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110 results for “Spatially explicit”
Data from: Unraveling conflicting density- and distance-dependent effects on plant reproduction using a spatially-explicit approach
1. Density- and distance-dependent (DDD) mechanisms are important determinants of plant reproductive success (PRS). Different components of sequential PRS can operate either in the same or in different directions and thus reinforce or neutralize each other, and they may also operate at different spatial scales. Thus, spatially-explicit approaches are needed to detect such complex DDD effects across multiple PRS components and spatial scales. 2. To reveal DDD effects of different components of early PRS of the Iberian pear (Pyrus bourgaeana) sampled over three consecutive years, we used marked point pattern analysis. Our special interest is to identify conflicting processes that regulate populations at different spatial scales, e.g. whether DDD on fruit initiation and on fruit development acted in opposite directions. To evaluate the significance of observed mark correlation functions based on empirical data (e.g. fruiting success) we compared them to expectations given by spatially-explicit null models. 3. Diverse DDD processes affected several aspects of PRS in a variable extent over the three seasons. First, early fruit set was higher for individuals with more neighbors at small distances (i.e. up to 40m). However, late P. bourgaeana fruit set decreased with increasing number of nearby neighbors, but these effects canceled for overall fruit set that did not show DDD effects. Second, the absolute number of fruits produced (crop sizes) by trees showed positive density dependence in 2011 and 2012 but not in 2013. Finally, the total number of seeds produced did not show DDD effects, indicating that conflicting demographic processes can disrupt the initial spatial pattern of tree investment in reproduction. 4. Synthesis: Understanding complex spatial effects of density- and distance-dependent (DDD) processes requires dissection of component processes to attain the complete picture since contrasting DDD processes may be hidden behind a single cumulative measure of reproductive success. The combination of novel and classic mark correlation functions used here constitute a powerful spatially-explicit tool that can be broadly applied to unravel conflicting mechanisms of DDD regulating the persistence of sessile organisms at a range of spatial scales. Our findings help to explain why some authors failed to find expected DDD of PRS and highlight the importance of detailed multi-year field studies on plant reproductive success.
Data from: Spatially explicit models of dynamic histories: examination of the genetic consequences of Pleistocene glaciation and recent climate change on the American Pika.
A central goal of phylogeography is to identify and characterize the processes underlying divergence. One of the biggest impediments currently faced is how to capture the spatiotemporal dynamic under which a species evolved. Here we described an approach that couples species distribution models (SDMs), demographic and genetic models in a spatiotemporally explicit manner. Analyses of American Pika (Ochotona priniceps) from the sky islands of the central Rocky Mountains of North America are used to provide insights into key questions about integrative approaches in landscape genetics, population genetics and phylogeography. This includes (i) general issues surrounding the conversion of time-specific SDMs into simple continuous, dynamic landscapes from past to current, and (ii) the utility of SDMs to inform demographic models with deme-specific carrying capacities and migration potentials, as well as (iii) the contribution of the temporal dynamic of colonization history in shaping genetic patterns of contemporary populations. Our results support that the inclusion of a spatiotemporal dynamic is an important factor when studying the impact of distributional shifts on patterns of genetic data. Our results also demonstrate the utility of SDMs to generate species-specific predictions about patterns of genetic variation that account for varying degrees of habitat specialization and life-history characteristics of taxa. Nevertheless, the results highlight some key issues when converting SDMs for use in demographic models. Because the transformations have direct affects on the genetic consequence of population expansion by prescribing how habitat heterogeneity and spatiotemporal variation is related to the species-specific demographic model, it is important to consider alternative transformations when studying the genetic consequences of distributional shifts.
