer to the scientific publication:</p> <p>Bonannella C, Hengl T, Parente L, de Bruin S. 2023. Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation. PeerJ 11:e15593 <a href="https://doi.org/10.7717/peerj.15593">https://doi.org/10.7717/peerj.15593</a></p>
Lotic Intersite Nitrogen eXperiment II (LINX II): a cross-site study of the effects of anthropogenic land use change on nitrate uptake and retention in 72 streams across 8 different biomes (2003 – 2006).
The LINX II (Lotic Intersite Nitrogen eXperiment) project was designed to quantify the rates and mechanisms of nitrate retention in streams using stable isotope tracer additions. The study encompassed 72 stream reaches spread across 8 North American biomes. Within each biome, 9 streams were selected in three watershed land-use categories: 3 reference, 3 agricultural, and 3 urbanized. The core of the study was a 24-hour release of 15N- labeled nitrate. Prior to the isotope addition, physical, chemical and biological characteristics of the stream were measured. The measurements included, but were not limited to, dissolved nutrient concentrations, dissolved conservative tracer additions (to quantify hydraulic and hyporheic retention, velocity and discharge), standing stocks of primary uptake biota (including suspended and benthic particulate materials) as well as channel dimensions, photosynthetically active radiation, and water temperature. During the isotope release, whole stream rates of ecosystem metabolism were quantified (including quantification of re-aeration coefficients using tracer gas additions), and concentrations of 15N-labeled NO3, NH4, N2 and N2O were measured. Immediately following the isotope addition, 15N uptake by aquatic organisms was quantified by sampling biomass components on the stream bed. The data generated from these 72 stream reaches were used to develop a stream nitrogen retention model for each biome, which was expanded to entire drainage networks to predict nitrogen fluxes. The LINX II study demonstrated how biotic uptake of nitrate and denitrification increased with increasing nitrate concentrations. However, the efficiency of total uptake and denitrification actually declined with increasing nitrate concentrations (such as those seen on agricultural or urbanized streams), yielding higher rates of dissolved nitrogen exports downstream. The datasets presented here consist of the primary data collected by the LINX II study participants.
Data for: Changes in evapotranspiration, transpiration and evaporation across natural and managed landscapes in the Amazon, Cerrado and Pantanal biomes
<p>This dataset contains measurements of evapotranspiration and other meteorological variables (net radiation, air temperature, vapor pressure deficit, etc) from nine eddy covariance towers located in different ecosystems in the Amazon (natural Amazon forest, cropland and pastureland), Cerrado (natural savannah, irrigated and rainfed croplands) and Pantanal (natural forest, pastureland) biomes. It also contains estimates of transpiration that were calculated using two different approaches, the transpiration estimation algorithm (TEA) and the underlying water use efficiency method (uWUE).</p>
Net loss of biomass predicted for tropical biomes in a changing climate
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Data & codes for "Changes in abundance and distribution of European forest bird populations depend on biome, ecological specialisation and traits"
<h1>1. Selection of European forest bird species and classification of their biome preferences</h1> <p>We selected all species that are related to forest and woodland based on two data sources: Storchová & Hořák (2018) and Tobias et al. (2022), resulting in 107 bird species studied (Data S1). We defined forest bird species as those using environments ranging from closed-canopy forests to more open-canopy woodlands (A. Lehikoinen & Virkkala, 2018; Storchová & Hořák, 2018; Tobias et al., 2022). We determined their biome specialisation using breeding distribution centroids and the overall breeding distribution of each of the species, using the global map of terrestrial ecoregions from Olson et al. (2001) and range data from European Breeding Bird Atlas 1 and 2 (Hagemeijer & Blair, 1997; Keller et al., 2020). We categorised species as Mediterranean, temperate, or boreal based on their predominant biogeographic region. We considered species commonly occurring over several biomes as “generalists”. For instance, we reclassified the two typically boreal species Glaucidium passerinum Linnaeus and Strix uralensis Pallas as “generalists” due to significant range expansions into central and southern Europe in recent decades, therefore no longer restricted to the boreal region. For the complete list of species, biome specialisation, traits, and specialisation indices, refer to Data S1.</p> <h1>2. Changes in abundance and distribution of European forest bird species</h1> <p>We assessed long-term changes in European forest bird populations through two approaches: (i) changes in estimated total European-level species abundance over a 40-year timeframe; and (ii) changes in species spatial distribution over a 30-year timeframe (Fig. 1).