Resources and predation: drivers of sociality in a cyclic mesopredator

In socially fexible species, the tendency to live in groups is expected to vary through a trade-of between costs and benefts, determined by ecological conditions. The Resource Dispersion Hypothesis predicts that group size changes in response to patterns in resource availability. An additional dimension is described in Hersteinsson’s model positing that sociality is further afected by a cost–beneft trade-of related to predation pressure. In the arctic fox (Vulpes lagopus), group-living follows a regional trade-of in resources’ availability and intra-guild predation pressure. However, the efect of local fuctuations is poorly known, but ofers an unusual opportunity to test predictions that difer between the two hypotheses in systems where prey availability is linked to intra-guild predation. Based on 17-year monitoring of arctic fox and cyclic rodent prey populations, we addressed the Resource Dispersion Hypothesis and discuss the results in relation to the impact of predation in Hersteinsson’s model. Group-living increased with prey density, from 7.7% (low density) to 28% (high density). However, it remained high (44%) despite a rodent crash and this could be explained by increased benefts from cooperative defence against prey switching by top predators. We conclude that both resource abundance and predation pressure are factors underpinning the formation of social groups in fuctuating ecosystems.publishedVersio

An artificial intelligence approach to remotely assess pale lichen biomass

Although generally given little attention in vegetation studies, ground-dwelling (terricolous) lichens are major contributors to overall carbon and nitrogen cycling, albedo, biodiversity and biomass in many high-latitude ecosystems. Changes in biomass of mat-forming pale lichens have the potential to affect vegetation, fauna, climate and human activities including reindeer husbandry. Lichens have a complex spectral signature and terricolous lichens have limited growth height, often growing in mixtures with taller vegetation. This has, so far, prevented the development of remote sensing techniques to accurately assess lichen biomass, which would be a powerful tool in ecosystem and ecological research and rangeland management. We present a Landsat based remote sensing model developed using deep neural networks, trained with 8914 field records of lichen volume collected for >20 years. In contrast to earlier proposed machine learning and regression methods for lichens, our model exploited the ability of neural networks to handle mixed spatial resolution input. We trained candidate models using input of 1 × 1 (30 × 30 m) and 3 × 3 Landsat pixels based on 7 reflective bands and 3 indices, combined with a 10 m spatial resolution digital elevation model. We normalised elevation data locally for each plot to remove the region-specific variation, while maintaining informative local variation in topography. The final model predicted lichen volume in an evaluation set (n = 159) reaching an R2 of 0.57. NDVI and elevation were the most important predictors, followed by the green band. Even with moderate tree cover density, the model was efficient, offering a considerable improvement compared to earlier methods based on specific reflectance. The model was in principle trained on data from Scandinavia, but when applied to sites in North America and Russia, the predictions of the model corresponded well with our visual interpretations of lichen abundance. We also accurately quantified a recent historic (35 years) change in lichen abundance in northern Norway. This new method enables further spatial and temporal studies of variation and changes in lichen biomass related to multiple research questions as well as rangeland management and economic and cultural ecosystem services. Combined with information on changes in drivers such as climate, land use and management, and air pollution, our model can be used to provide accurate estimates of ecosystem changes and to improve vegetation-climate models by including pale lichen