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

Leon Marshall,Stuart Roberts,Luísa G Carvalheiro,Jeroen Scheper,Jesús Aguirre‐Gutiérrez,Simon G Potts,Jacobus C Biesmeijer,Merijn Bos,G Arjen Groot,Menno Reemer,David Kleijn

doi:10.1002/ece3.1579

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.

Highlights

Pollinators are responsible for the pollination of over 80% of flowering plants (Ollerton et al 2011), and the vast majority of global food crops benefit from animal pollination, with approximately half of these crops being highly dependent (Klein et al 2007)
The abundance and richness of wild bees varied between habitat types, species trait groups, and sampling technique
Significant interactions were found between sampling and group and sampling and habitat type; the effect of habitat type decreased for transect collections and the effect of species trait groups was lower for transect collections than pan trap collections

Summary

Introduction

Pollinators are responsible for the pollination of over 80% of flowering plants (Ollerton et al 2011), and the vast majority of global food crops benefit from animal pollination, with approximately half of these crops being highly dependent (Klein et al 2007). While the honeybee (Apis mellifera L.) is considered the most economically valuable pollinator species for agriculture, wild pollinators can be more efficient per individual in enhancing the yield and quality of many crops (Klein et al 2007; Garibaldi et al 2013). Their diversity has declined in Europe (Biesmeijer et al 2006; Dupont et al 2011; Bommarco et al 2011; Carvalheiro et al 2013) and elsewhere (Bartomeus et al 2013; Martins et al 2013). Understanding which environmental factors determine where wild bees occur in the landscape is essential for the success of such targeted interventions

Methods

Results

Conclusion