The Eurasian badger (Meles meles) is implicated in the transmission of bovine tuberculosis (TB) to cattle in the UK and Republic of Ireland. Badger culling has been employed for the control of TB in cattle in both countries, with varying results. Social perturbation of badger populations following culling has been proposed as an explanation for the failure of culling to consistently demonstrate significant reductions in cattle TB. Field studies indicate that culling badgers may result in increased immigration into culled areas, disruption of territoriality, increased ranging and mixing between social groups. Our analysis shows that some measures of sociality may remain significantly disrupted for up to 8 years after culling. This may have epidemiological consequences because previous research has shown that even in a relatively undisturbed badger population, movements between groups are associated with increases in the incidence of Mycobacterium bovis infection. This is consistent with the results from a large-scale field trial, which demonstrated decreased benefits of culling at the edges of culled areas, and an increase in herd breakdown rates in neighbouring cattle.

The concept of nearness and that nearer things are more connected is useful in quantifying a variety of geographical patterns and processes, including ecological connectivity between geographic locations. In some ecological systems connectivity does not follow nearness relations defined by Euclidean distances, so distance must be measured another way. Least-cost modelling is a technique that can incorporate traversal costs across a landscape to measure the least-cost distance between locations as a function of both the distance travelled and the costs traversed. There has been a significant increase in the interest and use of least-cost modelling by ecologists in the last decade. However, perhaps because early applications of least-cost modelling in ecology tended to cite the method with reference to geographic information system software rather than the geographical science literature, ecologists are not currently making full use of available least-cost modelling techniques that have continued to develop. This review aims to describe the concepts of least-cost modelling, demonstrate current applications of least-cost modelling in landscape ecology, and to suggest future opportunities by linking the ecological application of least-cost modelling with recent geographical science developments from which leastcost modelling originally developed.

Climate change is expected to severely impact cultivated plants and consequently human livelihoods 1-3 , especially in Sub-Saharan Africa (SSA) 4-6. Increasing agricultural plant diversity (agrobiodiversity) could overcome this global challenge 7-9 given more information on the climatic tolerance of crops and their wild relatives. Using >200,000 worldwide occurrence records for 29 major crops and 778 of their wild relative species, we assess for each crop how future climatic conditions are expected to change in SSA and whether populations of the same 2 crop from other continents, wild relatives around the world, or other crops from SSA are better adapted to expected future climatic conditions in the region. We show that climate conditions not currently experienced by the 29 crops in SSA are predicted to become widespread, increasing production insecurity, especially for yams. However, crops such as potato, squash and finger millet may be maintained by using wild relatives or non-African crop populations with climatic niches more suited to future conditions. Crop insecurity increases over time and rising greenhouse gas emissions, but the potential for using agrobiodiversity for resilience is less altered. Climate change will therefore affect Sub-Saharan agriculture but agrobiodiversity can provide resilient solutions in the short-and medium-term. Main Text: Global climate has changed rapidly over recent decades, and temperature and precipitation regimes are predicted to shift significantly in the near future 10. Future impacts on both biodiversity and human livelihoods are significant and primarily negative 2,4,11. By affecting plant productivity, and thus industrial and food crop yield, climate change is expected to impact global human economy and subsistence 1,2. Its tropical location, socioeconomic, demographic, policy, and farming characteristics place sub-Saharan Africa (SSA) at major risk 5,6. Assessing which sub-Saharan crops, regions and populations will be most affected, as well as potential future adaptations is therefore essential. Agrobiodiversity and breeding programs represent an important adaptive strategy for agriculture in a changing world 8,12. Currently cultivated crops may exhibit reduced genetic variation compared to that found in wild relative populations, which may limit their resilience and adaptation to future environmental conditions 13. Crop improvement through selection for

The estimation of animal abundance has a central role in wildlife management and research, including the role of badgers Meles meles in bovine tuberculosis transmission to cattle. This is the first study to examine temporal change in the badger population of Northern Ireland over a medium-to long-term time frame of 14-18 years by repeating a national survey first conducted during 1990-1993. A total of 212 1-km 2 squares were surveyed during 2007-2008 and the number, type and activity of setts therein recorded. Badgers were widespread with 75% of squares containing at least one sett. The mean density of active main setts, which was equivalent to badger social group density, was 0.56 (95% CI: 0.46-0.67) active main setts per km 2 during 2007-2008. Social group density varied significantly among landclass groups and counties. The total number of social groups was estimated at 7,600 (95% CI: 6,200-9,000) and, not withstanding probable sources of error in estimating social group size, the total abundance of badgers was estimated to be 34,100 (95% CI: 26,200-42,000). There was no significant change in the badger population from that recorded during 1990-1993. A resource selection model provided a relative probability of sett construction at a spatial scale of 25 m. Sett locations were negatively associated with elevation and positively associated with slope, aspect, soil sand content, the presence of cover, and the area of improved grassland and arable agriculture within 300 m.

