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opencc-by-4.0Feb 2022View details →

Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions (v3.0)

Agricultural land resources – a global suitability evaluation (v3.0) Local climate, soil and topography determine the conditions under which agricultural crops are suitable for growth or not. The methodology uses a fuzzy logic approach that is described in Zabel et al. (2014). The approach is based on Liebig's law of the minimum. Accordingly, plant suitability is determined not by total available resources, but by the scarcest resource. The limiting factor depends on the local environmental conditions and the crop-specific requirements, that are taken from literature.  Determining Agricultural Suitability Agricultural suitability is calculated for each of 5 climate models (GFDL, HadGEM2, IPSL, MIROC and NorESM1) from the AR5 ISIMIP fast track protocol. Daily climate model data for temperature, precipitation and solar radiation are statistically downscaled to 30 arc seconds spatial resolution. A monthly bias-correction is applied using WorldClim data. The provided suitability data refers to the model median over the 5 climate simulations. Soil data is taken from the Harmonized World Soil Database (HWSD) v1.21. Considered soil properties are texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity. Soil depth is taken into account according to Pelletier et al. (2015). Topography data is applied from the Shuttle Radar Topography Mission (SRTM). Irrigation has strong impact on the suitability of crops and is considered in this approach. Agricultural Suitability The agricultural suitability data is provided at a spatial resolution of 30 arc seconds (approximately 1 km2 at the equator). The dataset contains four time periods (1980-2009, 2010-2039, 2040-2069, 2070-2099) and two climate change scenarios (RCP2.6 and RCP 8.5). Agricultural suitability is provided for rainfed conditions and for irrigated conditions seperately. Additionally, we provide a dataset in which the current irrigation areas according to Maier et al. (2018) are applied. The suitability is provided for 23 food, feed, fibre, and 1st and 2nd generation bio-energy crops. An 'overall suitability' is provided for all crops that considers the most suitable crop on each pixel. Additionally, we provide a dataset excluding 2nd generation bioenergy crops (18-23) from the overall aggregation of crops. <table> <caption>Food, feed, fiber and first-generation bioenergy crops</caption> <tbody> <tr> <td>Barley</td> <td>Potato</td> <td>Sugarbeet</td> </tr> <tr> <td>Cassava</td> <td>Rapeseed</td> <td>Sugarcane</td> </tr> <tr> <td>Groundnut</td> <td>Rice</td> <td>Sunflower</td> </tr> <tr> <td>Maize</td> <td>Rye</td> <td>Summer wheat</td> </tr> <tr> <td>Millet</td> <td>Sorghum</td> <td>Winter wheat</td> </tr> <tr> <td>Oilpalm</td> <td>Soybean</td> <td> </td> </tr> </tbody> </table> <table> <caption> Second-generation bioenergy crops </caption> <tbody> <tr> <td>Jatropha</td> <td>Reed canary grass</td> </tr> <tr> <td>Miscanthus</td> <td>Eucalyptus</td> </tr> <tr> <td>Switchgrass</td> <td>Willow</td> </tr> </tbody> </table> Growing Season Adaptation The agricultural suitability considers the adaptation of the growing season. For each pixel and crop, the growing season is optimized throughout the year, taking the annual course of precipitation, temperature, and solar radiation as well as their interplay, into account. Most Suitable Crop The most suitable crop for each pixel is provided in the data. Please note that a value of 126 means that no crop suitable and 127 means that multiple crops have the same suitability. Further information Detailled information are available in the following publications: Zabel F, Putzenlechner B, Mauser W (2014) Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions. PLOS ONE 9(9): e107522. doi: <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107522">10.1371/journal.pone.0107522</a> Cronin, J., Zabel, F., Dessens, O., Anandarajah, G. (2020): Land suitability for energy crops under scenarios of climate change and land-use. GCB Bioenergy, 12(8). doi: <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcbb.12697">10.1111/gcbb.12697</a> Schneider. J.M., Zabel, F., Mauser, W. (2022): Global inventory of suitable, cultivable and available cropland under different scenarios and policies. Scientific Data 9, 527. doi: <a href="https://doi.org/10.1038/s41597-022-01632-8">10.1038/s41597-022-01632-8</a> Meier, J., Zabel, F., Mauser, W. (2018): A global approach to estimate irrigated areas – a comparison between different data and statistics. Hydrol. Earth Syst. Sci., 22, 1119–1133, 2018. doi: <a href="https://hess.copernicus.org/articles/22/1119/2018/">10.5194/hess-22-1119-201</a> Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G.-Y., Williams, Z., Brunke, M. A., and Gochis, D. (2016), A gridded global data set of soil, immobile regolith, and sedimentary deposit thicknesses for regional and global land surface modeling, J. Adv. Model. Earth Syst., 8, 41– 65, doi: <a href="https://doi.org/10.1002/2015MS000526">10.1002/2015MS000526</a>. Improvements in v3.0 Compared to the previous version (<a href="https://zenodo.org/record/3748350">v2.0</a>), this version (v3.0) uses updated input data for soil (HWSD v1.21) and high resolution irrigated areas (Maier et al. 2018), and additionally considers soil depth (Pelletier et al. 2016). Moreover, the suitability is calculated for an ensemble of 5 climate models, and is available for more crops, including a number of second generation bioenergy crops. Contact Please contact: Dr. Florian Zabel, <a href="mailto:f.zabel@lmu.de">f.zabel@lmu.de</a>, Department of Geography, LMU München (<a href="http://www.geografie.uni-muenchen.de">www.geografie.uni-muenchen.de</a>)

