Developing robust field survey protocols in landscape ecology: a case study on birds, plants and butterflies

Abstract

Sustainable land management requires scientists to provide reliable data on diversity distribution patterns. Resource restrictions limit the affordable sampling effort, both with respect to number of survey sites and amount of effort per site. We compared different levels of survey effort in a case study in Central Romania, varying the number of repeats per site and number of survey sites. Target taxa were plants, birds and butterflies. For plants, we surveyed three 10 m 2 plots and ten plots of 1 m 2 at each site. For birds, we used point counts and for butterflies Pollard walks, in both cases with four repeats. We fitted hierarchical community models to estimate true species richness per site. Estimates of true species richness per site strongly correlated with observed species richness. However, hierarchical community models yielded unrealistically high estimates of true species richness per site, hence we used observed richness for further analyses. For each species group, we compared diversity indices from subsets of the dataset with the full dataset. Findings obtained with a reduced survey effort reflected well those obtained with full effort. Moreover, we conducted a power analysis to assess how the number of survey sites affected the minimum detectable effect of landscape heterogeneity on species richness, and found there was an exponential decrease in the minimum detectable effect with increasing number of sites. In combination, our findings suggest that assessing broad 123Biodivers Conserv (2015) 24:33-46 DOI 10.1007 diversity patterns in abundant and readily detectable organisms may be possible with relatively low survey effort per site. Our study demonstrates the utility of conducting pilot studies prior to designing large-scale studies on diversity distribution patterns.

Characterizing social–ecological units to inform biodiversity conservation in cultural landscapes

“…Bird richness was estimated by conducting three 10-min point counts of singing males within each site (Loos et al, 2014b). Butterfly richness was assessed by conducting standard Pollard walks (Pollard & Yates, 1993) of 200 m length within a given site, repeated at four different times (Loos et al, 2014a).…”

“…local scale, 1 ha) species richness of birds, butterflies and plants using generalized linear mixed-effects models (GLMER) with Poisson's error distributions using the LME4 package in the R environment (Bates et al, 2014). Test data consisted of 35 randomly selected sites (17 in arable, 13 in pasture and 5 in forests) distributed across five villages, at which richness data on all three groups had been collected in 2011 (Loos et al, 2014b). The full models contained the following fixed effects (at the site level): heterogeneity (linear and quadratic terms), woody vegetation cover (linear and quadratic terms), TWI, heatload, main land cover type (forest, pasture, arable), as well as interaction terms between land cover type and heterogeneity, and land cover type and woody vegetation cover.…”

Spatially explicit power analysis for detecting occupancy trends for multiple species

“…, Loos et al. ), (2) the level of sampling effort required to detect an effect size with a desired degree of confidence (Barata et al. ), and (3) which sampling regime will likely have the highest chance at detecting a specified level of change (Sewell et al.…”

“…It is calculated by specifying the change of interest that one wishes to detect (known as the effect size), the acceptable type 1 error rate (false alarm rate), and the "natural" or background variation in the observed data, which is composed of stochastic environmental variation and observation error (counting error or detection error). Power analysis can inform (1) how likely it is that monitoring will detect important changes in a species distribution and/or abundance (Thorn et al 2011, Loos et al 2015, (2) the level of sampling effort required to detect an effect size with a desired degree of confidence (Barata et al 2017), and (3) which sampling regime will likely have the highest chance at detecting a specified level of change (Sewell et al 2012).…”

Changes in butterfly movements along a gradient of land use in farmlands of Transylvania (Romania)

“…Based on the responses, we averaged the mobility scores provided by the experts and grouped species into three classes of mobility (1-3: low; 4-6: medium; and 7-9: high). For each class, we then selected three species that we knew from a previous study to be relatively widespread and abundant in our study area (Loos et al 2014b). We considered Pieris rapae, the species complex Colias hyale/alfacariensis, and Pontia edusa as mobile species; Pieris napi, Coenonympha pamphilus, and Melitaea phoebe as species of intermediate mobility; and Glaucopsyche alexis, Aphantopus hyperantus, and Minois dryas as species of low mobility.…”