A simulation study to quantify drift fence configuration and spacing effects when sampling mobile animals

Ellis, Marc H.; Bedward, Michael

doi:10.1890/es14-00078.1

Cited by 5 publications

(6 citation statements)

References 26 publications

(60 reference statements)

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“…The use of pitfall traps has been recognised as an important capture technique to improve species richness and abundance estimates of small mammals for two main reasons. First, pitfall traps do not depend on the attraction of animals to a bait (Sealander & James 1958, Williams & Braun 1983; they capture all animals that try to pass over each trap, and are especially effective if drift fences are used (Bury & Corn 1987, Ellis & Bedward 2014, Stromgren & Sullivan 2014. Second, the capture of one individual in a pitfall trap does not prevent the capture of other individuals, as is the case for livetraps, that may frequently be occupied by abundant and common species (Umetsu et al 2006).…”

Section: Discussionmentioning

confidence: 99%

Optimising sampling methods for small mammal communities in Neotropical rainforests

2017

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Quantifying mammalian biodiversity is a critical yet daunting challenge, particularly in species‐rich ecosystems. Non‐volant small mammals account for >60% of the mammalian diversity and often require several survey methods to estimate their species richness and abundance, because of differences in their size and behaviour. Using 117 studies at 278 sites in a species‐rich biome, the Brazilian Atlantic Forest, we determined the influence of trap configuration, trap type, and sampling effort on measures of species richness and abundance. We used generalised linear mixed‐effects models to determine which methodological approaches influenced estimates of species richness and abundance at the sampling sites. We used estimates of beta (β) to determine which methods improved species richness and abundance estimates, and generated predicted values for the overall species richness as a function of trap configuration (line transect, grid, or both), trap type (pitfall traps, live‐traps or both), and sampling effort (number of trap‐nights). Our results indicated that sites in which pitfall traps alone were used generated higher estimates of the overall small mammal species richness and abundance, and rodent abundance, than sites in which only live‐traps were used. Sites in which pitfall traps alone were used also produced higher estimates of species richness and abundance, and rodent species richness and abundance, than sites in which both trap types were used. Increased sampling effort led to increased estimates of species richness, but sites in which pitfall traps were used alone or together with live‐traps had higher estimates of species richness with less sampling effort than sites in which live‐traps only were used. Using pitfall traps greatly reduced the number of trap‐nights necessary to obtain a good estimate of small mammal species richness in a community. We found no influence of trap configuration (line transects or grids) on estimates of species richness, but the abundance of rodents was estimated to be higher in sites where both line transects and grids were used, than in sites where only one trap configuration was used. Our review shows the importance of using pitfall traps in research, monitoring, and environmental impact studies on species‐rich small mammal communities.

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Section: Discussionmentioning

confidence: 99%

Optimising sampling methods for small mammal communities in Neotropical rainforests

2017

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“…, Locey and White ). Recent simulation studies simulated the movement of one or two ground arthropod species specifically emphasizing the impact of the spatial arrangement of traps on efficiency and reliability of sampled densities (Crist and Wiens , Perner and Schueler , Ellis and Bedward ). These studies simulated the movement of virtual individuals in a homogeneous two‐dimensional landscape.…”

Section: Introductionmentioning

confidence: 99%

“…This parameterization effort, consequently, also proved to be a methodological frontier to analyses of how the species-specific sampling bias may affect community-level metrics, such as the species rank-abundance distribution (RAD) and Shannon diversity (McGill et al 2007, Locey andWhite 2013). Recent simulation studies simulated the movement of one or two ground arthropod species specifically emphasizing the impact of the spatial arrangement of traps on efficiency and reliability of sampled densities (Crist and Wiens 1995, Perner and Schueler 2004, Ellis and Bedward 2014. These studies simulated the movement of virtual individuals in a homogeneous two-dimensional landscape.…”

