Site-occupancy models may offer new opportunities for dragonfly monitoring based on daily species lists

Strien, A.J. van; Termaat, Tim; Groenendijk, Dick; Mensing, Victor; Kéry, Marc

doi:10.1016/j.baae.2010.05.003

Cited by 68 publications

(93 citation statements)

References 13 publications

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“…Van Strien et al (2010) tested the same procedure using dragonfly data, and found similar occupancy trends in opportunistic data as in independent monitoring data, thereby suggesting that the procedure worked well. We deduced nondetection records from the sightings of other butterfly species (see Methods).…”

Section: Perspectivesmentioning

confidence: 86%

“…Recently, dynamic site-occupancy models (MacKenzie et al 2006, Royle andKe´ry 2007) have also proven useful in estimating metapopulation parameters from opportunistic data (Ke´ry et al 2010b, van Strien et al 2010. For instance, in The Netherlands, butterflies have been studied by amateur and professional entomologists over a number of decades, and many records on the occurrence of species have been collected, but often without applying standardized field protocols.…”

Section: Introductionmentioning

confidence: 99%

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Metapopulation dynamics in the butterflyHipparchia semelechanged decades before occupancy declined in The Netherlands

Strien

Swaay

Kéry

2011

Ecological Applications

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The survival of many species in human-dominated, fragmented landscapes depends on metapopulation dynamics, i.e., on a dynamic equilibrium of extinctions and colonizations in patches of suitable habitat. To understand and predict distributional changes, knowledge of these dynamics can be essential, and for this, metapopulation studies are preferably based on long-time-series data from many sites. Alas, such data are very scarce. An alternative is to use opportunistic data (i.e., collected without applying standardized field methods), but these data suffer from large variations in field methods and search intensity between sites and years. Dynamic site-occupancy models offer a general approach to adjust for variable survey effort. These models extend classical metapopulation models to account for imperfect detection of species and yield estimates of the probabilities of occupancy, colonization, and survival of species at sites. By accounting for detection, they fully correct for among-year variability in search effort. As an illustration, we fitted a dynamic site-occupancy model to 60 years of presence-absence data (more precisely, detection-nondetection) of the heathland butterfly Hipparchia semele in The Netherlands. Detection records were obtained from a database containing volunteer-based data from 1950-2009, and nondetection records were deduced from database records of other butterfly species. Our model revealed that metapopulation dynamics of Hipparchia had changed decades before the species' distribution began to contract. Colonization probability had already started to decline from 1950 onward, but this was counterbalanced by an increase in the survival of existing populations, the result of which was a stable distribution. Only from 1990 onward was survival not sufficient to compensate for the further decrease of colonization, and occupancy started to decline. Hence, it appears that factors acting many decades ago triggered a change in the metapopulation dynamics of this species, which ultimately led to a severe decline in occupancy that only became apparent much later. Our study emphasizes the importance of knowledge of changes in survival and colonization of species in modern landscapes over a very long time scale. It also demonstrates the power of site-occupancy modeling to obtain important population dynamics information from databases containing opportunistic sighting records.

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Section: Perspectivesmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Metapopulation dynamics in the butterflyHipparchia semelechanged decades before occupancy declined in The Netherlands

Strien

Swaay

Kéry

2011

Ecological Applications

Self Cite

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“…In order to construct artificial data on non-detections we first consider each unique observer reporting at least one species at a site on a specific day to constitute a replicate visit within that day, following Kéry et al (2010) and van Strien et al (2010). Then, for each visit j, in day d, year t and site i any observation of the focal species was considered as a detection if the species was reported during the visit (yj,d,t,i = 1) and as a non-detection if it was not reported (yj,d,t,i = 0).…”

Section: Non-detection Recordsmentioning

confidence: 99%

Exploiting opportunistic observations to estimate changes in seasonal site use: an example with wetland birds

