Vectors of infectious diseases are generally thought to be regulated by abiotic conditions such as climate or the availability of specific hosts or habitats. In this study we tested whether blacklegged ticks, the vectors of Lyme disease, granulocytic anaplasmosis and babesiosis can be regulated by the species of vertebrate hosts on which they obligately feed. By subjecting field-caught hosts to parasitism by larval blacklegged ticks, we found that some host species (e.g. opossums, squirrels) that are abundantly parasitized in nature kill 83 -96% of the ticks that attempt to attach and feed, while other species are more permissive of tick feeding. Given natural tick burdens we document on these hosts, we show that some hosts can kill thousands of ticks per hectare. These results indicate that the abundance of tick vectors can be regulated by the identity of the hosts upon which these vectors feed. By simulating the removal of hosts from intact communities using empirical models, we show that the loss of biodiversity may exacerbate disease risk by increasing both vector numbers and vector infection rates with a zoonotic pathogen.

Climate warming over the next century is expected to have a large impact on the interactions between pathogens and their animal and human hosts. Vector-borne diseases are particularly sensitive to warming because temperature changes can alter vector development rates, shift their geographical distribution and alter transmission dynamics. For this reason, African trypanosomiasis (sleeping sickness), a vector-borne disease of humans and animals, was recently identified as one of the 12 infectious diseases likely to spread owing to climate change. We combine a variety of direct effects of temperature on vector ecology, vector biology and vector -parasite interactions via a disease transmission model and extrapolate the potential compounding effects of projected warming on the epidemiology of African trypanosomiasis. The model predicts that epidemics can occur when mean temperatures are between 20.78C and 26.18C. Our model does not predict a large-range expansion, but rather a large shift of up to 60 per cent in the geographical extent of the range. The model also predicts that 46-77 million additional people may be at risk of exposure by 2090. Future research could expand our analysis to include other environmental factors that influence tsetse populations and disease transmission such as humidity, as well as changes to human, livestock and wildlife distributions. The modelling approach presented here provides a framework for using the climate-sensitive aspects of vector and pathogen biology to predict changes in disease prevalence and risk owing to climate change.

Borrelia burgdorferi s.s., the bacterium that causes Lyme disease in North America, circulates among a suite of vertebrate hosts and their tick vector. The bacterium can be differentiated at the outer surface protein C (ospC) locus into 25 genotypes. Wildlife hosts can be infected with a suite of ospC types but knowledge on the transmission efficiencies of these naturally infected hosts to ticks is still lacking. To evaluate the occupancy and detection of ospC types in wildlife hosts, we adapted a likelihood-based species patch occupancy model to test for the occurrence probabilities (ψ – “occupancy”) and transmission efficiencies (ε – “detection”) of each ospC type. We detected differences in ospC occurrence and transmission efficiencies from the null models with HIS (human invasive strains) types A and K having the highest occurrence estimates, but both HIS and non-HIS types having high transmission efficiencies. We also examined ospC frequency patterns with respect to strains known to be invasive in humans across the host species and phylogenetic groups. We found that shrews and to a lesser extent, birds, were important host groups supporting relatively greater frequencies of HIS to non-HIS types. This novel method of simultaneously assessing occurrence and transmission of ospC types provides a powerful tool in assessing disease risk at the genotypic level in naturally infected wildlife hosts and offers the opportunity to examine disease risk at the community level.

Summary Nitrogen‐fixing rhizobial mutualists display high levels of genetic and phenotypic diversity at multiple spatial scales. Identifying the ecological and environmental drivers of variation in the genetic composition and function of rhizobial communities is critical to understanding rhizobial impacts on community and ecosystem productivity. In this study, we examined how environmental factors and host species jointly affect rhizobial phenotypic diversity and community structure. Individual host plants of two native Australian legumes, Acacia salicina and A. stenophylla, were inoculated with a community of six rhizobial strains (belonging to three genera) previously characterized for symbiotic effectiveness. The plants were grown in a fully factorial experiment under high and low phosphorus and water, and high or no nitrogen. Half the plants were harvested after 19 weeks and the other half after 30 weeks. The relative abundance of the six rhizobial strains was characterized using amplicon sequencing of the rhizobial 16S rRNA gene from DNA extracted from the root nodules of plants after each harvest. Variation in the relative abundance of the six rhizobial strains was best explained by host species, although interactions between environment and host species also emerged as significant predictors of variation in rhizobial community composition. We detected strong host‐specific interactions for two strains, while two other strains were clearly generalists, being relatively abundant in both host species. For both host species, the two generalist strains were as abundant as the best specialist strain within the rhizobial community. Synthesis. Our study show host species identity as the key determinant of rhizobial community structure within the root nodules of Acacia plants. Strains that were efficient at promoting the growth of their host plant had high relative abundance in experimental communities. Furthermore, interaction among host species, rhizobial host‐range, and environmental variation determine the structure of rhizobial communities, although not in clearly predictable ways. Such interactions contributed to the maintenance (albeit at relatively low frequencies) of ineffective strains in nodule communities. These interacting effects could help to explain why rhizobial communities are functionally diverse, such that specialists, generalists and seemingly ineffective rhizobia, can be simultaneously maintained in rhizosphere communities.

