Bergmann's rule in alien birds

Native bird species show latitudinal gradients in body size across species (Bergmann's rule), but whether or not such gradients are recapitulated in the alien distributions of bird species are unknown. Here, we test for the existence of Bergmann's rule in alien bird species worldwide, and investigate the causes of the observed patterns. Published databases were used to obtain the worldwide distributions of established alien bird populations, the locations of alien bird introductions, and bird body masses. Randomisation tests and linear models were used to assess latitudinal patterns in the body masses of introduced and established alien bird populations. Established alien bird species exhibit Bergmann's rule, but this is largely explained by where alien bird species have been introduced: latitudinal variation in the body masses of established alien bird species simply reflects latitudinal variation in the body masses of introduced species. There is some evidence that body mass is implicated in whether or not established species' alien ranges spread towards or contract away from the Equator following establishment. However, most alien bird ranges are encompassed by the latitudinal band(s) to which the species was introduced. Bergmann's rule in alien birds is therefore a consequence of where humans have introduced different species, rather than of natural processes operating after population introduction

Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data

Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autocorrelation parameters in SDMs are now widely available, but whether such approaches for inferring SDMs aid predictions compared to other methodologies is unknown. Here, within a simulated environment using 1000 generated species’ ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and boosted regression trees, BRT), to a spatial Bayesian SDM method (fitted using R-INLA), when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested how any recommended methodological settings designed to account for spatially non-random patterns in the data impact inference. Spatial Bayesian SDM method was the most consistently accurate method, being in the top 2 most accurate methods in 7 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly. When sampling points were randomly spread, BRT had a 1–3% greater accuracy over the other methods and when samples were clumped, the spatial Bayesian SDM method had a 4%-8% better AUC score. Alternatively, when sampling points were restricted to a small section of the true range all methods were on average 10–12% less accurate, with greater variation among the methods. Model inference under the recommended settings to account for autocorrelation was not impacted by clumping or restriction of data, except for the complexity of the spatial regression term in the spatial Bayesian model. Methods, such as those made available by R-INLA, can be successfully used to account for spatial autocorrelation in an SDM context and, by taking account of random effects, produce outputs that can better elucidate the role of covariates in predicting species occurrence. Given that it is often unclear what the drivers are behind data clumping in an empirical occurrence dataset, or indeed how geographically restricted these data are, spatially-explicit Bayesian SDMs may be the better choice when modelling the spatial distribution of target species

Spatial, seasonal and climatic predicitve models of Rift Valley Fever disease across Africa

Understanding the emergence and subsequent spread of human infectious diseases is a critical global challenge, especially for high-impact zoonotic and vector-borne diseases. Global climate and land-use change are likely to alter host and vector distributions, but understanding the impact of these changes on the burden of infectious diseases is difficult. Here, we use a Bayesian spatial model to investigate environmental drivers of one of the most important diseases in Africa, Rift Valley fever (RVF). The model uses a hierarchical approach to determine how environmental drivers vary both spatially and seasonally, and incorporates the effects of key climatic oscillations, to produce a continental risk map of RVF in livestock (as a proxy for human RVF risk). We find RVF risk has a distinct seasonal spatial pattern influenced by climatic variation, with the majority of cases occurring in South Africa and Kenya in the first half of an El Niño year. Irrigation, rainfall and human population density were the main drivers of RVF cases, independent of seasonal, climatic or spatial variation. By accounting more subtly for the patterns in RVF data, we better determine the importance of underlying environmental drivers, and also make space- and time-sensitive predictions to better direct future surveillance resources. This article is part of the themed issue ‘One Health for a changing world: zoonoses, ecosystems and human well-being’

The Global Avian Invasions Atlas, a database of alien bird distributions worldwide

The introduction of species to locations where they do not naturally occur (termed aliens) can have far-reaching and unpredictable environmental and economic consequences. Therefore there is a strong incentive to stem the tide of alien species introduction and spread. In order to identify broad patterns and processes of alien invasions, a spatially referenced, global dataset on the historical introductions and alien distributions of a complete taxonomic group is required. Here we present the Global Avian Invasions Atlas (GAVIA) - a new spatial and temporal dataset comprising 27,723 distribution records for 971 alien bird species introduced to 230 countries and administrative areas spanning the period 6000BCE - AD2014. GAVIA was initiated to provide a unified database of records on alien bird introductions, incorporating records from all stages of invasion, including introductions that have failed as well as those that have succeeded. GAVIA represents the most comprehensive resource on the global distribution of alien species in any major taxon, allowing the spatial and temporal dynamics of alien bird distributions to be examined

What factors increase the vulnerability of native birds to the impacts of alien birds?