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Comparing climatic suitability and niche distances to explain populations responses to extreme climatic events

Habitat suitability calculated from Species Distribution Models (SDMs) has been used to assess population performance, but empirical studies have provided weak or inconclusive support to this approach. Novel approaches measuring population distances to niche centroid and margin in environmental space have been recently proposed to explain population performance, particularly when populations experience exceptional environmental conditions that may place them outside of the species niche. Here, we use data of co-occurring species' decay, gathered after an extreme drought event occurring in the SE of the Iberian Peninsula which highly affected rich semiarid shrubland communities, to compare the relationship between population decay (mortality and remaining green canopy) and (1) distances between populations' location and species niche margin and centroid in the environmental space, and (2) climatic suitability estimated from frequently used SDMs (here MaxEnt) considering both the extreme climatic episode and the average reference climatic period before this. We found that both SDMs-derived suitability and distances to species niche properly predict populations performance when considering the reference climatic period; but climatic suitability failed to predict performance considering the extreme climate period. In addition, while distance to niche margins accurately predict both mortality and remaining green canopy responses, centroid distances failed to explain mortality, suggesting that indexes containing information about the position to niche margin (inside or outside) are better to predict binary responses. We conclude that the location of populations in the environmental space is consistent with performance responses to extreme drought. Niche distances appear to be a more efficient approach than the use of climate suitability indices derived from more frequently used SDMs to explain population performance when dealing with environmental conditions that are located outside the species environmental niche. The use of this alternative metrics may be particularly useful when designing conservation measures to mitigate impacts of shifting environmental conditions.

opencc-zeroAug 2022View details →

Figure 2 in Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics

Figure 2. Maxent predictions of suitable zebra mussel (Dreissena polymorpha) habitat in Texas. Shading indicates the logistic output of the Maxent model. Polygons represent state and national borders as well as major river basins within Texas.

Figure 1 in Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics

Figure 1. Physicochemical data survey lakes. Sites categorized by TPWD (at the time of this study in 2016) as "infested" (the water body has an established, reproducing population) or "positive" (zebra mussels or their larvae have been detected on more than one occasion despite lack of evidence of a fully established, reproducing population) are indicated by red triangles and included: Lakes Austin, Belton, Bridgeport, Dean Gilbert, Lavon, Lewisville, Ray Roberts, Stillhouse Hollow, Texoma, Travis, and Waco. Sites categorized by TPWD as zebra mussel "negative" are indicated by green circles and included: Lakes Aquilla, Buchanan, Georgetown, Granbury, Granger, Hubbard Creek, Inks, Lady Bird, LBJ, Limestone, Marble Falls, Palo Pinto, Pflugerville, Possum Kingdom, Proctor, and Whitney.

