Human disturbances affect the topology of food webs.

Networks describe nodes connected by links, with numbers of links per node, the degree, forming a range of distributions including random and scale-free. How network topologies emerge in natural systems still puzzles scientists. Based on previous theoretical simulations, we predict that scale-free food webs are favourably selected by random disturbances while random food webs are selected by targeted disturbances. We assume that lower human pressures are more likely associated with random disturbances, whereas higher pressures are associated with targeted ones. We examine these predictions using 351 empirical food webs, generally confirming our predictions. Should the topology of food webs respond to changes in the magnitude of disturbances in a predictable fashion, consistently across ecosystems and scales of organisation, it would provide a baseline expectation to understand and predict the consequences of human pressures on ecosystem dynamics.

Species traits, patch turnover and successional dynamics: when does intermediate disturbance favour metapopulation occupancy?

BACKGROUND: In fragmented landscapes, natural and anthropogenic disturbances coupled with successional processes result in the destruction and creation of habitat patches. Disturbances are expected to reduce metapopulation occupancy for species associated with stable habitats, but they may benefit species adapted to transitory habitats by maintaining a dynamic mosaic of successional stages. However, while early-successional species may be favoured by very frequent disturbances resetting successional dynamics, metapopulation occupancy may be highest at intermediate disturbance levels for species with mid-successional habitat preferences, though this may be conditional on species traits and patch network characteristics. Here we test this 'intermediate disturbance hypothesis' applied to metapopulations (MIDH), using stochastic patch occupancy simulation modelling to assess when does intermediate disturbance favour metapopulation occupancy. We focused on 54 virtual species varying in their habitat preferences, dispersal abilities and local extinction and colonization rates. Long-term metapopulation dynamics was estimated in landscapes with different habitat amounts and patch turnover rates (i.e. disturbance frequency). RESULTS: Equilibrium metapopulation occupancy by late-successional species strongly declined with increasing disturbance frequency, while occupancy by early-successional species increased with disturbance frequency at low disturbance levels and tended to level-off thereafter. Occupancy by mid-successional species tended to increase along with disturbance frequency at low disturbance levels and declining thereafter. Irrespective of habitat preferences, occupancy increased with the amount of habitat, and with species dispersal ability and colonisation efficiency. CONCLUSIONS: Our study suggests that MIDH is verified only for species associated with mid-successional habitats. These species may be particularly sensitive to land use changes causing either increases or decreases in disturbance frequency. This may be the case, for instance, of species associated with traditional agricultural and pastoral mosaic landscapes, where many species disappear either through intensification or abandonment processes that change disturbance frequency.

Integrative approaches to guide conservation decisions: Using genomics to define conservation units and functional corridors.

Climate change and increasing habitat loss greatly impact species survival, requiring range shifts, phenotypic plasticity and/or evolutionary change for long-term persistence, which may not readily occur unaided in threatened species. Therefore, defining conservation actions requires a detailed assessment of evolutionary factors. Existing genetic diversity needs to be thoroughly evaluated and spatially mapped to define conservation units (CUs) in an evolutionary context, and we address that here. We also propose a multidisciplinary approach to determine corridors and functional connectivity between CUs by including genetic diversity in the modelling while controlling for isolation by distance and phylogeographic history. We evaluate our approach on a Near Threatened Iberian endemic rodent by analysing genotyping-by-sequencing (GBS) genomic data from 107 Cabrera voles (Microtus cabrerae), screening the entire species distribution to define categories of CUs and their connectivity: We defined six management units (MUs) which can be grouped into four evolutionarily significant units (ESUs) and three (putatively) adaptive units (AUs). We demonstrate that the three different categories of CU can be objectively defined using genomic data, and their characteristics and connectivity can inform conservation decision-making. In particular, we show that connectivity of the Cabrera vole is very limited in eastern Iberia and that the pre-Pyrenean and part of the Betic geographic nuclei contribute the most to the species genetic diversity. We argue that a multidisciplinary framework for CU definition is essential and that this framework needs a strong evolutionary basis.

Power laws in species' biotic interaction networks can be inferred from co-occurrence data.

Inferring biotic interactions from species co-occurrence patterns has long intrigued ecologists. Yet recent research revealed that co-occurrences may not reliably represent pairwise biotic interactions. We propose that examining network-level co-occurrence patterns can provide valuable insights into community structure and assembly. Analysing ten bipartite networks of empirically sampled biotic interactions and associated species spatial distribution, we find that approximately 20% of co-occurrences correspond to actual interactions. Moreover, the degree distribution shifts from exponential in co-occurrence networks to power laws in networks of biotic interactions. This shift results from a strong interplay between species' biotic (their interacting partners) and abiotic (their environmental requirements) niches, and is accurately predicted by considering co-occurrence frequencies. Our work offers a mechanistic understanding of the assembly of ecological communities and suggests simple ways to infer fundamental biotic interaction network characteristics from co-occurrence data.