WorldClim Data Research Articles

Signature of present and projected climate change at an urban scale: The case of Addis Ababa

Understanding climate change and variability at an urban scale is essential for water resource management, land use planning, development of adaption plans, mitigation of air and water pollution. However, there are serious challenges to meet these goals due to unavailability of observed and/or simulated high resolution spatial and temporal climate data. The statistical downscaling of general circulation climate model, for instance, is usually driven by sparse observational data hindering the use of downscaled data to investigate urban scale climate variability and change in the past. Recently, these challenges are partly resolved by concerted international effort to produce global and high spatial resolution climate data. In this study, the 1 km2 high resolution NIMR-HadGEM2-AO simulations for future projections under Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios and gridded observations provided by Worldclim data center are used to assess changes in rainfall, minimum and maximum temperature expected under the two scenarios over Addis Ababa city. The gridded 1 km2 observational data set for the base period (1950–2000) is compared to observation from a meteorological station in the city in order to assess its quality for use as a reference (baseline) data. The comparison revealed that the data set has a very good quality. The rainfall anomalies under RCPs scenarios are wet in the 2030s (2020–2039), 2050s (2040–2069) and 2080s (2070–2099). Both minimum and maximum temperature anomalies under RCPs are successively getting warmer during these periods. Thus, the projected changes under RCPs scenarios show a general increase in rainfall and temperatures with strong variabilities in rainfall during rainy season implying level of difficulty in water resource use and management as well as land use planning and management.

Enhancing the WorldClim data set for national and regional applications

Climatic change in the last few decades has had a widespread impact on both natural and human systems, observable on all continents. Ecological and environmental models using climatic data often rely on gridded data, such as WorldClim. The main aim of this study was to devise and evaluate a computationally efficient approach to produce new high resolution (100m) estimates of current and future climatic variables to be used at the national and regional scale. The test area was Great Britain, where local data are available and of good quality. Present and future climate surfaces were produced. For the present, the approach involved the integration, via spatial interpolation, of local climate information and WorldClim to reduce bias. For future climate scenarios the approach involved spatially downscaling of WorldClim (1km) to a finer resolution of 100m.The main advantages of the proposed approach are: 1. finer resolution, 2. locally adapted to the study area with use of higher number of meteorological stations and improved accuracy and bias, and 3. computationally efficient while making use of the existing resources provided by WorldClim.Two applications were presented to illustrate the practical consequences of improvements obtained with this method. The first is a measure of rainfall intensity, i.e. the R-factor, widely applied in erosion and catchment-scale studies. The second is an application to species distribution modelling, involving a range of bioclimatic variables. The results highlighted the importance of considering the spatial variability and structure of the data integrated in the modelling, and using data adapted to the geographical extent of the analysis, whenever possible.The results of the applications showed the advantage of using enhanced climatic data in applications such as the estimation of soil erosion, species range shift, carbon stocks and the provision of ecosystem services.

Soil water storage appears to compensate for climatic aridity at the xeric margin of European tree species distribution

Based on macroecological data, we test the hypothesis whether European tree species of temperate and boreal distribution maintain their water and nutrient supply in the more arid southern margin of their distribution range by shifting to more fertile soils with higher water storage than in their humid core distribution range (cf. soil compensatory effects). To answer this question, we gathered a large dataset with more than 200,000 plots that we related to summer aridity (SA), derived from WorldClim data, as well as soil available water capacity (AWC) and soil nutrient status, derived from the European soil database. The soil compensatory effects on tree species distribution were tested through generalized additive models. The hypothesis of soil compensatory effects on tree species distribution under limiting aridity was supported in terms of statistical significance and plausibility. Compared to a bioclimatic baseline model, inclusion of soil variables systematically improved the models’ goodness of fit. However, the relevance measured as the gain in predictive performance was small, with largest improvements for P. sylvestris, Q. petraea and A. alba. All studied species, except P. sylvestris, preferred high AWC under high SA. For F. sylvatica, P. abies and Q. petraea, the compensatory effect of soil AWC under high SA was even more pronounced on acidic soils. Soil compensatory effects might have decisive implications for tree species redistribution and forest management strategies under anthropogenic climate change. Therefore, soil compensatory effects deserve more intensive investigation, ideally, in studies combining different spatial scales to reduce the uncertainty associated with the precision of soil information.

