No abstract

Historically, where forest habitats are deemed as the pristine landscape state, anthropogenic habitats such as managed grasslands or open spaces are often perceived to be antagonistic and of secondary conservation priority. Traditionally, studies on biodiversity responses to ecological variation, i.e. edge effect, have mostly focused on forest habitats. Yet recently there has been increased attention on communities beyond the forest edge in an effort to better understand how interactions between forests and adjacent habitats may potentially affect regional biodiversity. However, in Europe and the Mediterranean basin (a biodiversity hotspot), areas with high landscape heterogeneity and high edge density, there is a paucity of studies analysing the community responses across forest and "beyond edge" habitats across ecotones. In a protected area of central Italy, we investigated the responses of ground-dwelling arthropods (Araneae [spiders], Chilopoda [centipedes] and Carabidae [ground beetles]), which were differentiated into habitat-specific guilds (forest, edge and grassland species) across a forest-grassland ecotone. We investigated the extent to which a habitat edge influenced communities of arthropods associated with either the forest or grassland, and how far from the edge this effect penetrated into each habitat. Twelve 150 m-transects perpendicular to a forest-grassland edge were established and arthropods were sampled at nine progressive distances across the ecotone. An indicator species analysis (ISA) was used to detect species significantly associated with forest, edge-belt or grassland habitats, which were assumed representative of the respective communities. Logistic models of indicator species richness and abundances were used to describe responses of grassland and forest communities across the ecological boundaries. We found that grassland and edge habitats had habitat specialists and higher species richness compared to the forest habitat. Moreover, the occurrence of grassland-specific species was influenced by the presence of an edge up to 15 m from the habitat border. In contrast forest-associated indicator species were not affected by proximity to the habitat edge, rather individuals typical of forest habitats tended to "spill over" into grassland habitats. These findings support the hypothesis that in a forest-grassland mosaic, forest species are less sensitive to an edge and influence the community beyond the forest edge and into the grassland more than the reverse, i.e. the effect was asymmetric. From these data, we estimated that a minimum grassland habitat width of 600 m is necessary for grassland species to maintain a core area that is relatively unaffected by the spillover of species from adjacent forest habitats. Incorporating the directional influences of adjacent communities on each other allows for an empirical 3 assessment of habitat vulnerability that doesn't a priori value the conservation of one habitat over another.

High-quality biodiversity inventories are key tools to develop effective conservation strategies, but financial resources devoted to systematic species inventories are usually limited. Different sampling strategies have been proposed to efficiently allocate such limited resources (i.e. accessibility-based, stratified random and grid samplings), but their effectiveness may depend on the aim of the survey. Our aim was to assess which approach can provide the best trade-off between sampling costs and accuracy in estimating both single species distribution and regional species set composition. We generated simulated species distribution data to compare costs and performances of the three sampling methods in assessing species distribution. When we aim at measuring species range (i.e. area of occupancy or extent of occurrence), or obtaining baseline ecological data for conservation assessments (i.e. niche breadth), grid sampling usually provided the best trade-off between performances and costs at both the species and regional levels. Otherwise, the stratified random sampling outperformed the other methods when we are interested in assessing the relative rarity (i.e. species frequency) of the species across the study area. Low quality distribution data can lead to heavily biased conclusions on biodiversity trends or impacts of environmental changes; our findings highlight that selecting the right sampling strategy is essential to obtain reliable estimates of both single species distribution and regional species set composition.

Noxious species, i.e., crop pest or invasive alien species, are major threats to both natural and managed ecosystems. Invasive pests are of special importance, and knowledge about their distribution and abundance is fundamental to minimize economic losses and prioritize management activities. Occurrence models are a common tool used to identify suitable zones and map priority areas (i.e., risk maps) for noxious species management, although they provide a simplified description of species dynamics (i.e., no indication on species density). An alternative is to use abundance models, but translating abundance data into risk maps is often challenging. Here, we describe a general framework for generating abundance-based risk maps using multi-year pest data. We used an extensive data set of 3968 records collected between 2003 and 2013 in Wisconsin during annual surveys of soybean aphid (SBA), an exotic invasive pest in this region. By using an integrative approach, we modelled SBA responses to weather, seasonal, and habitat variability using generalized additive models (GAMs). Our models showed good to excellent performance in predicting SBA occurrence and abundance (TSS = 0.70, AUC = 0.92; R = 0.63). We found that temperature, precipitation, and growing degree days were the main drivers of SBA trends. In addition, a significant positive relationship between SBA abundance and the availability of overwintering habitats was observed. Our models showed aphid populations were also sensitive to thresholds associated with high and low temperatures, likely related to physiological tolerances of the insects. Finally, the resulting aphid predictions were integrated using a spatial prioritization algorithm ("Zonation") to produce an abundance-based risk map for the state of Wisconsin that emphasized the spatiotemporal consistency and magnitude of past infestation patterns. This abundance-based risk map can provide information on potential foci of pest outbreaks where scouting efforts and prophylactic measures should be concentrated. The approach we took is general, relatively simple, and can be applied to other species, habitats and geographical areas for which species abundance data and biotic and abiotic data are available.