Detecting long-term occupancy changes in Californian odonates from natural history and citizen science records

Abstract

In a world of rapid environmental change, effective biodiversity conservation and management relies on our ability to detect changes in species occurrence. While long-term, standardized monitoring is ideal for detecting change, such monitoring is costly and rare. An alternative approach is to use historical records from natural history collections as a baseline to compare with recent observations. Here, we combine natural history collection data with citizen science observations within a hierarchical Bayesian occupancy modeling framework to identify changes in the occupancy of Californian dragonflies and damselflies (Odonata) over the past century. We model changes in the probability of occupancy of 34 odonate species across years and as a function of climate, after correcting for likely variation in detection probability using proxies for recorder effort and seasonal variation. We then examine whether biological traits can help explain variation in temporal trends. Models built using only opportunistic records identify significant changes in occupancy across years for 14 species, with eight of those showing significant declines and six showing significant increases in occupancy in the period 1900-2013. These changes are consistent with estimates obtained using more standardized resurvey data, regardless of whether resurvey data are used individually or in conjunction with the opportunistic dataset. We find that species increasing in occupancy over time are also those whose occupancy tends to increase with higher minimum temperatures, which suggests that these species may be benefiting from increasing temperatures across California. Furthermore, these species are also mostly habitat generalists, whilst a number of habitat specialists display some of the largest declines in occupancy across years. Our approach enables more robust estimates of temporal trends from opportunistic specimen and observation data, thus facilitating the use of these data in biodiversity conservation and management.

Winners and losers over 35 years of dragonfly and damselfly distributional change in Germany

“…Moreover, since some citizen scientists are active year-round, CS data also tend to capture a larger proportion of the biological community, including rare species, than standardized data (Bradter et al 2018). As CS data has become more accessible, there has been simultaneous development of methods to analyze such data (van Strien et al 2013;Isaac et al 2014;Rapacciuolo et al 2017). Occupancy-detection models have emerged as one approach that is robust to different ways citizen scientists collect data, by explicitly modelling heterogeneity in survey effort and species detectability (Isaac et al 2014).…”

Geospatial Informatics Key to Recovering and Sharing Historical Ecological Data for Modern Use

“…Such data and collaboration frameworks have been called for since at least the 1990s (e.g., Davis, 1995). There are many recent examples of frameworks that integrate biological data and museum specimen collections with publicly available scientific data such as climate scenarios, land use, and remotely sensed imagery, and provide tools to promote visualization and in-depth analysis (e.g., Abbott and Broglie, 2005;Beaman and Cellinese, 2012;Rapacciuolo et al, 2017). The EcoEngine is one example of this kind of framework designed to address climate and land cover and land use change which span spatial and temporal dimensions.…”

Understanding ecological change across large spatial, temporal and taxonomic scales: integrating data and methods in light of theory

“…For instance, the application of hierarchical occupancy models to museum specimens and historical observations can vastly improve our estimates of the influence of selection on assemblages at the centennial scale (Tingley and Beissinger 2009, Tingley et al 2012, Iknayan et al 2014, Rapacciuolo et al 2017, Zeilinger et al 2017. For instance, the application of hierarchical occupancy models to museum specimens and historical observations can vastly improve our estimates of the influence of selection on assemblages at the centennial scale (Tingley and Beissinger 2009, Tingley et al 2012, Iknayan et al 2014, Rapacciuolo et al 2017, Zeilinger et al 2017.…”

“…Owing to their ability to account for different types of sampling bias, occupancy models are also becoming key to modeling longer-term data sources. For instance, the application of hierarchical occupancy models to museum specimens and historical observations can vastly improve our estimates of the influence of selection on assemblages at the centennial scale (Tingley and Beissinger 2009, Tingley et al 2012, Iknayan et al 2014, Rapacciuolo et al 2017, Zeilinger et al 2017. Although different approaches have traditionally been preferred to account for imperfect detection in paleoecological data (Alroy et al 2001, Sugita 2007a, b, Pirzamanbein et al 2014, hierarchical occupancy models are beginning to be applied to the fossil record for assemblage-level inference (Liow 2013).…”