Reynoutria niche modelling and protected area prioritization for restoration and protection from invasion: A Southeastern Europe case study

An important step in managing invasive species is determining the factors responsible for their current and potential distribution, especially when species distribution is climatically determined like in Reynoutria taxa case. The main aim of this paper was to integrate all available distribution data of the Reynoutria taxa in SE Europe and to predict in which habitats and along which corridors its future spread can be expected. Distribution data of the Reynoutria taxa were obtained from extensive field studies in the period from 2006 to 2016, as well as from the literature and herbarium sources. A total of 4081 localities in Serbia and the Kosovo region, Montenegro, Slovenia, Croatia, Bosnia and Herzegovina, Bulgaria and Romania have been recorded, most frequently in riparian and human-made habitats. Ecologically suitable ranges for the Reynoutria taxa were predicted using the MaxEnt program and 19 bioclimatic variables which were derived from the WorldClim data set. Results of this study showed that the most frequent Reynoutria taxa in SE Europe are R. japonica var. japonica and hybrid R.×bohemica. The most suitable geographical areas for R. japonica and R. sachalinensis are mostly located in the north, while for R.×bohemica the central part of SE Europe is suitable. The precipitation of the warmest quarter had a high influence on the potential distribution modelling of these closely related species. Potential distribution modelling revealed R.×bohemica’s high temperature tolerance in different seasons with high durability of drought (up to 60mm per year less precipitation than parental species). Moreover, this research predicts which areas are most likely to be invaded, and makes suggestions where to focus management efforts for survey and removal of these invaders, especially in protected areas. According to the future climate analysis R. japonica and R.×bohemica could expand their range in riparian habitats up to 30–40%. Consequently, invasion in Slovenia’s and Croatia’s rivers requires urgent preventative measures. This study provides empirical evidence that these plants could become widespread throughout SE Europe within 25 years under the current trend and emphasizes that regionally specific management plans should be implemented to prevent further spread of these taxa.

Climate change and population growth impacts on surface water supply and demand of Addis Ababa, Ethiopia

Addis Ababa is expected to experience water supply stress as a result of complex interaction of urbanization and climate change. The aim of this study is to investigate water demand and supply prospects for the City of Addis Ababa by applying the Water Evaluation and Planning (WEAP) hydrological model and using scenarios of population growth trends and climate change. The study includes analysis of water consumption, hydrological information and climate data which is statistically downscaled using approach used to generate climate data available at the Worldclim data center. Bias corrected climate model data of NIMR-HadGEM2-AO under a midrange RCP 4.5 scenario and RCP8.5, high emissions scenario was used for the study. The result shows that the projected population of Addis Ababa city using high population growth rate (3.3%) will be about 7 million by the year 2039. The climate change projections result under RCP 4.5 and RCP 8.5 scenarios on surface water supply shows that the level of reservoirs volume both at Legedadi/Dire and Gefersa reservoirs will be reduced in the projected years between the years 2023 and 2039. The result of the RCP 8.5 scenario with low population growth shows that the unmet water demand will be 257.28millionm3 in 2037. The result of the RCP 4.5 scenario with low population growth shows that the unmet water demand will be 314.91millionm3 in 2037. This indicates that the unmet water demand with the dry climate of RCP 4.5 climate change scenario is higher than RCP 8.5 scenario. Under the RCP 4.5 scenario with high population growth (3.3%) the unmet water demand is 87.42millionm3 in 2030, 158.38millionm3 in 2035 and 380.72millionm3 in 2037. This indicates that the unmet water demand in both high population growth and the dry climate of RCP 4.5 climate change scenario will lead to severe shortage of water in the city. The most effective management options are water tariff increasing, domestic water use technology efficiency improvement and water harvesting which give satisfactory result in mitigating unmet demand of climate change and population growth in the city.