Spatially explicit models for decision-making in animal conservation and restoration
<p>Models are useful tools for understanding and predicting ecological patterns and processes. Under ongoing climate and biodiversity change, they can greatly facilitate decision-making in conservation and restoration and help designing adequate management strategies for an uncertain future. Here, we review the use of spatially explicit models for decision support and identify key gaps in current modelling in conservation and restoration. Of 650 reviewed publications, 217 publications had a clear management application and were included in our quantitative analyses. Overall, modelling studies were biased towards static models (79 %), towards the species and population level (80 %) and towards conservation (rather than restoration) applications (71 %). Correlative niche models were the most widely used model type. Dynamic models as well as the gene-to-individual level and the community-to-ecosystem level were underrepresented, and explicit cost optimisation approaches were only used in 10 % of the studies. We present a new model typology for selecting models for animal conservation and restoration, characterising model types according to organisational levels, biological processes of interest and desired management applications. This typology will help to more closely link models to management goals. Additionally, future efforts need to overcome important challenges related to data integration, model integration, and decision-making. We conclude with five key recommendations, suggesting that wider usage of spatially explicit models for decision support can be achieved by (1) developing a toolbox with multiple, easier-to-use methods, (2) improving calibration and validation of dynamic modelling approaches, and (3) developing best-practise guidelines for applying these models. Further, more robust decision-making can be achieved by (4) combining multiple modelling approaches to assess uncertainty, and (5) placing models at the core of adaptive management. These efforts must be accompanied by long-term funding for modelling and monitoring, and improved communication between research and practise to ensure optimal conservation and restoration outcomes.</p>
Global spatially explicit crop water consumption shows an overall increase of 9% for 46 agricultural crops from 2010 to 2020: Data and software
<p>This dataset comprises spatial and temporal data related to our analysis on blue and green water consumption (WC) of global crop production in high spatial resolution (5 arc-minutes – approximately 10 km at the equator) for the years 2020, 2010 and 2000.</p> <p><strong>Modelling water consumption of SPAM data<br></strong></p> <p>We use SPAM (Spatial Production Allocation Model) data, released by the International Food Policy research Institute (IFPRI). We use SPAM2020 data for the year 2020 (46 crops), SPAM2010 data for the year 2010 (42 crops) and SPAM2000 data for the year 2000 (20 crops).</p> <p>We develop a Python-based global gridded crop green and blue WC assessment tool, entitled <em>CropGBWater. </em>Operating on a daily time scale, CropGBWater dynamically simulates rootzone water balance and related fluxes. We provide this model open access as <a href="https://zenodo.org/api/records/17059989/draft/files/Data_S10_CropGBWater_v02_1c-clean.ipynb/content">Data_S10</a> </p> <p>SPAM2020 crop data are modelled for the years 2018-2022, SPAM2010 crop data for the years 2008-2012 and SPAM2000 crop data for the years 1998-2002. We compute WCbl (blue WC) and WCgn (green WC), with components WCgn,irr (green WC of irrigated area) and WCgn,rf (green WC of rainfed area)<br><br><strong>File description:</strong></p> <p>The data-set consists of the following files:</p> <ul> <li>Data_S4: <a href="https://zenodo.org/records/15779747/files/Data_S4_Y2020_WC_m3_gridded.zip" target="_blank" rel="noopener">Data_S4_Y2020_WC_m3_gridded.zip</a><br>Folder with 46 individual crop grid files (5arc min resolution, with x & y coordinates), monthly and annual WCbl, WCgn,irr and WCgn,rf values in m3 in csv format, year 2020. Individual crop GIS-Rasters for annual m3 amounts are provided as Data_S17</li> <li>Data_S5: <a href="https://zenodo.org/records/15779747/files/Data_S5_Y2020_WC_mm_gridded.zip" target="_blank" rel="noopener">Data_S5_YR2020_WC_mm_gridded_csv</a><br>Folder with 46 individual crop grid files (5arc min resolution, with x & y coordinates), monthly and annual WCbl, WCgn,irr and WCgn, rf in mm as well as SPAM harvested area values in csv format, year 