Non‐random sampling along rural–urban gradients may reduce reliability of multi‐species farmland bird indicators and their trends

Abstract

The continued global biodiversity crisis necessitates the continuation and development of new well‐designed monitoring strategies and action plans with particular focus on under‐represented countries and regions. However, limited resources in terms of budget and availability of qualified field personnel can restrict the geographical coverage of monitoring efforts. Focusing monitoring efforts on a representative subset of species and locations can improve cost‐efficiency. Optimal performance of multi‐species indicators derived from such an approach requires objective methods for species selection and a sampling design that reduces inherent sampling bias caused by regional differences in habitat availability or accessibility. To explore the performance of a multi‐species indicator across different regions within a nation, we developed a multi‐species indicator (MSI) for farmland birds in Norway using objective niche‐based selection of species. We compare the performance of this indicator at national and regional scales (Central and East regions) in Norway, and between urban and rural sites within regions. The seven‐species indicator obtained from the species selection provided similar indicator values and trends for Norway and the Central and East regions, as well as for rural sites within the combined Central + East region. All trends were defined as showing moderate decline from 2007–2016. Urban sites within the combined Central + East region provided trend estimates that showed stronger decline than rural areas in the same region during the time span. Our results emphasize the need to control for sampling bias when structuring monitoring programmes such as a Breeding Bird Survey (BBS). This is especially important if limited resources restrict the geographical coverage of the monitoring scheme. We recommend that monitoring schemes follow a stratified random sampling design that represents both the availability of different land cover types and their distribution with regard to proximity to highly populated areas. If that is not possible, statistically weighting data from different regions or landscapes is likely to be necessary.

Temporal trends in the spatial bias of species occurrence records

“…At the global‐scale, more occurrence records are found in countries with developed biodiversity data infrastructure and higher GDP (Collen et al 2008, Meyer et al 2015, Callaghan et al 2021c). At regional or national‐scales, spatial bias is commonly associated with human population density, settlements and roads (Kelling et al 2015, Geldmann et al 2016, Mair and Ruete 2016, Hugo and Altwegg 2017, Dissanayake et al 2019, Girardello et al 2019, Husby et al 2021). The higher number of biodiversity records near urban areas can reflect the local densities of observers as well as the behavior of observers to visit the most accessible places from their home.…”

“…We studied the spatial bias of species occurrence data and the implications of changing spatial biases for biodiversity change research, using a combination of empirical analysis of species occurrence record databases and simulations. We focused on spatial bias towards urban and protected areas since previous studies have documented spatial bias with respect to these land covers/uses (Kelling et al 2015, Geldmann et al 2016, Mair and Ruete 2016, Hugo and Altwegg 2017, Dissanayake et al 2019, Girardello et al 2019, Husby et al 2021). We used multiple datasets from different databases, which include citizen science and other sources of species occurrence records, for birds, amphibians, butterflies and plants.…”

Species distributions models may predict accurately future distributions but poorly how distributions change: A critical perspective on model validation

“…One gradient that is not covered with this sampling is the increasing human population density towards southern latitudes. This could be important because in Norway, Husby et al (2021) found that farmland birds have a stronger decline near urban areas than in rural areas. We acknowledge the possibility that omitting this effect of urban landscape may have distorted our results, yet we assume its effect to be marginal as only a small proportion of the study species breed in farmlands.…”

Wind Farms and Power Lines Have Negative Effects on Territory Occupancy in Eurasian Eagle Owls (Bubo bubo)

“…During wind farm construction, strong anthropogenic noise is likely an important disturbing factor for birds in the surrounding areas [23][24][25][26][27]. Farmland birds decline more significantly near urban areas compared with rural areas with less anthropogenic impact, including noise [28]. Owls are, to a large extent, acoustically specialized predators, and therefore potentially vulnerable to noise.…”