Fine‐grain, large‐domain climate models based on climate station and comprehensive topographic information improve microrefugia detection

Large‐domain species distribution models (SDMs) fail to identify microrefugia, as they are based on climate estimates that are either too coarse or that ignore relevant topographic climate‐forcing factors. Climate station data are considered inadequate to produce such estimates, a viewpoint we challenge here.Using climate stations and topographic data, we developed three sets of large‐domain (450 000 km²), fine‐grain (50 m) temperature grids accounting for different levels of topographic complexity. Using these fine‐grain grids and the Worldclim data, we fitted SDMs for 78 alpine species over Sweden, and assessed over‐ versus underestimations of local extinction and area of microrefugia by comparing modelled distributions at species' rear edges. Accounting for well‐known topographic climate‐forcing factors improved our ability to model fine‐scale climate, despite using only climate station data. This approach captured the effect of cool air pooling, distance to sea, and relative humidity on local‐scale temperature, but the effect of solar radiation could not be accurately accounted for. Predicted extinction rate decreased with increasing spatial resolution of the climate models and with increasing number of topographic climate‐forcing factors accounted for. About half of the microrefugia detected in the most topographically complete models were not detected in the coarser SDMs and in the models calibrated from climate variables extracted from elevation only.Although major limitations remain, climate station data can potentially be used to produce fine‐grain topoclimate grids, opening up the opportunity to model local‐scale ecological processes over large domains. Accounting for the topographic complexity encountered within landscapes permits the detection of microrefugia that would otherwise remain undetected. Topographic heterogeneity is likely to have a massive impact on species persistence, and should be included in studies on the effects of climate change.

Comparison modeling for alpine vegetation distribution in an arid area.

Mapping and modeling vegetation distribution are fundamental topics in vegetation ecology. With the rise of powerful new statistical techniques and GIS tools, the development of predictive vegetation distribution models has increased rapidly. However, modeling alpine vegetation with high accuracy in arid areas is still a challenge because of the complexity and heterogeneity of the environment. Here, we used a set of 70 variables from ASTER GDEM, WorldClim, and Landsat-8 OLI (land surface albedo and spectral vegetation indices) data with decision tree (DT), maximum likelihood classification (MLC), and random forest (RF) models to discriminate the eight vegetation groups and 19 vegetation formations in the upper reaches of the Heihe River Basin in the Qilian Mountains, northwest China. The combination of variables clearly discriminated vegetation groups but failed to discriminate vegetation formations. Different variable combinations performed differently in each type of model, but the most consistently important parameter in alpine vegetation modeling was elevation. The best RF model was more accurate for vegetation modeling compared with the DT and MLC models for this alpine region, with an overall accuracy of 75% and a kappa coefficient of 0.64 verified against field point data and an overall accuracy of 65% and a kappa of 0.52 verified against vegetation map data. The accuracy of regional vegetation modeling differed depending on the variable combinations and models, resulting in different classifications for specific vegetation groups.

Assessment of soil organic carbon stocks under future climate and land cover changes in Europe

Soil organic carbon plays an important role in the carbon cycling of terrestrial ecosystems, variations in soil organic carbon stocks are very important for the ecosystem. In this study, a geostatistical model was used for predicting current and future soil organic carbon (SOC) stocks in Europe. The first phase of the study predicts current soil organic carbon content by using stepwise multiple linear regression and ordinary kriging and the second phase of the study projects the soil organic carbon to the near future (2050) by using a set of environmental predictors. We demonstrate here an approach to predict present and future soil organic carbon stocks by using climate, land cover, terrain and soil data and their projections. The covariates were selected for their role in the carbon cycle and their availability for the future model. The regression-kriging as a base model is predicting current SOC stocks in Europe by using a set of covariates and dense SOC measurements coming from LUCAS Soil Database. The base model delivers coefficients for each of the covariates to the future model. The overall model produced soil organic carbon maps which reflect the present and the future predictions (2050) based on climate and land cover projections. The data of the present climate conditions (long-term average (1950–2000)) and the future projections for 2050 were obtained from WorldClim data portal. The future climate projections are the recent climate projections mentioned in the Fifth Assessment IPCC report. These projections were extracted from the global climate models (GCMs) for four representative concentration pathways (RCPs). The results suggest an overall increase in SOC stocks by 2050 in Europe (EU26) under all climate and land cover scenarios, but the extent of the increase varies between the climate model and emissions scenarios.

