Estimating effects of arable land use intensity on farmland birds using joint species modeling

Abstract

Declines in European farmland birds over past decades have been attributed to the combined effects of agricultural intensification and abandonment. Consequently, aspirations to stop declines should focus attention on reversing these changes through voluntary or policy‐driven interventions. The design of such interventions should ideally be informed by scientific knowledge of which aspects of the transformation of agricultural landscapes have contributed to the farmland bird declines. Declines may be associated with loss of natural habitats or the intensification and homogenization of land use management on production land, and furthermore, these changes may interact. Here, we applied an orthogonal design exploiting spatial variation in land use in a major agricultural region of Sweden to seek evidence for benefits to farmland birds of reversing some of the intensifications on and among arable fields and whether effects are modified by the availability of seminatural habitats (pastures and field borders) in the landscape. We accounted for the potentially confounding effect of interactions between species by using a joint species distribution model explicitly controlling for additional variation and covariation among species. We found that interventions aimed specifically at land in production could provide benefits to farmland birds. Landscapes with a higher proportion leys or fallows and/or with a more diverse set of crops held higher abundances of most farmland birds. However, effects were only apparent in landscapes with low availability of seminatural habitats and were sometimes even negative in landscapes with high amounts of such habitats, demonstrating context dependence. Even if we found little evidence of interactions between species, the joint modeling approach provided several benefits. It allowed information to be shared between species making analyses robust to uncertainty due to low abundances and provided direct information about the mean and variability in effects of studied predictors among species. We also found that care needs to be taken regarding prior and distributional assumptions as the importance of species interactions might otherwise be overstated. We conclude that this approach is well suited for evaluating agricultural policies by providing evidence for or against certain interventions or to be linked to policy scenarios of land use change.

Landscape-scale terrestrial factors are also vital in shaping Odonata assemblages of watercourses

“…The findings suggest that an extensive land use management is necessary for a successful conservation management of Odonata assemblages. This kind of management supports species-rich Odonata assemblages and may also be beneficial for several other taxa such as amphibians, butterflies and farmland birds 40–42 . For preservation of species-rich Odonata assemblages future policies should take the landscape context into consideration and management actions should be directed toward regions where availability of extensively used areas is high.…”

Impacts of the EU's Common Agricultural Policy “Greening” Reform on Agricultural Development, Biodiversity, and Ecosystem Services

“…The aim is not to provide a universal LHI, but one that is meaningful given the type of agricultural regions and environmental responses that our study focuses on. Our focus is on landscapes that have a high proportion of agricultural land (>40%), with a percentage of the agricultural land covered by seminatural pastures and field borders below 50% (see Stjernman et al (2019) for further details). In these regions, seminatural pastures and field edges are elements of landscape heterogeneity that are important for biodiversity and ecosystem services, as substantiated by extensive empirical findings.…”

“…As a biodiversity production function we used a statistical model from a study on the effects of agricultural intensification on farmland birds in the same regions. The predictive ability of the production function for farmland bird diversity was motivated by the selection of predictors based on expert knowledge using mechanistic understanding, the structure of the statistical model considering covariation between species, and the calibration and validation using bird data collected in a field study conducted in the GSS and GMB regions (Stjernman et al 2019).…”

Optimizing Species Richness in Mosaic Landscapes: A Probabilistic Model of Species-Area Relationships

“…Half of the landscapes were sampled in the "mixed farming region" and half in the "plains region." Full details of the methods are provided by Stjernman et al (2019) and in Appendix S2. Landscapes were either 2.5 × 2.5 km squares or 1 km radius circles (Appendix S2).…”