Figure 4 in Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics

Figure 4. Biplot of components 1 and 2 (top) and 1 and 3 (bottom) from Principal Component Analysis of water quality variables in 27 study lakes. Variables that predominated in each component (|factor loading| ≥ 0.50) are shown on the appropriate axes. Individual lake data are represented by symbols, with open circles representing lakes without previously reported incidences of zebra mussels (absent, 16 lakes), and solid circles those known to harbor the invasive species (present, 11 lakes) at the time of sampling (October 2016). No separation between the two lake groups is evident in either of the biplots. Ca, calcium, N, nitrogen; P, phosphorous.

Figure 3 in Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics

Figure 3. Maxent predictions of suitable quagga mussel (Dreissena bugensis) habitat in Texas. Shading indicates the logistic output of the Maxent model. Polygons represent state and national borders as well as major river basins within Texas.

Fig. 4 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus

Fig. 4 Area projected as suitable or unsutable under current and future (2081–2100) climatic conditions (km2) for the three tick species in comparison. a Ixodes ricinus. b Dermacentor reticulatus. c D. marginatus. The corresponding maps are shown in Figs. 1–3 in the main document. Future suitable conditions refers to the area (km2) projected as unsuitable under current climatic conditions but suitable under future climatic conditions (i.e., potential new range). Continuing suitable conditions refers to area (km2) projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable presence). Continuing unsuitable conditions refers to area (km 2) projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e. stable absence). Future unsuitable conditions refers to the area (km.2) projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction)

Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus

Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red: area projected as unsuitable under current climatic conditions but suitable under future climatic conditions (i.e., potential new range). AUC = 0.8333 (average over 10 replicates using cross-validation, standard deviation = 0.001113603). Threshold to transform the logistic model output: 0.3816 (10% omission rate threshold). Maps were built using ESRI ArcGIS (Release 10.7, www.esri.com). Projection: Europe Albers Equal Area Conic

Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus

Fig. 4 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus

Fig. 1 Projected future changes for Ixodes ricinus until 2081–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus

Fig. 1 Projected future changes for Ixodes ricinus until 2081–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red:

Fig. 5 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus

Fig. 5 Potential co-occurrence under current and future climatic conditions. a Under near current climatic conditions (1970–2000). b Under projected future climatic conditions (exemplarily for SSP 245) for the period 2041–2060. c Under projected future climatic conditions (SSP 245) for the period 2081–2100. Colors indicate areas where climatic suitability is projected for the respective species; for non-mentioned species ("none of them"), the area is climatically unsuitable according to the modelling results. The thresholds to transform the logistic model output (10% omission rate threshold) are as follows: 0.3368 for Ixodes ricinus, 0.3816 for Dermacentor reticulatus, and 0.4298 for D. marginatus. Maps were built using ESRI Arc-GIS (Release 10.7, www.esri.com). Projection: Europe Albers Equal Area Conic. (A hatch-based version of this figure is additionally provided in the Supplementary Material: Figure S11.)

Fig. 3 Projected future changes for Dermacentor marginatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus

Fig. 3 Projected future changes for Dermacentor marginatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red: area projected as unsuitable under current climatic conditions but suitable under future climatic conditions (i.e., potential new range). AUC = 0.8229 (average over 10 replicates using cross-validation, standard deviation = 0.001121953). Threshold to transform the logistic model output: 0.4298 (10% omission rate threshold). Maps were built using ESRI ArcGIS (Release 10.7, www.esri.com). Projection: Europe Albers Equal Area Conic

Figure 4 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method

Figure 4. Mean suitability for Arborophila crudigularis under baseline climate conditions and future climate scenarios. cccma and csiro represent two general circulation models; RCP2.6, and RCP8.5 represent two greenhouse gas emission scenarios; EN is entire suitable habitat; PR is presence records.

Figure 2 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method

Figure 2. Performance of each model for predicting the suitable habitat for Arborophila crudigularis. GLM: Generalized linear model; GBM: generalized boosting model; GAM: generalized additive model; CTA: classification tree analysis; ANN: artificial neural network; FDA: flexible discriminant analysis; MARS: multiple adaptive regression splines; RF: random forest; MAXENT: maximum entropy model.

Figure 6 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method

Figure 6. Changes in suitable habitat for Arborophila crudigularis under the RCP8.5 emission scenario. cccma and csiro represent two general circulation models.

Figure 5 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method

Figure 5. Changes in suitable habitat for Arborophila crudigularis under the RCP2.6 emission scenario. cccma and csiro represent two general circulation models.