Predicting soil respiration for the Qinghai-Tibet Plateau: An empirical comparison of regression models

Alpine ecosystems like the Qinghai-Tibet Plateau strongly respond to global warming. Their soils, containing large carbon stocks, release more carbon dioxide as a possible consequence. Reciprocally, this may intensify climate warming. The Qinghai-Tibet plateaús large and almost inaccessible terrain results in a general data scarcity for this area making the quantification of soil carbon dynamics challenging. The current study provides an area-wide estimation of soil respiration for the Qinghai-Tibet Plateau, which is a key region for climate change studies due to its size and sensitivity. We compared the ability of six regression models to predict soil respiration that were developed within different studies and are based on mean annual air temperature, mean annual precipitation and belowground biomass. We used the WorldClim data sets to approximate annual soil respiration on a regional scale. Compared to field measurements of soil respiration at single spots in different vegetation zones on the Qinghai-Tibet Plateau (max. 1876.63gCm−2year−1), our predicted results (max. 1765.13gCm−2year−1) appear to be consistent. The basic difference between grasslands and forests in soil respiration is indicated by all regression models, however, a more precise differentiation between vegetation types is only exhibited by the regression model based on mean annual precipitation. Overall, this model performs best for most and the largest vegetation zones. Nevertheless, the approximations of the model based on mean annual temperature by Raich and Schlesinger (1992) with a lower constant better represent the vegetation zone of the alpine steppe. With this spatial estimation of soil respiration at a regional scale, a basis for assessing an area-specific potential of greenhouse gas emissions on the Qinghai-Tibet Plateau is provided. Moreover, we quantify a complex soil ecological process for this data-scarce area.

Climatic marginality: a new metric for the susceptibility of tree species to warming exemplified by Fagus sylvatica (L.) and Ellenberg’s quotient

In the face of climate warming, maps of potential tree species distribution can support forest management planning at coarse scales. For evaluating future suitability, conditions at the rear edge, i.e. at the meridional and lower altitudinal limits of species distribution, are of particular importance. Therefore, we present the concept of climatic marginality (distance to the rear edge) as a metric for the susceptibility against climate warming. Using a statistic niche model fitted to observed and potential beech occurrence in ICP Forests Level I monitoring plots and WorldClim data, we evaluate the modelled xeric limit of European beech based on the Ellenberg’s climate quotient involving thresholds suggested by Ellenberg and other authors. The applicability of the marginality index was tested with independent study sites. Despite the limitations of niche modelling, estimated climatic thresholds of beech were well in accordance with textbook knowledge and recent research. The regional patterns of climatic marginality were plausible and more meaningful with respect to the rear edge compared to conventional niche model outputs. In terms of climatic marginality, most regions in Central Europe are far from the xeric limit of beech. Evaluation based on independently sampled sites indicated that inclusion of soil and topography (microclimate) may permit implications at the local scale, e.g. growth potential estimations.

Soil microorganisms behave like macroscopic organisms: patterns in the global distribution of soil euglyphid testate amoebae

AbstractAimPatterns of α‐ and β‐diversity of soil protist communities and the factors that shape them remain largely unknown. We undertook a world‐wide survey of forest litter to investigate the patterns of diversity in a group of testate amoebae. We aimed to assess: (1) whether there is a latitudinal gradient in α‐diversity, and (2) whether β‐diversity was correlated solely with environmental factors commonly used in soil biology research or if it was also independently explained by geographical barriers.LocationWorld‐wide.MethodsWe studied the diversity of Euglyphida, a common group of testate amoebae, in 35 samples of forest litter and moss samples from a global survey, using small subunit rRNA gene sequences. We assessed the relationship between sample α‐diversity and latitude using generalized additive models (GAM). Furthermore, we determined the relationships between community composition and geographical models (distance‐based Moran's Eigenvector Maps – db‐MEM) using Generalized UniFrac distances (GUniFrac). We also investigated the relationship between individual measured soil parameters, WorldClim data and diversity (alpha plus beta) using both raw data and synthetic variables obtained through principal components analysis.ResultsWe recorded 245 phylotypes belonging to 6 out of 7 known Euglyphida families, plus four novel deep clades. Euglyphid α‐diversity was positively correlated with temperature and negatively with latitude and litter C/N ratio. Euglyphida community structure was correlated with the spatial eigenvector Db‐MEM31, independently of all measured environmental variables. Db‐MEM31 corresponds to a natural barrier constituted by the Northern Hemisphere desert belt. Beta diversity was correlated with other environmental variables, such as pH, isothermality and temperature in the coldest month of the year.Main conclusionsSoil euglyphid α‐diversity displays a latitudinal gradient, and β‐diversity is not only correlated with climatic and physicochemical parameters but also with geographical barriers. Such patterns of diversity were until recently believed to be characteristic only for macroscopic organisms.

