Dataset Information

Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type.

ABSTRACT: Species distribution models (SDM) are increasingly used to understand the factors that regulate variation in biodiversity patterns and to help plan conservation strategies. However, these models are rarely validated with independently collected data and it is unclear whether SDM performance is maintained across distinct habitats and for species with different functional traits. Highly mobile species, such as bees, can be particularly challenging to model. Here, we use independent sets of occurrence data collected systematically in several agricultural habitats to test how the predictive performance of SDMs for wild bee species depends on species traits, habitat type, and sampling technique. We used a species distribution modeling approach parametrized for the Netherlands, with presence records from 1990 to 2010 for 193 Dutch wild bees. For each species, we built a Maxent model based on 13 climate and landscape variables. We tested the predictive performance of the SDMs with independent datasets collected from orchards and arable fields across the Netherlands from 2010 to 2013, using transect surveys or pan traps. Model predictive performance depended on species traits and habitat type. Occurrence of bee species specialized in habitat and diet was better predicted than generalist bees. Predictions of habitat suitability were also more precise for habitats that are temporally more stable (orchards) than for habitats that suffer regular alterations (arable), particularly for small, solitary bees. As a conservation tool, SDMs are best suited to modeling rarer, specialist species than more generalist and will work best in long-term stable habitats. The variability of complex, short-term habitats is difficult to capture in such models and historical land use generally has low thematic resolution. To improve SDMs' usefulness, models require explanatory variables and collection data that include detailed landscape characteristics, for example, variability of crops and flower availability. Additionally, testing SDMs with field surveys should involve multiple collection techniques.

SUBMITTER: Marshall L

PROVIDER: S-EPMC4667819 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type.

Marshall Leon L Carvalheiro Luísa G LG Aguirre-Gutiérrez Jesús J Bos Merijn M de Groot G Arjen GA Kleijn David D Potts Simon G SG Reemer Menno M Roberts Stuart S Scheper Jeroen J Biesmeijer Jacobus C JC

Ecology and evolution 20150923 19

Species distribution models (SDM) are increasingly used to understand the factors that regulate variation in biodiversity patterns and to help plan conservation strategies. However, these models are rarely validated with independently collected data and it is unclear whether SDM performance is maintained across distinct habitats and for species with different functional traits. Highly mobile species, such as bees, can be particularly challenging to model. Here, we use independent sets of occurre ...[more]

PMID: 26664689

Similar Datasets

Project description:Habitat classifications provide guidelines for mapping and comparing marine resources across geographic regions. Calcareous bio-concretions and their associated biota have not been exhaustively categorized. Furthermore, for management and conservation purposes, species and habitat mapping is critical. Recently, several developments have occurred in the field of predictive habitat modeling, and multiple methods are available. In this study, we defined the habitats constituting northern Adriatic biogenic reefs and created a predictive habitat distribution model. We used an updated dataset of the epibenthic assemblages to define the habitats, which we verified using the fuzzy k-means (FKM) clustering method. Redundancy analysis was employed to model the relationships between the environmental descriptors and the FKM membership grades. Predictive modelling was carried out to map habitats across the basin. Habitat A (opportunistic macroalgae, encrusting Porifera, bioeroders) characterizes reefs closest to the coastline, which are affected by coastal currents and river inputs. Habitat B is distinguished by massive Porifera, erect Tunicata, and non-calcareous encrusting algae (Peyssonnelia spp.). Habitat C (non-articulated coralline, Polycitor adriaticus) is predicted in deeper areas. The onshore-offshore gradient explains the variability of the assemblages because of the influence of coastal freshwater, which is the main driver of nutrient dynamics. This model supports the interpretation of Habitat A and C as the extremes of a gradient that characterizes the epibenthic assemblages, while Habitat B demonstrates intermediate characteristics. Areas of transition are a natural feature of the marine environment and may include a mixture of habitats and species. The habitats proposed are easy to identify in the field, are related to different environmental features, and may be suitable for application in studies focused on other geographic areas. The habitat model outputs provide insight into the environmental drivers that control the distribution of the habitat and can be used to guide future research efforts and cost-effective management and conservation plans.

Project description:Isotope analysis has proven useful for understanding diets of animals that are difficult to track for extended periods. Bees are small yet highly mobile and often forage from multiple habitats. However, current methods of assessing diet are limited in scope. Efficient methods of tracking bee diets that integrate across life stages, distinguish habitat use, and are sensitive to taxonomic differences will inform conservation strategies. We evaluated the utility of stable isotope analysis for estimating contributions of adjacent habitats to bees' diets. We also investigated taxonomic variation in bee and flower isotope composition. We measured natural abundance of carbon and nitrogen stable isotopes in two body regions from three wild bee genera, as well as in 25 species of flowers that likely comprised their diets. Bee ∂13C and ∂15N varied with habitat and taxonomic groups (conflated with month), but did not match spatial or seasonal trends in their food plants. Flower ∂13C was lowest in the forest and in April-June, as expected if driven by water availability. However, bee ∂13C was elevated in the spring, likely from overwintering nutritional stress or unpredictable food availability. Bumble bees (Bombus) were enriched in ∂15N compared to others, possibly reflecting differences in larval feeding. Bee diet mixing models had high variation and should be interpreted with caution. Models estimated similar habitat contributions to diets of spring Andrena and overwintered Bombus queens. Summer Bombus queens and workers were indistinguishable. Sweat bees (Halictus) were estimated to use comparatively more field flowers than others. Overall, taxon more strongly influenced isotope composition than either foraging habitat or month, likely because of associated differences in sociality and timing of annual activity. Future studies seeking to reveal bee diets by isotope analysis may gain better resolution in more isotopically distinct habitats, in conjunction with controlled feeding or isotope labeling experiments.

Dataset Information

Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type.

Publications

Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets