Plot-based butterfly surveys: statistical and methodological aspects

Hardersen, Sönke; Corezzola, Serena

doi:10.1007/s10841-014-9728-3

Cited by 11 publications

(3 citation statements)

References 43 publications

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“…A potential compromise between the required effort and data generated could be achieved through a fourth method—a plot‐based survey. In plot‐based surveys, entire plots (e.g., a whole patch or other predetermined areas) are searched for butterflies for a set amount of time (Grill et al, 2005; Hardersen & Corezzola, 2014; Kral, 2018; Levanoni et al, 2011; Marini et al, 2009; Taron & Ries, 2015). As the whole plot is zig‐zagged through, it might not have the same issues with poor species detection and underestimations of species abundance (Kadlec et al, 2012) as fixed transects.…”

Section: Introductionmentioning

confidence: 99%

“…Knowledge of the spatial distribution of a species can provide crucial information as to how a species utilises a habitat and can identify possible hotspots, for example, areas important for reproduction (Lewis & Hurford, 1997). While the method of plot‐based surveys has not yet been standardised, it has good potential to be (Hardersen & Corezzola, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Novel grid‐based population estimates correlate with actual population sizes of the marsh fritillary (Euphydryas aurinia), while transect and larvae counts are less reliable

Norman,

Säwenfalk,

Kindvall

et al. 2023

Ecological Entomology

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Established butterfly survey methods that are useful for monitoring species that are widely distributed and numerous may be less accurate for more rare species. We therefore need new monitoring approaches. We describe a plot‐based survey method, where butterflies or larvae nests are counted within 1‐ha grid cells. The aim was to compare this grid method with more traditional transect counts and evaluate both methods in relation to high‐quality capture–mark–release (CMR) population estimates (reflecting the ‘true’ population). We do this using data from a large population of the marsh fritillary butterfly in Sweden. Moreover, we followed the overall population trend from 2017 to 2021 for both adult butterflies and larvae. Results showed a higher detection probability using the grid method compared with transect counts, which for adult butterflies seem to be explained by time effort. Moreover, grid surveys of adult butterflies showed a clear significant relationship with the estimated ‘true’ population size from CMR, while transect counts did not. For larvae, both methods showed significant relationships with the estimated adult population size, but the grid method found 5.7 times more larvae. The overall population fluctuated significantly across years. In years with low densities, the transect method largely failed to detect the species. The grid method seems more reliable for detecting the marsh fritillary and for estimating its population size, and thus, tracking the population trend. We propose this novel method to be integrated into surveys and monitoring of biodiversity, especially when focusing on rare habitat specialists that are normally underrepresented in monitoring based on volunteer counts.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel grid‐based population estimates correlate with actual population sizes of the marsh fritillary (Euphydryas aurinia), while transect and larvae counts are less reliable

Norman,

Säwenfalk,

Kindvall

et al. 2023

Ecological Entomology

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“…To our knowledge, however, the use of SERs has not become routine (a recent ecological application can be found in Janisova et al 2014). Conversely, a plethora of recent studies (e.g.,Hardersen and Corezzola 2014;Siegloch et al 2014;Brazee et al 2014;Jung et al 2014;Giesecke et al 2014, Xu et al 2014aXu et al 2014b) and statistical software (see, for example,Oksanen et al 2015;Cardoso et al 2015) continue to use nonspatially-explicit rarefaction methods. In this paper, we aim at demonstrating how SERs can overcome the problem of spatial autocorrelation in the analysis of plant or animal communities.…”

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confidence: 99%

Incorporating spatial autocorrelation in rarefaction methods: Implications for ecologists and conservation biologists

Bacaro

Altobelli

Cameletti

et al. 2016

Ecological Indicators

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Recently, methods for constructing Spatially Explicit Rarefaction (SER) curves have been introduced in the scientific literature to describe the relation between the recorded species richness and sampling effort and taking into account for the spatial autocorrelation in the data. Despite these methodological advances, the use of SERs has not become routine and ecologists continue to use rarefaction methods that are not spatially explicit. Using two study cases from Italian vegetation surveys, we demonstrate that classic rarefaction methods that do not account for spatial structure can produce inaccurate results. Furthermore, our goal in this paper is to demonstrate how SERs can overcome the problem of spatial autocorrelation in the analysis of plant or animal communities. Our analyses demonstrate that using a spatially-explicit method for constructing rarefaction curves can substantially alter estimates of relative species richness. For both analyzed data sets, we found that the rank ordering of standardized species richness estimates was reversed between the two methods. We strongly advise the use of Spatially Explicit Rarefaction methods when analyzing biodiversity: the inclusion of spatial autocorrelation into rarefaction analyses can substantially alter conclusions and change the way we might prioritize or manage nature reserves

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Species Methods

Seber,

Schofield

2023

Statistics for Biology and Health

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Plot-based butterfly surveys: statistical and methodological aspects

Cited by 11 publications

References 43 publications

Novel grid‐based population estimates correlate with actual population sizes of the marsh fritillary (Euphydryas aurinia), while transect and larvae counts are less reliable

Novel grid‐based population estimates correlate with actual population sizes of the marsh fritillary (Euphydryas aurinia), while transect and larvae counts are less reliable

Incorporating spatial autocorrelation in rarefaction methods: Implications for ecologists and conservation biologists

Species Methods

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