Regional avian species declines estimated from volunteer‐collected long‐term data using List Length Analysis

Abstract

Long-term systematic population monitoring data sets are rare but are essential in identifying changes in species abundance. In contrast, community groups and natural history organizations have collected many species lists. These represent a large, untapped source of information on changes in abundance but are generally considered of little value. The major problem with using species lists to detect population changes is that the amount of effort used to obtain the list is often uncontrolled and usually unknown. It has been suggested that using the number of species on the list, the "list length," can be a measure of effort. This paper significantly extends the utility of Franklin's approach using Bayesian logistic regression. We demonstrate the value of List Length Analysis to model changes in species prevalence (i.e., the proportion of lists on which the species occurs) using bird lists collected by a local bird club over 40 years around Brisbane, southeast Queensland, Australia. We estimate the magnitude and certainty of change for 269 bird species and calculate the probabilities that there have been declines and increases of given magnitudes. List Length Analysis confirmed suspected species declines and increases. This method is an important complement to systematically designed intensive monitoring schemes and provides a means of utilizing data that may otherwise be deemed useless. The results of List Length Analysis can be used for targeting species of conservation concern for listing purposes or for more intensive monitoring. While Bayesian methods are not essential for List Length Analysis, they can offer more flexibility in interrogating the data and are able to provide a range of parameters that are easy to interpret and can facilitate conservation listing and prioritization.

Quantifying the sampling error in tree census measurements by volunteers and its effect on carbon stock estimates

“…Several surveys in the United Kingdom, United States, and Australia have used volunteer-collected data for moths, plants, amphibians, marine species, and birds (Greenwood 2007, Lotz and Allen 2007, Delaney et al 2008, Milberg et al 2008, Fox et al 2010, Szabo et al 2010; in 2008 the Ecological Society of America included a symposium to discuss the philosophy, Manuscript received 17 November 2011; revised 10 October 2012; accepted 21 November 2012. Corresponding Editor: S. Henderson. 5 E-mail: n.butt@uq.edu.au validity, and value of citizen science at that year's annual meeting (Cohn 2008), and in 2012 Frontiers in Ecology and the Environment dedicated a Special Issue to an exposition of new techniques and lessons learned from a range of different projects around the world (Henderson 2012).…”

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“…Consequently, the shorter the list the lower the assumed observation effort or the likelihood to report an observed species (Szabo et al 2010;van Strien et al 2013). With this function pj,d,t,i converges asymptotically to 1 as SLLj,d,t,i gets closer to ∞; however, note that pj,d,t,i will be lower than 1 even when SLL is equal to the local species richness.…”

“…We calculated the length of the list of observed species for each visit (Species List Length; SLL hereafter), later to be used as a measure of effort (Szabo et al 2010). For computational reasons, we restricted the maximum number of visits to 40 per day and site, prioritizing visits with the longest species lists.…”

“…Opportunistic data offer benefits in the form of a wide coverage at spatial and temporal scales (Suarez & Tsutsui 2004) and often a large number of repeated observations. However, opportunistic data are not collected in a standardised way and there are several potential sources of bias (Lukyanenko, Parsons & Wiersma 2016); absences of species are often not available as non-detections are frequently not reported, and corrections for variation in sampling effort are needed (Szabo et al 2010). …”

“…A common approach is to construct absences of species by compiling species lists for individual observers visiting sites. The length of species lists corresponding to observer visits to specific sites are then used as covariates for detection probability, as a proxy for sampling effort and tendency to report species (Szabo et al 2010;van Strien et al 2013;Isaac et al 2014).…”