Landscape context affects patch habitat contributions to biodiversity in agroecosystems

Effective conservation schemes are needed to advance dual objectives of biodiversity conservation and agronomic production in agricultural landscapes. Understanding how plant and arthropod taxa respond to both local habitat patch characteristics and landscape complexity is crucial for planning effective agri-environment schemes. This study investigated the relative effects of local (≤ 100 m from patch habitat center) and landscape (≤ 5 km from patch habitat center) variables on diversity of plants and arthropods within non-crop habitat patches (i) at different spatial extents ranging from 0.1 km to 5 km, while (ii) quantifying differential effects of local and landscape variables on unique components of diversity (i.e. species richness and abundance), and accounting for (iii) unique components of landscape extent (0.1, 0.5, 1, 2 and 5 km radii) and complexity (i.e. landscape composition and configuration). Landscape variables were significantly correlated with local plant and arthropod species richness and abundance at all spatial extents. Biodiversity responses to landscape variables were largely scale-dependent, as pairwise comparisons were significantly different between all spatial extents except between 1-km and 2-km extents, and correlations were lowest at the 5-km extent. Partial R-squared values for predicting local biodiversity were highest when both local and landscape variables were included as predictors of species richness and abundance, underscoring the importance of considering both local and landscape effects on local diversity. Landscape configuration variables accounted for more variation in plant and arthropod species richness than composition variables. However, models performed best when composition and configuration were considered together rather than alone, suggesting that both components of landscape complexity should be considered for identifying and managing conservation areas in crop fields. Conservation schemes that incentivize farmers to create or conserve small patch habitat within crop fields may be more effective when combined with landscape-scale designs that enhance landscape complexity across the Northern Great Plains. Local conservation efforts should be coordinated with landscape-level efforts to ultimately enhance biodiversity and desired ecosystem service outcomes across agricultural landscapes.

Methods
This landscape-scale study was conducted within large-scale wheat production systems in the Northern Great Plains. To quantify the relative importance of local and landscape variables explaining local diversity, local response variables and local explanatory variables were collected at the local scale, which was defined as ≤ 100 m from the patch habitat, or ecological refuge (ER) center. Landscape explanatory variables were extracted for spatially nested buffers at the landscape scale, which was defined as ≤ 5 km from ER center. As buffers were nested circles rather than concentric rings, larger spatial extents included smaller spatial extents. At the local spatial extent, arthropod and plant species richness and abundance were observed at 20, 40, 60, 80, and 100 m from the center of the ER, which included the crop field margin. Percent cover of crop field and ER were calculated at each local spatial extent. For landscape analysis, five nested circular buffers were created around each ER at spatial extents of 0.1, 0.5, 1, 2, and 5 km radii, respectively. Landscape composition data, including land cover type diversity, richness, and percent cover of each land cover type, and landscape configuration data, including cohesion, division, large patch dominance, total edge and number of patches, were extracted for every landscape spatial extent.

Explanatory and response variables observed at the local spatial extent for this study included percent cover of ER and crop field, plant species richness, plant abundance (i.e. percent cover), arthropod species richness, and arthropod abundance within 100 meters from the ER center. Local plant percent cover and species richness variables were collected along six 100-meter transects in a radial design from the ER center using ocular percent canopy cover for each species within 0.1m2 circular frames every 20 meters. Ocular percent canopy cover was evaluated by averaging two observers’ visual estimates of plant percent cover in each sampling frame. Arthropod data were collected using sweep net sampling at 20-meter increments along the same transects at each site and mean plant species richness and diversity were aggregated at the same 20-m scale. In the lab, arthropods were divided into taxonomic groups including Orthoptera, Hemiptera, Coleoptera, Diptera, Hymenoptera, Araneae, Odonata, and Lepidoptera (Duff, Maxwell, and Debinski, 2024). Arthropod individuals were identified as morphospecies within each taxonomic group based on visual similarities in form and structure and used as a practical surrogate for species classification. Plant and arthropod species richness were calculated for each 20-meter increment vegetative observation or sweep net sample using the vegan package in R (Oksanen et al., 2017).  All data from each transect were summed by taxonomic group (plants or arthropods) across the two years of data collection, and placed in the response variable matrix for analysis.

Landscape variable data were obtained from the Cropland Data Layer (USDA, 2023) and extracted for the five landscape spatial buffers surrounding each ER. The variables selected to represent landscape compositional heterogeneity in this study were land cover type diversity, number of land cover types, and the percent cover of agricultural cultivated land, undeveloped land, or developed land within each spatial extent. All landscape composition metrics were calculated using the vegan package in R (Oksanen et al., 2017). Land cover type richness was calculated as species richness, where each land cover type was considered a “species” type.  Landcover type diversity was calculated using Shannon Diversity Index, where each land cover type was considered a “species” type, and pixel abundance was considered as “species” abundance (Reynolds et al., 2018). Land cover types were manually classified as agricultural land, wildland or developed land.

The landscape variables selected to represent landscape configurational heterogeneity in this study included patch cohesion as a metric of connectedness within each spatial extent, patch division as a metric of patchiness within each spatial extent, total edge amount as a metric of fragmentation within each spatial extent, total number of patches as a metric of patchiness within each spatial extent, and Large Patch Index as a measure of the percentage of landscape covered by the dominant patch type within each spatial extent (McGarigal et al., 2012). All landscape configuration metrics were calculated from the Cropland Data Layer using the LandscapeMetric package in R (McGarigal et al., 2012) at 30-meter (0.09-hectare pixel) resolution for each landscape extent buffer.