Soil biodiversity is essential for terrestrial ecosystems, influencing nutrient cycling, carbon sequestration, agricultural productivity, and resilience to environmental changes. Yet, it faces significant threats from land-use changes, pollution, agricultural intensification, and climate change. Effective predictive modeling tools are urgently needed to inform conservation and management strategies. Although species distribution models (SDMs) have been successful for aboveground biodiversity, their application to soil biodiversity is limited by scarce large-scale datasets with spatial mismatches between environmental data and soil habitats. Recent advances, including environmental DNA (eDNA) metabarcoding, now allow extensive multi-taxa assessments of soil biodiversity. Simultaneously, remote sensing technologies provide high-resolution spatial data, potentially overcoming traditional coarse-gridded environmental limitations. This study evaluates Earth Observation Foundation (EOF) models, deep learning models pretrained on massive remote sensing datasets to summarize earth observation images into embeddings, to predict multi-trophic soil biodiversity in the French Alps. We compare models using EOF-derived embeddings from orthophotos with coarse-gridded and high-quality in-situ variables. We modeled relative abundance for 51 trophic groups across seven taxa using Random Forest, Light Gradient Boosting Machine, and Artificial Neural Networks, evaluating four data configurations: coarse-gridded environmental data, high-quality in-situ data, EOF embeddings, and a hybrid embedding-tabular approach. High-quality in-situ climate and soil data consistently delivered the highest predictive accuracy, especially for microbial and fungal groups. EOF embeddings provided valuable spatial context but did not surpass in-situ data performance, showing partial redundancy. Integrating remote sensing data can enhance biodiversity modeling in areas lacking detailed in-situ measurements, underscoring their complementary role in ecological assessments. ### Competing Interest Statement The authors have declared no competing interest. Natura Connect, 101060429 OBSGESSION, 101134954 MIAI, ANR-19-P3IA-0003 OFB
Population models have not considered the problem of home-range settlement when the grain of the landscape is smaller than the home-range size. We present an individual-based model addressing this problem that combines age-structured population dynamics, optimal foraging and habitat selection. During home-range settlement each juvenile tries to maximize her fitness, which depends on the proportion of high-quality habitat in her home range. We assume that home ranges do not overlap, which can happen because the home range is defended as a territory or because individuals avoid areas used by conspecifics. We show that the population supported by the landscape at equilibrium, the carrying capacity of the landscape, decreases with the amount of low-quality habitat cover. However, this decrease is non-linear, the carrying capacity starts to decline only below a critical habitat threshold. Furthermore, when the home-range size is larger than the grain of the landscape, the carrying capacity declines faster when the habitat is fragmented. Therefore species with small home-ranges persist in instances where species with large home-ranges go deterministically extinct. Species with large population growth rates have low critical habitat sizes, and are more resilient to habitat conversion. ### Competing Interest Statement The authors have declared no competing interest.
Providing accurate estimates of uncertainty is key for the analysis, adoption, and interpretation of species distribution models. In this manuscript, through the analysis of data from an emblematic North American cryptid, I illustrate how Conformal Prediction allows fast and informative uncertainty quantification. I discuss how the conformal predictions can be used to gain more knowledge about the importance of variables in driving presences and absences, and how they help assess the importance of climatic novelty when doing future predictions.
