Use of Large‐Scale Acoustic Monitoring to Assess Anthropogenic Pressures on Orthoptera Communities

Biodiversity monitoring at large spatial and temporal scales is greatly needed in the context of global changes. Although insects are a species‐rich group and are important for ecosystem functioning, they have been largely neglected in conservation studies and policies, mainly due to technical and methodological constraints. Sound detection, a nondestructive method, is easily applied within a citizen‐science framework and could be an interesting solution for insect monitoring. However, it has not yet been tested at a large scale. We assessed the value of a citizen‐science program in which Orthoptera species (Tettigoniidae) were monitored acoustically along roads. We used Bayesian model‐averaging analyses to test whether we could detect widely known patterns of anthropogenic effects on insects, such as the negative effects of urbanization or intensive agriculture on Orthoptera populations and communities. We also examined site‐abundance correlations between years and estimated the biases in species detection to evaluate and improve the protocol. Urbanization and intensive agricultural landscapes negatively affected Orthoptera species richness, diversity, and abundance. This finding is consistent with results of previous studies of Orthoptera, vertebrates, carabids, and butterflies. The average mass of communities decreased as urbanization increased. The dispersal ability of communities increased as the percentage of agricultural land and, to a lesser extent, urban area increased. Despite changes in abundances over time, we found significant correlations between yearly abundances. We identified biases linked to the protocol (e.g., car speed or temperature) that can be accounted for ease in analyses. We argue that acoustic monitoring of Orthoptera along roads offers several advantages for assessing Orthoptera biodiversity at large spatial and temporal extents, particularly in a citizen science framework. El Uso de Monitoreos Acústicos a Gran Escala para Estudiar las Presiones Antropogénicas sobre Comunidades de Orthoptera     

Identifying Conservation Areas on the Basis of Alternative Distribution Data Sets

Abstract:  Distribution data on biodiversity features is a major component of conservation planning that are often inaccurate; thus, the true distribution of each feature is commonly over- or underrepresented. The selection of distribution data sets may therefore lead to variability in the spatial configuration and size of proposed reserve networks and uncertainty regarding the extent to which these networks actually contain the biodiversity features they were identified to protect. Our goals were to investigate the impact on reserve selection of choosing different distribution data sets and to propose novel methods to minimize uncertainty about target attainment within reserves. To do so, we used common prioritization methods (richness mapping, systematic reserve design, and a novel approach that integrates multiple types of distribution data) and three types of data on the distribution of mammals (predicted distribution models, occurrence records, and a novel combination of the two) to simulate the establishment of regional biodiversity reserves for the state of Arizona (U.S.A.). Using the results of these simulations, we explored variability in reserve placement and size as a function of the distribution data set. Spatial overlap of reserve networks identified with only predicted distribution data or only occurrence distribution data never exceeded 16%. In pairwise comparisons between reserves created with all three types of distribution data, overlap never achieved 50%. The reserve size required to meet conservation targets also varied with the type of distribution data used and the conservation goal; the largest reserve system was 10 times the smallest. Our results highlight the impact of employing different types of distribution data and identify novel tools for application to existing distribution data sets that can minimize uncertainty about target attainment.