BTO research reveals that the release of Pheasants and Red-legged Partridges for commercial shoots may be boosting numbers of the avian predators and scavengers. Every year, 41-50 million non-native gamebirds (Pheasant and Red-legged Partridge) are released in the UK. Fewer than half these birds are shot, meaning there is potentially a large food resource available to predators and scavengers, sustaining their populations above the levels they would otherwise reach. If it occurs, this inflation of predator numbers might alter predator-prey dynamics, increasing predation pressure on some vulnerable species, including declining breeding waders like Curlew. This study used data from the Breeding Bird Survey and Bird Atlas 2007-11 to identify associations between the occurrence patterns of gamebirds and the abundance and population growth rates of several generalist predators, including Buzzard, Jay, Raven, Magpie and crows (Carrion and Hooded combined). While many other factors influence predator abundance, such as fine-scale habitat variation, availability of other food sources, and game management activities, the results suggest that large-scale variation in avian predator populations is predominantly positively affected by gamebird releases. The potential implications of this finding need to be thoroughly tested. Such tests could include regulation of releases on a trial basis, to determine effects on ground nesting birds, for example. The compulsory recording of releases and the number of predators controlled would also be valuable for a better understanding of the impacts, positive or negative, of gamebird releases on the wider environment. Read a blog post by publication author Henrietta Pringle discussing the work behind this paper.

The ability to predict spatial variation in biodiversity is a long-standing but elusive objective of landscape ecology. It depends on a detailed understanding of relationships between landscape and patch structure and taxonomic richness, and accurate spatial modelling. Complex heterogeneous environments such as cities pose particular challenges, as well as heightened relevance, given the increasing rate of urbanisation globally. Here we use a GIS-linked Bayesian Belief Network approach to test whether landscape and patch structural characteristics (including vegetation height, green-space patch size and their connectivity) drive measured taxonomic richness of numerous invertebrate, plant, and avian groups. We find that modelled richness is typically higher in larger and better-connected green-spaces with taller vegetation, indicative of more complex vegetation structure and consistent with the principle of ‘bigger, better, and more joined up’. Assessing the relative importance of these variables indicates that vegetation height is the most influential in determining richness for a majority of taxa. There is variation, however, between taxonomic groups in the relationships between richness and landscape structural characteristics, and the sensitivity of these relationships to particular predictors. Consequently, despite some broad commonalities, there will be trade-offs between different taxonomic groups when designing urban landscapes to maximise biodiversity. This research demonstrates the feasibility of using a GIS-coupled Bayesian Belief Network approach to model biodiversity at fine spatial scales in complex landscapes where current data and appropriate modelling approaches are lacking, and our findings have important implications for ecologists, conservationists and planners.

Newly published research from BTO shows how the popular pastime of feeding the birds is significantly shaping garden bird communities in Britain. The populations of several species of garden birds have grown in number, and the diversity of species visiting feeders has also increased.

AbstractAssessing the state and trend of biodiversity in the face of anthropogenic threats requires large-scale and long-time monitoring, for which new recording methods offer interesting possibilities. Reduced costs and a huge increase in storage capacity of acoustic recorders has resulted in an exponential use of Passive Acoustic Monitoring (PAM) on a wide range of animal groups in recent years, in particular for bats for which PAM constitutes an efficient tool. PAM for bats has led to a rapid growth in the quantity of acoustic data, making manual identification increasingly time-consuming. Therefore, software detecting sound events, extracting numerous features, and automatically identifying species have been developed. However, automated identification generates identification errors, which could influence analyses which looks at the ecological response of species. In this study we propose a cautious method to account for errors in acoustic identifications without excessive manual checking of recordings. We propose to check a representative sample of the outputs of a software commonly used in acoustic surveys (Tadarida), to model the identification success probability of 10 species and 2 species groups as a function of the confidence score provided for each automated identification. Using this relationship, we then investigated the effect of setting different False Positive Tolerances (FPTs), from a 50% to 10% false positive rate, above which data are discarded, by repeating a largescale analysis of bat response to environmental variables and checking for consistency in the results. Considering estimates, standard errors and significance of species response to environmental variables, the main changes occurred between the naive (i.e. raw data) and robust analyses (i.e. using FPTs). Responses were highly stable between FPTs. We conclude it was essential to, at least, remove data above 50% FPT to minimize false positives. We recommend systematically checking the consistency of responses for at least two contrasting FPTs (e.g. 50% and 10%), in order to ensure robustness, and only going on to conclusive interpretation when these are consistent. This study provides a huge saving of time for manual checking, which will facilitate the improvement of large-scale monitoring, and ultimately our understanding of ecological responses.