</p> <p>We utilized the estimated trends in European-level population size (i.e., the total number of individuals) for each common native European bird species from 1980 to 2017, as reported by Burns et al. (2021). Three species out of the 107 studied forest species were missing in the original manuscript and we used data generated with the same method from 1980 to 2018 from the European assessment, Article 12 (https://nature-art12.eionet.europa.eu/article12/). These abundance trends were calculated by Burns et al. (2021) using multi-sourced annual times series. For each species, they gathered population estimates and trends from each European country as well as European Union (EU)-level population trends. They analysed these data with a Bayesian hierarchical model to reconstruct EU-level smoothed species population time series. The model outputs include an average annual rate of abundance change and an associated 95% credible interval (Burns et al., 2021). Therefore, we did not directly use the average annual rate of abundance change, as this would have led us to consider species with low uncertainty as similar to those with high uncertainty. To account for the uncertainty, we categorised species as (i) declining, i.e., annual rates below one, (ii) increasing, i.e., annual rates above one and (iii) stable, i.e., annual rate whose 95% CI overlap one, i.e., no significant change. To better acknowledge the magnitude of the abundance change, significant changes with rates below 0.98 were labelled as “strongly declining” (i.e., 6.5% of the 107 species), while those above 1.02 were labelled as “strongly increasing” (i.e., 11% of the 107 species). To evaluate the sensitivity of the decision to categorised abundance change data, we also analysed abundance trend as continuous variable (see Supporting Information Fig. S8).</p> <p>To determine changes in species distributions, we used a comparison of species distributions between two periods (i.e., 1985-1988 and 2013-2017) using the European Breeding Bird Atlas 1 and 2 (EBBA 1 & 2; Hagemeijer & Blair, 1997; Howard et al., 2023; Keller et al., 2020). Howard et al. (2023) provided calculations of observed colonisation and extinction areas at a 50 x 50 km resolution across Europe. We measured changes in range as the difference between colonisations and extinctions of each species, with negative values indicating contracting ranges and positive values indicating expanding ranges. Additionally, we calculated the shift in the centre of gravity of the distribution range between the two periods, as a distance (km) along the south-north gradient for each species (Howard et al., 2023).</p> <h1>3. Trait and specialisation data for European forest bird species</h1> <p>We extracted data for six functional traits from several sources (Table 1). (i) The species temperature index (STI)represents the long-term average temperature within the species’ breeding range (A. Lehikoinen et al., 2021). (ii) Diet data during the breeding season were obtained from Storchová & Hořák (2018), classifying species into binary variables as vertebrate carnivorous, invertebrate carnivorous, and herbivores (combining the leaf and seed eaters). Storchová & Hořák (2018) classified species into a diet category when the corresponding food resource represented at least 10% of the species diet throughout the breeding season. Therefore, one species can be in several categories (i.e., omnivores). (iii) We obtained nesting site data from Pearman et al. (2014), classifying species into binary variables as ground nesters, tree hole nesters, or elevated nesters (> 1 m in a tree or shrub). We also included data on (iv) species dependence on old-growth forests (Data S1; mostly from Fraixedas et al. (2015) and Mönkkönen et al. (2014), if present on both references, we classified them as “1” and if only in one reference as “0.5”), (v) migration distance (Howard et al., 2023), and (vi) body mass (Tobias et al., 2022).</p> <p>Finally, we extracted and developed seven species specialisation indices. (i) We used an overall specialisation index based on multiple traits (i.e., temperature, diet, foraging behaviour and substrate, habitat, and nesting site), and (ii) a nesting specialisation index, both obtained from Morelli et al. (2019). Both indices represent species specialization based on the dispersion of trait preferences for each species: e.g., nesting specialism equal 0 for species that nest in all habitat type and equal 1 for species that nest in only one habitat type). They are both calculated using the Gini index of inequality, which measures overall dispersion across, e.g., all traits for the overall specialization, based on data from Pearman et al. (2014) and Storchová & Hořák (2018). For additional information, see Morelli et al. (2019). We also used (iii) the diet specialisation index, (iv) the species distribution range during the breeding season (hereafter “breeding range area”) and (v) the climatic niche breadth from Reif et al. (2016). The diet specialisation index was calculated as the coefficient of variation for diet preferences for each species, where high values denotes specialized species (Reif et al., 2016). The breeding range area was evaluated as the number of 50-km squares in the distribution maps in Europe occupied by each species during the reproduction period, and is based on EBBA 1 (Hagemeijer & Blair, 1997). The climatic niche breadth was calculated as the difference between the 5% hottest and the 5% coldest mean temperature between April and June in which each species occurs, using EBBA 1 (Hagemeijer & Blair, 1997; Reif et al., 2016).</p> <p>Additionally, (vi) we calculated a broadleaf forest specialisation index based on binary forest habitat preferences (Storchová & Hořák, 2018), assigning values of one for species found only in broadleaf forests; zero for those in coniferous forests, and 0.5 for those found in both. Lastly, (vii) we created a forest specialisation index based on the species habitat preferences (Storchová & Hořák, 2018). The forest specialisation index was calculated as the mean of species affinity across habitats. We used increasing habitat weights along a gradient of tree dominance: open habitats as 1, shrubland as 1.5, woodland as 2 (i.e., species associated with habitats structured by trees in lower density than in forest), forest generalist (found in both coniferous and broadleaf dense forests) as 3, and forest specialist (found only either in coniferous or broadleaf dense forests) as 4. For instance, the index value for species occurring either in shrubland, woodland or both broadleaf and coniferous forests is 2.167.