2020. Individual crop GIS-Rasters for annual mm amounts are provided as Data_S18</li> <li>Data_S6: <a href="https://zenodo.org/records/15779747/files/Data_S6_YR2020_WC_gridded_individual-crops-m3_annual.xlsx" target="_blank" rel="noopener">Data_S6_YR2020_WC_gridded_individual-crops-m3_annual.xlsx</a><br>One grid file (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in m3, differentiating between individual crops, year 2020. </li> <li>Data_S7: <a href="https://zenodo.org/records/15779747/files/Data_S7_YR2020_WC_gridded_sum-of-crops-m3_monthly-annual.csv" target="_blank" rel="noopener">Data_S7_YR2020_WC_gridded_sum-of-crops-m3_monthly-annual.csv</a><br>One grid file (5arc min resolution, with x & y coordinates) with monthly and annual WCbl, WCgn,irr and WCgn, rf values in m3, for the sum of all crops, year 2020</li> <li>Data_S8: <a href="https://zenodo.org/records/15779747/files/Data_S8_YR2000_WC_mm_m3_gridded.zip" target="_blank" rel="noopener">Data_S8_YR2000_WC_mm_m3_gridded.zip</a><br>Grid (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in mm and m3, as well as SPAM harvested area amounts, for each crop, year 2000</li> <li>Data_S9: <a href="https://zenodo.org/records/17059989/files/Data_S9_YR2010_WC_mm_m3_gridded.zip" target="_blank" rel="noopener">Data_S9_YR2010_WC_mm_m3_gridded.zip</a><br>Grid (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in mm and m3, as well as SPAM harvested area amounts, for each crop, year 2010</li> <li>Data_S10: <a href="https://zenodo.org/records/17059989/files/Data_S10_CropGBWater_v02_1c-clean.ipynb">Data_S10_CropGBWater_v02_1c-clean.ipynb</a> Python-based global gridded crop green and blue WC assessment tool, entitled <em>CropGBWater</em></li> </ul> <ul> <li>Data_S11: <a href="https://zenodo.org/records/15779747/files/Data_S11_YR2020_WC-mm_Rice_RiceAtlas.xlsx" target="_blank" rel="noopener">Data_S11_YR2020_WC-mm_Rice_RiceAtlas.xls</a><br>Grid (5arc min resolution, with x & y coordinates) with monthly and annual WCbl, WCgn,irr and WCgn, rf values in mm, for rice, year 2020, RiceAtlas calendar</li> <li>Data_S12: <a href="https://zenodo.org/records/15779747/files/Data_S12_INPUT_YR2020_cropcalendars.zip" target="_blank" rel="noopener">Data_S12_INPUT_YR2020_cropcalendars.zip</a><br>Modelling INPUT data for year 2020: Crop calendars</li> <li>Data_S13: <a href="https://zenodo.org/records/15779747/files/Data_S13_INPUT_YR2020_ET0.zip" target="_blank" rel="noopener">Data_S13_INPUT_YR2020_ET0.zip</a><br>Modelling INPUT data for year 2020: daily ET0</li> <li>Data_S14: <a href="https://zenodo.org/records/15779747/files/Data_S14_INPUT_YR2020_Precip.zip" target="_blank" rel="noopener">Data_S14_INPUT_YR2020_Precip.zip</a><br>Modelling INPUT data for year 2020: daily Precipitation</li> <li>Data_S15: <a href="https://zenodo.org/records/15779747/files/Soil.zip" target="_blank" rel="noopener">Data_S15_INPUT_YR2020_Soil.zip</a><br>Modelling INPUT data for year 2020: Soil</li> <li>Data_S16: <a href="https://zenodo.org/records/15779747/files/Data_S16_INPUT_YR2020_Climate-SPAM-grid_&_Scripts.zip" target="_blank" rel="noopener">Data_S16_INPUT_YR2020_Climate-SPAM-grid_&_Scripts.zip</a><br>Modelling INPUT for year 2020: Climate-SPAM-grid and different processing scripts</li> <li>Data_S17: <a href="https://zenodo.org/records/15779747/files/Data_S17_Y2020_WC_m3_GisRasters.zip" target="_blank" rel="noopener">Data_S17_Y2020_WC_m3_GisRasters</a><br>GIS-Rasters of individual crops for year 2020 (as well as the sum of all crops), values in m3 per year, differentiation between WCbl, WCgn,irr and WCgn</li> <li>Data_S18: <a href="https://zenodo.org/records/15779747/files/Data_S18_Y2020_WC_mm_GisRasters.zip" target="_blank" rel="noopener">Data_S18_Y2020_WC_mm_GisRasters</a><br>GIS-Rasters of individual crops for year 2020, values in mm per year, differentiation between WCbl, WCgn,irr and WCgn</li> </ul> <p><em>Please only use the latest version of this zenodo repository</em></p> <p><strong>Publication:</strong></p> <p>For all details, please refer to the open access paper:</p> <p>Chukalla, A.D., Mekonnen, M.M., Gunathilake, D., Wolkeba, F.T., Gunasekara, B., Vanham, D. (2025) Global spatially explicit crop water consumption shows an overall increase of 9% for 46 agricultural crops from 2010 to 2020, Nature Food, Volume 6, <a href="https://doi.org/10.1038/s43016-025-01231-x" target="_blank" rel="noopener">https://doi.org/10.1038/s43016-025-01231-x</a></p> <p><strong>Funding:</strong></p> <p>This research, led by IWMI, a CGIAR centre, was carried out under the CGIAR Initiative on Foresight (<a href="www.cgiar.org/initiative/foresight/" target="_blank" rel="noopener">www.cgiar.org/initiative/foresight/</a>) as well as the CGIAR “Policy innovations” Science Program (<a href="www.cgiar.org/cgiar-research-porfolio-2025-2030/policy-innovations" target="_blank" rel="noopener">www.cgiar.org/cgiar-research-porfolio-2025-2030/policy-innovations</a>). The authors would like to thank all funders who supported this research through their contributions to the CGIAR Trust Fund (<a href="www.cgiar.org/funders" target="_blank" rel="noopener">www.cgiar.org/funders</a>).</p> <p><strong> </strong></p>