Biodiversity impacts caused by alien species can be severe, including those caused by alien birds. In order to protect native birds, we aimed to identify factors that influence their vulnerability to the impacts of alien birds. We first reviewed the literature to identify native bird species sustaining such impacts. We then assigned impact severity scores to each native bird species, depending on the severity of the impacts sustained, and performed two types of analyses. First, we used contingency table tests to examine the distribution of impacts across their severity, type and location, and across native bird orders. Second, we used mixed‐effects models to test factors hypothesised to influence the vulnerability of native birds to the impacts of alien birds. Ground‐nesting shorebirds and seabirds were more prone to impacts through predation, while cavity‐nesting woodpeckers and parrots were more prone to impacts through competition. Native bird species were more vulnerable when they occupied islands, warm regions, regions with climatic conditions similar to those in the native range of the invading alien species, and when they were physically smaller than the invading alien species. To a lesser extent, they were also vulnerable when they shared habitat preferences with the invading alien species. By considering the number and type of native bird species affected by alien birds, we demonstrate predation impacts to be more widespread than previously indicated, but also that damaging predation impacts may be underreported. We identify vulnerable orders of native birds, which may require conservation interventions; characteristics of native birds that increase their vulnerability, which may be used to inform risk assessments; and regions where native birds are most vulnerable, which may direct management interventions. The impacts sustained by native birds may be going unnoticed in many regions of the world: there is a clear need to identify and manage them

Understanding the cryptic nature of Lassa Fever in West Africa

Lassa fever (LF) is increasingly recognized by global health institutions as an important rodent-borne disease with severe impacts on some of West Africa’s poorest communities. However, our knowledge of LF ecology, epidemiology and distribution is limited, which presents barriers to both short-term disease forecasting and prediction of long-term impacts of environmental change on Lassa virus (LASV) zoonotic transmission dynamics. Here, we synthesize current knowledge to show that extrapolations from past research have produced an incomplete picture of the incidence and distribution of LF, with negative consequences for policy planning, medical treatment and management interventions. Although the recent increase in LF case reports is likely due to improved surveillance, recent studies suggest that future socio-ecological changes in West Africa may drive increases in LF burden. Future research should focus on the geographical distribution and disease burden of LF, in order to improve its integration into public policy and disease control strategies

Global evolutionary isolation measures can capture key local conservation species in Nearctic and Neotropical bird communities

Understanding how to prioritise among the most deserving imperilled species has been a focus of biodiversity science for the past three decades. Though global metrics that integrate evolutionary history and likelihood of loss have been successfully implemented, conservation is typically carried out at sub-global scales on communities of species rather than among members of complete taxonomic assemblages. Whether and how global measures map to a local scale has received little scrutiny. At a local scale, conservation-relevant assemblages of species are likely to be made up of relatively few species spread across a large phylogenetic tree, and as a consequence there are potentially relatively large amounts of evolutionary history at stake. We ask to what extent global metrics of evolutionary history are useful for conservation priority setting at the community level by evaluating the extent to which three global measures of evolutionary isolation (Evolutionary Distinctiveness, Average Pairwise Distance, and the Pendant Edge or Unique PD Contribution) capture community level phylogenetic and trait diversity for a large sample of Neotropical and Nearctic bird communities. We find that prioritizing the most Evolutionarily Distinctive species globally safeguards more than twice the total phylogenetic diversity of local communities on average, but that this does not translate into increased local trait diversity. In contrast, global Average Pairwise Distance is strongly related to the Average Pairwise Distance of those same species at the community level, and prioritizing these species also safeguards local phylogenetic diversity and trait diversity. The next step for biologists is to understand the variation in the concordance of global and local level scores and what this means for conservation priorities: we need more directed research on the use of different measures of evolutionary isolation to determine which might best capture desirable aspects of biodiversity

What is macroecology?

The symposium 'What is Macroecology?' was held in London on 20 June 2012. The event was the inaugural meeting of the Macroecology Special Interest Group of the British Ecological Society and was attended by nearly 100 scientists from 11 countries. The meeting reviewed the recent development of the macroecological agenda. The key themes that emerged were a shift towards more explicit modelling of ecological processes, a growing synthesis across systems and scales, and new opportunities to apply macroecological concepts in other research fields

Integrative modelling for One Health: pattern, process and participation

This paper argues for an integrative modelling approach for understanding zoonoses disease dynamics, combining process, pattern and participatory models. Each type of modelling provides important insights, but all are limited. Combining these in a ‘3P’ approach offers the opportunity for a productive conversation between modelling efforts, contributing to a ‘One Health’ agenda. The aim is not to come up with a composite model, but seek synergies between perspectives, encouraging cross-disciplinary interactions. We illustrate our argument with cases from Africa, and in particular from our work on Ebola virus and Lassa fever virus. Combining process-based compartmental models with macroecological data offers a spatial perspective on potential disease impacts. However, without insights from the ground, the ‘black box’ of transmission dynamics, so crucial to model assumptions, may not be fully understood. We show how participatory modelling and ethnographic research of Ebola and Lassa fever can reveal social roles, unsafe practices, mobility and movement and temporal changes in livelihoods. Together with longer-term dynamics of change in societies and ecologies, all can be important in explaining disease transmission, and provide important complementary insights to other modelling efforts. An integrative modelling approach therefore can offer help to improve disease control efforts and public health responses. This article is part of the themed issue ‘One Health for a changing world: zoonoses, ecosystems and human well-being’.This work was undertaken under the umbrella of the Dynamic Drivers of Disease in Africa programme, hosted by the ESRC STEPS Centre (http://steps-centre.org/project/drivers_of_disease/). The programme was funded by ESPA (Ecosystem Services for Poverty Alleviation), supported by NERC (Natural Environment Research Council), DFID (Department for International Development) and ESRC (Economic and Social Research Council) (NE-J001570-1)