</p> <h1>4. Data analysis</h1> <p>Data analyses were conducted with R software version 4.4.1. (R Core Team, 2024). Given the non-independence of species due to their genetic relatedness, we accounted for interspecific phylogenetic distance in all models. We constructed the phylogenetic tree for the 107 European forest bird species using ‘rotl’ and ‘ape’ R-packages (Michonneau et al., 2022; Paradis et al., 2023). We used rotl as an interface with the "Open Tree of Life", employing tol_induced_subtree R-function to generate the phylogenetic tree and compute.brlen R-function to set branch lengths using Grafen’s computation. We generated separate phylogenetic trees for boreal (17), temperate (15), Mediterranean (16) and “generalist” (59) species to perform biome-specific analysis (see Supplementary Information, Figs. S1 & S2).</p> <p>To investigate the effects of functional traits and specialisation indices on abundance, range changes, and distribution shift, we used two regression methods. All methods were based on the relationships between a measure of change and a functional trait or specialisation index. Our sample unit is an individual forest bird species (i.e., one value for each species, either abundance or range change, or distribution shift). Abundance change was a categorical variable (i.e., strong decline – decline – stable – increase – strong increase), while range change (i.e., difference between colonisation and extinction) and distribution shift (i.e., south-north shift) were continuous variables. Therefore, to study abundance changes, we used proportional-odds linear mixed effects model using (Phylo)clmm R-function from the ‘ordinal’ R-package (Christensen, 2022). Interspecific phylogenetic relatedness was included as a random effect, reflecting the correlation between species based on phylogenetic distances (see also Hagge et al. (2021) and Seibold et al. (2015)). For distribution changes, we employed phylogenetic generalised least squares regression (PGLS) using the gls R-function from the ‘nlme’ R-package (Pinheiro et al., 2023). The phylogenetic correlation structure was integrated into PGLS using Pagel’s lambda parameter (λ; Pagel (1999)) a widely used measured of phylogenetic signal strength (see, e.g., Hagge et al., 2021; Triviño et al., 2013).</p> <p>Furthermore, we included latitude, a key driver of bird communities at broad scales (Luoto et al., 2007), as a fixed covariable (centroid latitude of the species’ breeding distribution) in all global models (i.e., species from all biomes together), except for the STI model due to strong correlation. For biome-specific analysis, we included latitude only in boreal species models for range change and distribution shift, as it significantly improved model fit (ΔAIC < -2). We did not add latitude for models specific to temperate, Mediterranean, and generalist species since it did not improve model fits (ΔAIC > -2). Additionally, we included breeding range area in range change and distribution shift models, assuming that species with larger ranges would exhibit larger shifts. We scaled predictors to a mean of 0 and standard deviation of 1 to facilitate effect size comparisons. We adjusted p-values using the Holm method (for n=3) to account for multiple testing of traits and specialisation indices on three response variables.</p>
Projected climatic changes lead to biome changes in areas of previously constant biome
<p class="Indentedmaintext"><b>Aim: </b>Recent studies in southern Africa identified past biome stability as an important predictor of biodiversity. We aimed to assess the extent to which past biome stability predicts present global biodiversity patterns, and the extent to which projected climatic changes may lead to eventual biome changes in areas with constant past biome.</p> <p class="Indentedmaintext"><b>Location: </b>Global.</p> <p class="Indentedmaintext"><b>Taxon: </b>Spermatophyta; terrestrial vertebrates.</p> <p class="Indentedmaintext"><b>Methods: </b>Biome constancy was assessed and mapped using results from 89 dynamic global vegetation model simulations, driven by outputs of palaeoclimate experiments spanning the past 140 ka. We tested the hypothesis that terrestrial vertebrate diversity is predicted by biome constancy. We also simulated potential future vegetation, and hence potential future biome patterns, and quantified and mapped the extent of projected eventual future biome change in areas of past constant biome.</p> <p class="Indentedmaintext"><b>Results: </b>Approximately 11% of global ice-free land had a constant biome since 140 ka. Aside from areas of constant Desert, many areas with constant biome support high species diversity. All terrestrial vertebrate groups show a strong positive relationship between biome constancy and vertebrate diversity in areas of greater diversity, but no relationship in less diverse areas. Climatic change projected by 2100 commits 46–66% of global ice-free land, and 34–52% of areas of past constant biome (excluding areas of constant Desert) to eventual biome change.</p> <p class="Indentedmaintext"><b>Main conclusions: </b>Past biome stability strongly predicts vertebrate diversity in areas of higher diversity. Future climatic changes will lead to biome changes in many areas of past constant biome, with profound implications for biodiversity conservation. Some projected biome changes will result in substantial reductions in biospheric carbon sequestration and other ecosystem services.</p>