Data set for "Spatially explicit ecological modeling improves empirical characterization of dispersal"
<p>Data set used and created in the simulations, analysis and figures of the associated paper.</p>
Data from: How large spatially-explicit optimal reserve design models can we solve now? an exploration of current models’ computational efficiency
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Evaluating spatially explicit sharing-sparing scenarios for multiple environmental outcomes
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Data from: Integrating genetic analysis of mixed populations with a spatially-explicit population dynamics model
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Data from: Spatially explicit models of dynamic histories: examination of the genetic consequences of Pleistocene glaciation and recent climate change on the American Pika.
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Data from: Varying dataset resolution alters predictive accuracy of spatially explicit ensemble models for avian species distribution
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Spatially explicit models for decision-making in animal conservation and restoration
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Data from: Counting cats: spatially explicit population estimates of cheetah (Acinonyx jubatus) using unstructured sampling data
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Data from: Unraveling conflicting density- and distance-dependent effects on plant reproduction using a spatially-explicit approach
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Data from: Estimating range expansion of wildlife in heterogeneous landscapes: a spatially explicit state-space matrix model coupled with an improved numerical integration technique
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A spatially explicit model to simulate the population dynamics of gypsy moth (Lymantria dispar)
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Data from: The shift from plant–plant facilitation to competition under severe water deficit is spatially explicit
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Data from: Spatially explicit abundance estimation of a rare habitat specialist: implications for SECR study design
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Data from: A spatially explicit hierarchical model to characterize population viability
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A Spatially Explicit Model of Vegetation-Habitat Interactions on Barrier Beaches
ISLAND is a model designed to simulate annual changes in vegetation, geomorphology, water table depth, and average ground water salinity on a cross-sectional transect, running from ocean to lagoon, on a barrier beach. The model is composed of three submodels; (1) a vegetation submodel which simulates the development of a four "species" plant community consisting of two grasses, an annual and a perennial shrub, (2) a geomorphology submodel which simulates the redistribution of sand by water and wind, and (3) a ground water submodel which simulates the average depth of the water table and the salinity of the ground water. Information calculated in each of the submodels is reevaluated and made available to the other submodels after each time iteration. The model works on a one year time step and acts on five meter sections along the transect. It is assumed that each five meter section is homogeneous with respect to vegetation, height above sea level, water table depth, and ground water salinity.
Supplementary material 3 from: Vrebos D, Staes J, Broekx S, de Nocker L, Gabriels K, Hermy M, Liekens I, Marsboom C, Ottoy S, Van Der Biest K, van Orshoven J, Meire P (2020) Facilitating spatially-explicit assessments of ecosystem service delivery to support land use planning. One Ecosystem 5: e50540. https://doi.org/10.3897/oneeco.5.e50540
Calculation methods of the ecosystem services
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