As the European badger (Meles meles) can be of conservation or management concern, it is important to have a good understanding of the species' dispersal ability. In particular, knowledge of landscape elements that affect dispersal can contribute to devising effective management strategies. However, the standard approach of using Bayesian clustering methods to correlate genetic discontinuities with landscape elements cannot easily be applied to this problem, as badger populations are often characterized by a strong confounding isolation-by-distance (IBD) pattern. We therefore developed a two-step method that compares the location of pairs of related badgers relative to a putative barrier and utilizes the expected spatial genetic structure characterized by IBD as a null model to test for the presence of a barrier. If a linear feature disrupts dispersal, the IBD pattern characterising pairs of individuals located on different sides of a putative barrier should differ significantly from the pattern obtained with pairs of individuals located on the same side. We used our new approach to assess the impact of rivers and roads of different sizes on badger dispersal in western England. We show that a large, wide river represented a barrier to badger dispersal and found evidence that a motorway may also restrict badger movement. Conversely, we did not find any evidence for small rivers and roads interfering with badger movement. One of the advantages of our approach is that potentially it can detect features that disrupt gene flow locally, without necessarily creating distinct identifiable genetic units.

Summary 1.Landscape genetics is an area of research that can help to understand many spatial ecological processes, but requires significant interdisciplinary collaboration. Use of geographic information system (GIS) software is essential, but requires a degree of customisation that is often beyond the non-specialist. 2. To help address this, a series of Python script based GIS tools have been developed for use in landscape genetics studies. 3. The scripts convert files, visualise genetic relatedness, and measure landscape connectivity using least-cost path analysis. The scripts are housed in an ArcToolbox that is freely available along with the underlying Python code. 4. The Python scripts allow researchers to use more current software, provide the option of further development by the user community, and reduce the amount of time that would be spent developing common solutions.

Summary1. Neutral landscape models (NLMs) are widely used to model ecological patterns and processes across landscapes. However, the ability to generate NLMs is often made available through standalone bespoke software packages that have platform limitations. 2. We have developed a PYTHON package that brings together some of the more popular NLM algorithms using a general numerical framework. 3. The resulting NLMpy package: (i) allows for the creation of NLMs directly within a PYTHON modelling workflow or by other modelling software capable executing a PYTHON script, (ii) enables the first opportunity to create a NLM that combines different algorithms, (iii) provides easy integration with geographic information system data and (iv) creates a framework for developing other NLMs.

Aim Project-specific data for biogeographical models are often logistically impractical to collect, forcing the use of existing data from a variety of sources. Use of these data is complicated when neither absence nor an estimate of the area sampled is available, as these are requirements of most analytical techniques. We demonstrate the Mahalanobis distance statistic (D 2 ), which is a presence-only modelling technique and does not require information on species absence or the sampled area. We use badger (Meles meles) setts as the basis for this investigation, as their landscape associations are well understood, and survey data exist against which to compare estimates of sett distribution and abundance.Location England and Wales (151,403 km 2 ).Methods We used stratified random samples of sett locations, and landscape variables that are known to be important for choice of badger sett location within a geographic information system at a cell resolution of 100 · 100 m. Landscape conditions at two scales were extracted, at and around sett locations, and the D 2 was used to classify all cells in England and Wales into a sett suitability model. Comparison of this sett suitability model with known main sett densities allowed estimates of main sett density to be made across England and Wales, with associated uncertainty.Results The sett suitability model was shown through iterative sampling and model evaluation using independent data to be stable and accurate. Main sett density estimates were biologically plausible in comparison with previous fieldderived estimates. We estimate 58,000 main setts within England and Wales, with 95% confidence intervals suggesting a value between 31,000 and 93,000.Main conclusions The D 2 , which could be applied to other species and locations, proved useful in our context, where absence data were not available and the sampled area could not be reliably established. We were able to predict sett suitability across a large area and at a fine resolution, and to generate plausible estimates of main sett density. The final model provides valuable information on probable badger sett distribution and abundance, and may contribute to future research on the spatial ecology of badgers in England and Wales.