Ruete

Pärt

Berg

et al. 2016

Preprint

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Non-systematically collected, a.k.a. opportunistic, species observations are accumulating at a high rate in biodiversity databases. Occupancy models have arisen as the main tool to reduce effects of limited knowledge about effort in analyses of opportunistic data. These models are generally using long closure periods (e.g. breeding season) for the estimation of probability of detection and occurrence. Here we use the fact that multiple opportunistic observations in biodiversity databases may be available even within days (e.g. at popular birding localities) to reduce the closure period to one day in order to estimate daily occupancies within the breeding season. We use a hierarchical dynamic occupancy model for daily visits to analyse opportunistic observations of 71 species from nine wetlands during 10 years. Our model derives measures of seasonal site use within seasons from estimates of daily occupancy. Comparing results from our "seasonal site use model" to results from a traditional annual occupancy model (using a closure criterion of two months or more) showed that our model provide more detailed biologically relevant information. For example, when the aim is to analyse occurrences of breeding species, an annual occupancy model will over-estimate site use of species with temporary occurrences (e.g. migrants passing by, single itinerary prospecting individuals) as even a single observation during the closure period will be viewed as an occupancy. Alternatively, our model produce estimates of the extent to which sites are actually used. Model validation based on simulated data confirmed that our model is robust to certain changes and variability in sampling effort and species detectability. We conclude that more information can be gained from opportunistic data with multiple replicates (e.g. several reports per day almost every day) by reducing the time window of the closure criterion to acquire estimates of occupancies within seasons.

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“…Occupancy–detection models, which are derived from capture–recapture theory (MacKenzie ), have recently been successfully applied to large‐scale models of distribution (Lahoz‐Monfort, Guillera‐Arroita & Wintle ) and distributional change (van Strien et al . ; van Strien, van Swaay & Termaat ). The key feature of occupancy–detection modelling is the use of replicated visits within a season to estimate the conditional probability that a species is recorded when present.…”

Section: Introductionmentioning

confidence: 99%

Statistics for citizen science: extracting signals of change from noisy ecological data

et al. 2014

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Summary1. Policy-makers increasingly demand robust measures of biodiversity change over short time periods. Longterm monitoring schemes provide high-quality data, often on an annual basis, but are taxonomically and geographically restricted. By contrast, opportunistic biological records are relatively unstructured but vast in quantity. Recently, these data have been applied to increasingly elaborate science and policy questions, using a range of methods. At present, we lack a firm understanding of which methods, if any, are capable of delivering unbiased trend estimates on policy-relevant time-scales. 2. We identified a set of candidate methods that employ data filtering criteria and/or correction factors to deal with variation in recorder activity. We designed a computer simulation to compare the statistical properties of these methods under a suite of realistic data collection scenarios. We measured the Type I error rates of each method-scenario combination, as well as the power to detect genuine trends. 3. We found that simple methods produce biased trend estimates, and/or had low power. Most methods are robust to variation in sampling effort, but biases in spatial coverage, sampling effort per visit, and detectability, as well as turnover in community composition, all induced some methods to fail. No method was wholly unaffected by all forms of variation in recorder activity, although some performed well enough to be useful. 4. We warn against the use of simple methods. Sophisticated methods that model the data collection process offer the greatest potential to estimate timely trends, notably Frescalo and occupancy-detection models. 5. The potential of these methods and the value of opportunistic data would be further enhanced by assessing the validity of model assumptions and by capturing small amounts of information about sampling intensity at the point of data collection.

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Site-occupancy models may offer new opportunities for dragonfly monitoring based on daily species lists

Cited by 68 publications

References 13 publications

Metapopulation dynamics in the butterflyHipparchia semelechanged decades before occupancy declined in The Netherlands

Metapopulation dynamics in the butterflyHipparchia semelechanged decades before occupancy declined in The Netherlands

Exploiting opportunistic observations to estimate changes in seasonal site use: an example with wetland birds

Statistics for citizen science: extracting signals of change from noisy ecological data

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