Ecological Indicators (Oct 2024)
Surprisingly good fit of pressure-based cropland condition map and bird census data at the national scale
Abstract
As the world is struggling to halt the rapid decline of biodiversity, the assessment and mapping of ecosystem condition is getting ever increasing attention. Croplands are artificial ecosystems, but as they occupy a large portion of land, they significantly influence biodiversity. Yet, there is a knowledge gap about their suitability to support and maintain wildlife on a national scale. Large-scale condition mapping is meant to address this gap; the good condition of croplands includes their ability to support biodiversity. However, the lack of suitable databases is often a challenge when creating such maps. As there is a strong causal relation between pressures on the ecosystem and its condition, pressure indicators can be used as proxies to approximate condition when more direct indicators are lacking. The validation of condition maps based on pressure proxies is a key but challenging step. In this study, we tested a previously designed pressure-based cropland condition map for Hungary using bird census data. Besides validating the composite condition indicator, we also tested some key elements of the mapping process, such as the choice of variables and thresholds.Using multiple comparisons of means by Tukey’s contrast and Random Forest modelling, we examined the relationship of (1) the continuous pressure-based cropland condition variables, (2) their rescaled, ordinal version (called sub-indicators), and (3) the composite cropland condition indicator (sum of the sub-indicators) with a biodiversity measure, the standardised relative richness of characteristic farmland bird species (rRRCS). To get a picture of the spatial patterns of the examined relationships across Hungary, individual Random Forest models were constructed for all the spatial units of the bird census database, using focal analysis with a 30 km radius moving window.We found significant differences in the mean rRRCS for nearly all sub-indicator categories, signifying that the literature-derived thresholds were mostly sound. Categories with higher (better) condition scores had higher mean rRRCS; the differences are significant in the mid-range but not in the extreme categories, indicating a need for a meaningful simplification of the categories. The goodness-of-fit (R2) of the Random Forest models was found to be high, but it is spatially heterogeneous (ranged from 0.69 to 0.89, with a median value of 0.81), similarly to the variable importance. The proportion of semi-natural areas proved to be the most important condition variable. The proportion of maize and alfalfa were more important than parcel size. Our results show that condition maps based on pressure proxies can reflect patterns of biodiversity surprisingly well. They also highlight the spatial context dependence of the uncertainty of condition maps.
