Aim (1) To increase awareness of the challenges induced by imperfect detection, which is a fundamental issue in species distribution modelling; (2) to emphasize the value of replicate observations for species distribution modelling; and (3) to show how 'cheap' checklist data in faunal/floral databases may be used for the rigorous modelling of distributions by site-occupancy models.Location Switzerland.Methods We used checklist data collected by volunteers during 1999 and 2000 to analyse the distribution of the blue hawker, Aeshna cyanea (Odonata, Aeshnidae), a common dragonfly in Switzerland. We used data from repeated visits to 1-ha pixels to derive 'detection histories' and apply site-occupancy models to estimate the 'true' species distribution, i.e. corrected for imperfect detection. We modelled blue hawker distribution as a function of elevation and year and its detection probability of elevation, year and season.Results The best model contained cubic polynomial elevation effects for distribution and quadratic effects of elevation and season for detectability. We compared the site-occupancy model with a conventional distribution model based on a generalized linear model, which assumes perfect detectability (p = 1). The conventional distribution map looked very different from the distribution map obtained using site-occupancy models that accounted for the imperfect detection. The conventional model underestimated the species distribution by 60%, and the slope parameters of the occurrence-elevation relationship were also underestimated when assuming p = 1. Elevation was not only an important predictor of blue hawker occurrence, but also of the detection probability, with a bell-shaped relationship. Furthermore, detectability increased over the season. The average detection probability was estimated at only 0.19 per survey.Main conclusions Conventional species distribution models do not model species distributions per se but rather the apparent distribution, i.e. an unknown proportion of species distributions. That unknown proportion is equivalent to detectability. Imperfect detection in conventional species distribution models yields underestimates of the extent of distributions and covariate effects that are biased towards zero. In addition, patterns in detectability will erroneously be ascribed to species distributions. In contrast, site-occupancy models applied to replicated detection/non-detection data offer a powerful framework for making inferences about species distributions corrected for imperfect detection. The use of 'cheap' checklist data greatly enhances the scope of applications of this useful class of models.
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Climate and land-use changes are main drivers of insect declines, but their combined effects have not yet been quantified over large spatiotemporal scales. We analysed changes in the distribution (mean occupancy of squares) of 390 insect species (butterflies, grasshoppers, dragonflies), using 1.45 million records from across bioclimatic gradients of Switzerland between 1980 and 2020. We found no overall decline, but strong increases and decreases in the distributions of different species. For species that showed strongest increases (25% quantile), the average proportion of occupied squares increased in 40 years by 0.128 (95% credible interval: 0.123–0.132), which equals an average increase in mean occupancy of 71.3% (95% CI: 67.4–75.1%) relative to their 40-year mean occupancy. For species that showed strongest declines (25% quantile), the average proportion decreased by 0.0660 (95% CI: 0.0613–0.0709), equalling an average decrease in mean occupancy of 58.3% (95% CI: 52.2–64.4%). Decreases were strongest for narrow-ranged, specialised, and cold-adapted species. Short-term distribution changes were associated to both climate changes and regional land-use changes. Moreover, interactive effects between climate and regional land-use changes confirm that the various drivers of global change can have even greater impacts on biodiversity in combination than alone. In contrast, 40-year distribution changes were not clearly related to regional land-use changes, potentially reflecting mixed changes in local land use after 1980. Climate warming however was strongly linked to 40-year changes, indicating its key role in driving insect trends of temperate regions in recent decades.
Les tourbières des Vallées de la Brévine et des Ponts-de-Martel ont perdu plus de 90% de leur surface au 20e siècle suite à l'exploitation industrielle de la tourbe. Les travaux de revitalisation entrepris entre 1996 et 2018 ont permis d'y augmenter le nombre de plans d'eau de 240 à 341, leur surface passant de 1.3 à 10.1 hectares. Dès 2005, les odonates ont fait l'objet de suivis réguliers dans plusieurs marais. En 2017 et 2018, un suivi exhaustif a permis de recenser 38 espèces. L'ensemble des données récoltées entre 1938 et 2018 concerne ainsi 52 espèces, soit plus des 2/3 de la faune de Suisse. Parmi elles, neuf figurent sur la Liste Rouge nationale. Les espèces inféodées aux hauts-marais profitent des mesures de revitalisation et voient leur nombre augmenter depuis 2005. Leucorrhinia pectoralis s'est ainsi implantée de manière spectaculaire dans 12 hauts-marais sur 15, alors que L. albifrons, l'une des libellules les plus rares de Suisse, se reproduit dans une tourbière ayant fait l'objet d'importantes revitalisations. De même, les découvertes d'Aeshna subarctica et de Ceriagrion tenellum laissent présager leur implantation dans la région d'étude. Fort de ce bilan positif, le canton de Neuchâtel prévoit de poursuivre son programme de revitalisations au moins pour les cinq prochaines années. Parallèlement, seule une gestion coordonnée des différents marais visant à garantir une offre variée en habitats permettra le maintien des espèces les plus exigeantes.
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