Geographical trends of soil-associated biodiversity changes due to tree plantations in South America: biome and climate constraints revealed through meta-analysis
<p><strong>Aim</strong></p> <p>Evaluate the interaction between climate and biome structure when explaining changes in species richness of soil-associated communities due to tree plantations developed in different biomes. Compare the response of plants, soil invertebrates, and soil microorganisms, and test whether they should be considered sensitive-coupled biotas. Location Continental South America.</p> <p><strong>Time period</strong></p> <p>1996–2023 </p> <p><strong>Major taxa studied </strong></p> <p>Plants, soil invertebrates and soil microorganisms </p> <p><strong>Methods </strong></p> <p>Through a meta-analysis, the change in species richness (i.e., response ratio) associated with tree plantations was evaluated in 127 points of study across South America, considering soil-associated communities of plants, invertebrates and microorganisms. The influence of biome structure (open vs. closed habitats) on the response ratio and its interaction with the actual evapotranspiration (AET) and temperature seasonality was evaluated. Differentiated responses of different taxa were tested by comparing models with and without an interaction term referring to the taxon studied. The regional agricultural cover and plantation age were considered as anthropogenic variables.</p> <p><strong>Results </strong></p> <p>Models containing the AET were better at explaining the trend of change in species richness than those with temperature seasonality. The response to the change in species richness was oppositely related to the AET in open and closed biomes. Plants presented a higher loss in species richness than soil invertebrates and microorganisms. The three taxa were positively associated with AET, while seasonality was not relevant in any case. Both anthropogenic variables significantly lessened the change in species richness in all models. </p> <p><strong>Main conclusions</strong></p> <p>The structural contrast between the anthropogenic habitat and the biome where it is developed is a key factor influencing the response of soil-associated communities to tree plantations. Nevertheless, its influence must be assessed together with climatic and anthropogenic variables given that their interaction can explain different geographical trends in the change in species richness across regions.</p>
Regional Biomes outperform broader spatial units in capturing biodiversity responses to land-use change
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Projected climatic changes lead to biome changes in areas of previously constant biome
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Geographical trends of soil-associated biodiversity changes due to tree plantations in South America: biome and climate constraints revealed through meta-analysis
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Data from: Protected areas buffer the Brazilian semi-arid biome from climate change
The Caatinga is a botanically unique semi-arid ecosystem in northeast Brazil whose vegetation is adapted to the periodic droughts that characterize this region. However, recent extreme droughts events caused by anthropogenic climate change have challenged its ecological resilience. Here, we evaluate how deforestation and protection status affect the response of the Caatinga vegetation to drought. Specifically, we compared vegetation responses to drought in natural and deforested areas as well as inside and outside protected areas, using a time-series of satellite-derived Normalized Difference Vegetation Index (NDVI) and climatic data for 2008–2013. We observed a strong effect of deforestation and land protection on overall vegetation productivity and in productivity dynamics in response to precipitation. Overall, deforested areas had significantly lower NDVI and delayed greening in response to precipitation. By contrast, strictly protected areas had higher productivity and considerable resilience to low levels of precipitation, when compared to sustainable use or unprotected areas. These results highlight the importance of protected areas in protecting ecosystem processes and native vegetation in the Caatinga against the negative effects of climate change and deforestation. Given the extremely small area of the Caatinga currently under strict protection, the creation of new conservation areas must be a priority to ensure the sustainability of ecological processes and to avoid further desertification.
Datasets associated with Dale et al. 2022 Diversification and trait evolution in New Zealand woody lineages across changing biomes
<p>Biome occupancy and trait datasets for New Zealand <em>Melicytus, Myrsine</em>, and <em>Pseudopanax</em> species used in Dale et al. 2022 Diversification and trait evolution in New Zealand woody lineages across changing biomes (https://doi.org/10.1080/03036758.2022.2108071). The datasets are biomes occupied, Specific Leaf Area, stem density, leaf nutrient content (N, P, K), and cold sensitivity. "dataset info.txt" outlines the variable names and their units.</p>
Data from: Protected areas buffer the Brazilian semi-arid biome from climate change
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Data from: An operational definition of the biome for global change research
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In transition: avian biogeographic responses to a century of climate change across desert biomes
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Data from: Defining functional biomes and monitoring their change globally
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