DOI: 10.5061/dryad.02v6wwqgn
title: Optimizing coordination and trade-offs between food security and biodiversity conservation goals

description: Balancing food security and biodiversity conservation—two often
 conflicting objectives—is essential for achieving global goals (e.g., SDG
 2 and 15; GBF Targets 1, 3, and 10). While previous studies have explored
 global or national-level trade-offs, there is a lack of spatially
 explicit, scenario-based planning frameworks at regional scales to
 reconcile cropland expansion and biodiversity conservation. The study
 develops a multi-objective spatial planning framework to assess how future
 cropland expansion may be optimized to reduce biodiversity impacts while
 ensuring food security in the northwestern dry geo-eco region of China.
 Using a random forest model trained with environmental, socio-economic and
 trend variables to project cropland expansion from 2020 to 2030 and
 identify areas of spatial conflict with biodiversity priority regions.
 Results reveal intense conflicts in ecologically sensitive areas such as
 the Altai and Tianshan Mountains. Under a food-security-first scenario,
 expanding 300,000 km² of cropland would result in 167,978 km² of conflict
 areas and a 12.20% habitat loss rate. In contrast, a biodiversity-priority
 scenario achieves only 199,782 km² of cropland expansion, reducing habitat
 loss to 2.39%. A trade-off coordination scenario offers an optimized
 balance, enabling 300,000 km² of cropland expansion while protecting 30%
 of biodiversity priority areas and limiting habitat loss to 3.52%. This
 study highlights a novel framework for integrating food security and
 biodiversity conservation, offering spatially explicit strategies to
 support region-specific sustainable land-use planning.
description: # Optimizing coordination and trade-offs between food security and
 biodiversity conservation goals Dataset DOI:
 [10.5061/dryad.02v6wwqgn](10.5061/dryad.02v6wwqgn) ## Description of the
 data and file structure This dataset was generated as part of the study
 "Optimizing Coordination and Trade-offs between Food Security and
 Biodiversity Conservation Goals" (submitted to Ecography). The data
 include two raster layers: A probability map of future cropland expansion
 in the northwestern dry geo-eco region of China for the period 2020–2030,
 produced using a Random Forest (RF) model trained with environmental,
 socio-economic, and trend variables. A biodiversity conservation priority
 map derived from spatial prioritization analysis based on area of habitat
 (AOH). These datasets were used to identify spatial conflicts between
 predicted cropland expansion and biodiversity priority areas, and to
 develop land-use planning scenarios under food security and biodiversity
 conservation objectives. ### Files and variables #### File:
 Probability_map_of_cropland_expansion_in_the_study_area_for_2020-2030.tif
 **Description:** Raster layer representing the modeled probability (range:
 0–1) of cropland expansion for each 1 km × 1 km grid cell within the study
 area for the period 2020–2030.\ Value: Probability of cropland expansion
 (unitless; 0 = no likelihood, 1 = maximum likelihood).\ Missing
 values: None. #### File:
 Biodiversity_conservation_priority_map_of_the_study_area.tif
 **Description:** Raster layer showing biodiversity conservation priority
 scores derived from a spatial prioritization analysis. Higher scores
 indicate higher priority for conservation action.\ Value: Conservation
 priority index (unitless; range: 0–1, with 1 being highest priority).\
 Missing values: None. ## Code/software Viewing software: Any GIS software
 capable of reading GeoTIFF format, such as ArcGIS Pro. Modeling software:
 * R (v4.3.1) with packages: ranger, raster, sp, rgdal, biovars, dismo. *
 ArcGIS Pro (v3.0+) for spatial processing (Euclidean Distance, slope
 extraction, raster resampling). * Zonation (v4.0) for biodiversity
 prioritization. Workflow: 1.Model training: * Time-lagged Random Forest
 (RF) modeling: predictor variables from an earlier period (e.g.,
 2000–2005) used to model expansion in the subsequent period (e.g.,
 2005–2015). * Training sample balancing: tested ratios of expansion to
 non-expansion grids (1:1, 1:2, …, all available points). * RF implemented
 with 500 trees using the ranger package in R. 2.Model evaluation:  *
 Predictions for 2010–2020 compared with observed expansion. * Evaluated
 using Overall Accuracy, Precision, Recall, F1 Score, Brier Score, AUC-ROC,
 and AUC-PRC. * Best-performing RF model selected based on combined
 performance metrics. 3.Probability mapping: Best RF model applied with
 2015–2020 predictor variables to project cropland expansion probability
 (0–1) for 2020–2030 across the Northwestern dry geo-eco region of China.
 4.Biodiversity prioritization: * Species Area of Habitat (AOH) maps from
 Lumbierres et al. (2022) were processed to 1 km resolution. * Zonation
 software applied with additive benefit function (ABF) and species weights
 based on IUCN Red List categories (CR: 8, EN: 6, VU: 4, NT/DD: 2, LC: 1).
 * Output: biodiversity conservation priority index map (0–1). 5.Conflict
 analysis:  Spatial overlay of cropland expansion probability map and
 biodiversity priority map to identify potential conflict zones under
 different land-use planning scenarios. ## Access information Data was
 derived from the following sources: * LULC: Nine major land cover classes
 (cropland, forest, shrub, grassland, water, snow/ice, barren, impervious,
 and wetland) were obtained from the China Land Cover Dataset (CLCD,
 https://zenodo.org/records/12779975). * Climate data: We obtained 1 km
 resolution datasets for China covering  temperature, precipitation,
 potential evapotranspiration, relative humidity, and average wind
 speed from the National Earth System Science Data Center
 (http://www.geodata.cn/). * Topography: Elevation data were obtained from
 WorldClim (https://www.worldclim.org/). * Soil: We used the Harmonized
 World Soil Database (HWSD v1.2) at 1 km resolution from the FAO
 (https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/). * Population: Population data at 1 km resolution for China were obtained from WorldPop (https://hub.worldpop.org/). * Roads: Road network data were sourced from OpenStreetMap (https://www.openstreetmap.org/), and Euclidean Distance analysis was used to calculate distance to roads.