Modeling sensitivity to climate change and estimating the uncertainty of its impact: A probabilistic concept for risk assessment in forestry

Many uncertainties emerge dealing with future climate conditions and their possible impacts on forests. In this paper we suggest a probabilistic approach extending existing ensemble species distribution modeling concepts by addressing important sources of uncertainty. We exemplify our approach using European beech as the target tree species and bioclimatic predictors derived from WorldClim data. Model parameter uncertainty is represented by 1000 parameter samples from a Bayesian generalized linear model. Climate change impact (CCI) is based on 63 different climate model outputs using four RCP-scenarios (Representative Concentration Pathways) in addition to the parameter uncertainty. The proposed difference of the probability of occurrence pocc to a predefined threshold, allows for evaluation of parameter uncertainty as well as for the uncertainty of future climate and describes the changing niche position. Further, we suggest the probability that the probability of occurrence exceeds a predefined threshold (pexc) as a metric for the distance of a site to the niche edge. These metrics are unambiguously determinable, intuitive and evident. Stands at a central, a marginal and an intermediate niche position are taken to exemplify deduction and application of pocc and pexc. A regional exercise shows how a map of pexc may support forest management planning and decision making under severe uncertainty. A key advantage of our novel metrics is the reformulation of common species distribution model (SDM) outputs in terms of risk thereby accounting for two important sources of uncertainty.

Bioclimatology and Vegetation Series in Sicily: A Geostatistical Approach

Tackling the Sicilian woody vegetation as a case-study, this work aims to verify the correspondence between Rivas-Martinez's bioclimatic units and the main vegetation series in the Mediterranean region. Following this approach, one macrobioclimate and 25 bioclimatic type belts can be recognized in Sicily. By means of a geostatistical analysis based on WorldClim data sets, cartographic models of the distribution range of each single bioclimatic unit were obtained and combined with vegetation data, in order to develop a new regional spatial framework, integrating climatic and vegetation data. Fidelity of each vegetation unit to a given climatic range was then evaluated as percent distribution of the occupied surface within a given bioclimatic unit, while the predictive power of the WorldClim data sets was tested by using half of the spatial data of the processed vegetation units as independent variables. Our results suggest that: (1) any kind of numerical threshold used to define bioclimatic units is not ef...

Bioclimatic variables derived from remote sensing: assessment and application for species distribution modelling

Summary Remote sensing techniques offer an opportunity to improve biodiversity modelling and prediction world‐wide. Yet, to date, the weather station‐based WorldClim data set has been the primary source of temperature and precipitation information used in correlative species distribution models. WorldClim consists of grids interpolated from in situ station data recorded primarily from 1960 to 1990. Those data sets suffer from uneven geographic coverage, with many areas of Earth poorly represented. Here, we compare two remote sensing data sources for the purposes of biodiversity prediction: MERRA climate reanalysis data and AMSR‐E, a pure remote sensing data source. We use these data to generate novel temperature‐based bioclimatic information and to model the distributions of 20 species of vertebrates endemic to four regions of South America: Amazonia, the Atlantic Forest, the Cerrado and Patagonia. We compare the bioclimatic data sets derived from MERRA and AMSR‐E information with in situ station data and contrast species distribution models based on these two products to models built with WorldClim. Surface temperature estimates provided by MERRA and AMSR‐E showed warm temperature biases relative to the in situ data fields, but the reliability of these data sets varied in geographic space. Species distribution models derived from the MERRA data performed equally well (in Cerrado, Amazonia and Patagonia) or better (Atlantic Forest) than models built with the WorldClim data. In contrast, the performance of models constructed with the AMSR‐E data was similar to (Amazonia, Atlantic Forest, Cerrado) or worse than (Patagonia) that of models built with WorldClim data. Whereas this initial comparison assessed only temperature fields, efforts to estimate precipitation from remote sensing information hold great promise; furthermore, other environmental data sets with higher spatial and temporal fidelity may improve upon these results.