Section: Introductionmentioning

confidence: 99%

Pitfall trap sampling bias depends on body mass, temperature, and trap number: insights from an individual‐based model

et al. 2017

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Abstract. The diversity and community composition of ground arthropods is routinely analyzed by pitfall trap sampling, which is a cost-and time-effective method to gather large numbers of replicates but also known to generate data that are biased by species-specific differences in locomotory activity. Previous studies have looked at factors that influence the sampling bias. These studies, however, were limited to one or few species and did rarely quantify how the species-specific sampling bias shapes community-level diversity metrics. In this study, we systematically quantify the species-specific and community-level sampling bias with an allometric individual-based model that simulates movement and pitfall sampling of 10 generic ground arthropod species differing in body mass. We perform multiple simulation experiments covering different scenarios of pitfall trap number, spatial trap arrangement, temperature, and population density. We show that the sampling bias decreased strongly with increasing body mass, temperature, and pitfall trap number, while population density had no effect and trap arrangement only had little effect. The average movement speed of a species in the field integrates body mass and temperature effects and could be used to derive reliable estimates of absolute species abundance. We demonstrate how unbiased relative species abundance can be derived using correction factors that need only information on species body mass. We find that community-level diversity metrics are sensitive to the particular community structure, namely the relation between body mass and relative abundance across species. Generally, pitfall trap sampling flattens the rank-abundance distribution and leads to overestimations of ground arthropod Shannon diversity. We conclude that the correction of the species-specific pitfall trap sampling bias is necessary for the reliability of conclusions drawn from ground arthropod field studies. We propose bias correction is a manageable task using either body mass to derive unbiased relative abundance or the average speed to derive reliable estimates of absolute abundance from pitfall trap sampling.

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“…They were connected in a “Y” shape with plastic drift fences (50 cm length and 15 cm height) and half filled with 90% ethanol to conserve the captured animals. We installed the drift fences close to the ground to drive the local fauna towards the traps and increase the capture sampling (Ellis & Bedward, ). Pitfalls are widely used for sampling scorpions and harvestmen in taxonomic and ecological studies, including as the only sampling method (Dias et al., ; Freire & Motta, ).…”

Section: Methodsmentioning

confidence: 99%

Regional distribution patterns can predict the local habitat specialization of arachnids in heterogeneous landscapes of the Atlantic Forest

Porto

Pinto‐da‐Rocha²,

Rocha

2017

Diversity and Distributions

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Aim This study formally evaluates the ability of three models to use geographical data on species distribution to predict the habitat use patterns of species in heterogeneous landscapes. Location Species and habitats in the Brazilian Atlantic Rain Forest were investigated. Methods Based on empirical data on harvestmen and scorpions, we estimated the strength of species association with preferred habitat and classified them as habitat generalists or habitat specialists. We compared these empirical results with predictions made using data on species range size (model 1), species occurrence in biomes (model 2) and species occurrence in habitats within the biomes (model 3). Results We used 1,278 records of eight harvestman and two scorpion species that had specific determination and enough sampling numbers to allow safe identification of habitat specialization. We observed the following: (1) the extension of species occurrence did not influence the strength of species–habitat association (estimated by IndVal), which led us to reject model 1; (2) species habitat specialization derived from occurrences in biomes was 60% coincident with the classification derived from empirical data. This value is not different enough from the value expected by chance for these data, which also led us to reject model 2; and (3) species classification derived from secondary data about the habitats used had a significant coincidence of 80% with the empirical classification, which led us to accept model 3. Main conclusions For correct classification of species habitat specialization using secondary distributional data, we recommend that future studies consider using the most accurate information available on the habitats used by species. Especially for megadiverse and understudied groups, information about habitats used is not easy to obtain, so it is important for researchers and institutions to register and disseminate this information, which could support many other studies.

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A simulation study to quantify drift fence configuration and spacing effects when sampling mobile animals

Cited by 5 publications

References 26 publications

Optimising sampling methods for small mammal communities in Neotropical rainforests

Optimising sampling methods for small mammal communities in Neotropical rainforests

Pitfall trap sampling bias depends on body mass, temperature, and trap number: insights from an individual‐based model

Regional distribution patterns can predict the local habitat specialization of arachnids in heterogeneous landscapes of the Atlantic Forest

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