Distribution patterns in the native vascular flora of Iceland.

The aim of our study was to reveal biogeographical patterns in the native vascular flora of Iceland and to define ecological factors responsible for these patterns. We analysed dataset of more than 500,000 records containing information on the occurrence of vascular plants. Analysis of ecological factors included climatic (derived from WORLDCLIM data), topographic (calculated from digital elevation model) and geological (bedrock characteristics) variables. Spherical k-means clustering and principal component analysis were used to detect biogeographical patterns and to study the factors responsible for them. We defined 10 biotic elements exhibiting different biogeographical patterns. We showed that climatic (temperature-related) and topographic variables were the most important factors contributing to the spatial patterns within the Icelandic vascular flora and that these patterns are almost completely independent of edaphic factors (bedrock type). Our study is the first one to analyse the biogeographical differentiation of the native vascular flora of Iceland.

GlUV: a global UV‐B radiation data set for macroecological studies

Summary Macroecology has prospered in recent years due in part to the wide array of climatic data, such as those provided by the WorldClim and CliMond data sets, which has become available for research. However, important environmental variables have still been missing, including spatial data sets on UV‐B radiation, an increasingly recognized driver of ecological processes. We developed a set of global UV‐B surfaces (glUV) suitable to match common spatial scales in macroecology. Our data set is based on remotely sensed records from NASA's Ozone Monitoring Instrument (Aura‐OMI). Following a similar approach as for the WorldClim and CliMond data sets, we processed daily UV‐B measurements acquired over a period of eight years into monthly mean UV‐B data and six ecologically meaningful UV‐B variables with a 15‐arc minute resolution. These bioclimatic variables represent Annual Mean UV‐B, UV‐B Seasonality, Mean UV‐B of Highest Month, Mean UV‐B of Lowest Month, Sum of Monthly Mean UV‐B during Highest Quarter and Sum of Monthly Mean UV‐B during Lowest Quarter. We correlated our data sets with selected variables of existing bioclimatic surfaces for land and with Terra–MODIS Sea Surface Temperature for ocean regions to test for relations to known gradients and patterns. UV‐B surfaces showed a distinct seasonal variance at a global scale, while the intensity of UV‐B radiation decreased towards higher latitudes and was modified by topographic and climatic heterogeneity. UV‐B surfaces were correlated with global mean temperature and annual mean radiation data, but exhibited variable spatial associations across the globe. UV‐B surfaces were otherwise widely independent of existing bioclimatic surfaces. Our data set provides new climatological information relevant for macroecological analyses. As UV‐B is a known driver of numerous biological patterns and processes, our data set offers the potential to generate a better understanding of these dynamics in macroecology, biogeography, global change research and beyond. The glUV data set containing monthly mean UV‐B data and six derived UV‐B surfaces is freely available for download at: http://www.ufz.de/gluv.

Relative importance of climate, topography, and habitats for breeding wetland birds with different latitudinal distributions in the Czech Republic

Given species' vulnerability to climate change, land use change, and habitat loss, it is pertinent to examine how the distribution of a particular species is related to those factors. We assessed the use of climate, habitat, and topography data for modeling the distributions of 14 central European wetland birds, and compared the relative importance of these factors among bird groups with differing latitudinal distributions in Europe. We used the Third Atlas of Breeding Birds in the Czech Republic as a source of species distribution data. Variables were derived from Corine Land Cover, WorldClim, and Shuttle Radar Topography Mission (SRTM) data. Hierarchical partitioning and multiple logistic models identified climatic, topographical, and habitat predictors as important determinants of distribution for each of the species under study. However, the relative contributions of particular variables differed among the species. Climatic, topographical, and habitat factor groups also differed in their importance to latitudinal species groups. Our results indicated that wetland birds with range margins close to the Czech Republic were potentially limited by two different factors: climate conditions impact the southerly distributed species and the availability of suitable habitat affects the northerly distributed species. The accuracy of the study models varied from fair to high (the area under curve values was 0.60–0.89) and revealed negative correlations with the relative occurrence area. In this study, we propose that any difference in model performance is more attributable to data characteristics than to a species' geographical characteristics.

The body temperature of active amphibians along a tropical elevation gradient: patterns of mean and variance and inference from environmental data

Summary Tropical montane amphibians have been the focus of recent and crucial conservation efforts. These initiatives require understanding on how elevation influences amphibian body temperature beyond the simplistic assumption of a monotonical decrease with elevation. This study addresses patterns and potential for inference in this context. As elevation increases, mean body temperature (BT) of tropical montane amphibians decreases linearly, but intrapopulation variation (VAR) in BT increases exponentially. These relationships are influenced by biome structure, but display both local nuances and species‐specific remarks. Substrate temperature (ST) and BT hold a close relationship across elevation. The noise around this relationship is lowest in mid‐elevation cloud forests and maximum in the paramo, a biome above the tree line. The relationships between BT and ST, and between elevation and either BT or VAR, are valuable to infer general patterns of thermal ecology for amphibians and to highlight species‐specific exceptional cases. The BT of montane tropical amphibians can be estimated from temperature data collected at a scale compatible with the size and microhabitat of individual frogs. Estimates from elevation are valid as general trends that can be enhanced if natural history is taken into account. Worldclim data allow for rough inference only and have limited predictive power. A framework is proposed to study how the BT and VAR of amphibians change with elevation. This framework encompasses information on biome structure and natural history.

Response of switchgrass yield to future climate change

A climate envelope approach was used to model the response of switchgrass, a model bioenergy species in the United States, to future climate change. The model was built using general additive models (GAMs), and switchgrass yields collected at 45 field trial locations as the response variable. The model incorporated variables previously shown to be the main determinants of switchgrass yield, and utilized current and predicted 1 km climate data from WorldClim. The models were run with current WorldClim data and compared with results of predicted yield obtained using two climate change scenarios across three global change models for three time steps. Results did not predict an increase in maximum switchgrass yield but showed an overall shift in areas of high switchgrass productivity for both cytotypes. For upland cytotypes, the shift in high yields was concentrated in northern and north-eastern areas where there were increases in average growing season temperature, whereas for lowland cultivars the areas where yields were projected to increase were associated with increases in average early growing season precipitation. These results highlight the fact that the influences of climate change on switchgrass yield are spatially heterogeneous and vary depending on cytotype. Knowledge of spatial distribution of suitable areas for switchgrass production under climate change should be incorporated into planning of current and future biofuel production. Understanding how switchgrass yields will be affected by future changes in climate is important for achieving a sustainable biofuels economy.

The influence of input data quality in determining areas suitable for crop growth at the global scale – a comparative analysis of two soil and climate datasets

AbstractThe assessment of biophysical crop suitability requires datasets on soil and climate. In this study, we investigated the differences in topsoil properties for the dominant soil mapping units between two global soil datasets. We compared the ISRIC World Soil Information Center’s World Inventory of Soil Emissions Potential 5 by 5 arc min Soil Map of the World (ISRIC‐WISE 5by5 SMW ) with the Harmonized World Soil Database (HWSD) in 0.5 arc min. We also incorporated annual mean temperature and mean precipitation from two global climate datasets that were the WorldClim measurement‐based climate dataset and the Kiel Climate Model (KCM) modelled results of global climate from 1960 to 1990. We then applied a fuzzy logic approach using different combinations and resolutions of the datasets to determine the effects on the extent and distribution of suitable areas for 15 crops. We only used the spatially dominant soil class in the mapping units in the soil databases (resampled to the same resolution of 5 arc min), and we found that the estimates of topsoil properties (0–20 cm in ISRIC‐WISE and 0–30 cm in HWSD) of the seven analysed parameters were up to 40% lower in most of the HWSD than in the ISRIC‐WISE 5by5 SMW. Results from the KCM are 0.1 °C (1%) lower in mean global annual temperature and 20% higher in average global annual precipitation compared with the WorldClim data. The HWSD‐based runs resulted in 10% less crop‐suitable land than the ISRIC‐WISE 5by5 SMW‐based results. The KCM simulations predicted 1% less crop‐suitable land than the WorldClim model. Despite generalizations, our results demonstrate that discrepancies in crop suitable areas are largely due to the differences in the soil databases rather than to climate.