Mixed hardwood forests of the northeast United States support a guild of granivorous/omnivorous rodents including gray squirrels (Sciurus carolinensis), eastern chipmunks (Tamias striatus), and white-footed mice (Peromyscus leucopus). These species coincide geographically, co-occur locally, and consume similar food resources. Despite their idiosyncratic responses to landscape and patch variables, patch occupancy models suggest that competition may influence their respective distributions and abundances, and accordingly their influence on the rest of the forest community. Experimental studies, however, are wanting. We present the result of a large-scale experiment in which we removed white-footed mice or gray squirrels from small, isolated forest fragments in Dutchess County, New York, and added these mammals to other fragments in order to alter the abundance of these two species. We then used mark–recapture analyses to quantify the population-level and individual-level effects on resident mice, squirrels, and chipmunks. Overall, we found little evidence of competition. There were essentially no within-season numerical responses to changes in the abundance of putative competitors. Moreover, while individual-level responses (apparent survival and capture probability) did vary with competitor densities in some models, these effects were often better explained by site-specific parameters and were restricted to few of the 19 sites we studied. With only weak or nonexistent competition among these three common rodent species, we expect their patterns of habitat occupancy and population dynamics to be largely independent of one another.

Lyme disease is a major vector-borne bacterial disease in the USA. The disease is caused by Borrelia burgdorferi, and transmitted among hosts and humans, primarily by blacklegged ticks (Ixodes scapularis). The ~25 B. burgdorferi genotypes, based on genotypic variation of their outer surface protein C (ospC), can be phenotypically separated as strains that primarily cause human diseases—human invasive strains (HIS)—or those that rarely do. Additionally, the genotypes are non-randomly associated with host species. The goal of this study was to examine the extent to which phenotypic outcomes of B. burgdorferi could be explained by the host communities fed upon by blacklegged ticks. In 2006 and 2009, we determined the host community composition based on abundance estimates of the vertebrate hosts, and collected host-seeking nymphal ticks in 2007 and 2010 to determine the ospC genotypes within infected ticks. We regressed instances of B. burgdorferi phenotypes on site-specific characteristics of host communities by constructing Bayesian hierarchical models that properly handled missing data. The models provided quantitative support for the relevance of host composition on Lyme disease risk pertaining to B. burgdorferi prevalence (i.e. overall nymphal infection prevalence, or NIPAll) and HIS prevalence among the infected ticks (NIPHIS). In each year, NIPAll and NIPHIS was found to be associated with host relative abundances and diversity. For mice and chipmunks, the association with NIPAll was positive, but tended to be negative with NIPHIS in both years. However, the direction of association between shrew relative abundance with NIPAll or NIPHIS differed across the two years. And, diversity (H') had a negative association with NIPAll, but positive association with NIPHIS in both years. Our analyses highlight that the relationships between the relative abundances of three primary hosts and the community diversity with NIPAll, and NIPHIS, are variable in time and space, and that disease risk inference, based on the role of host community, changes when we examine risk overall or at the phenotypic level. Our discussion focuses on the observed relationships between prevalence and host community characteristics and how they substantiate the ecological understanding of phenotypic Lyme disease risk.

The potential vectors of West Nile virus (family Flaviviridae, genus Flavivirus, WNV) in Doña Ana County, NM, were determined during 2004 and 2005. Trapping was conducted using Centers for Disease Control and Prevention miniature light-traps baited with dry ice, and gravid traps baited with a hay infusion. In addition, sentinel chickens were housed at four of the trapping locations to monitor WNV epizootic transmission. In total, 5,576 pools consisting of 115,797 female mosquitoes were tested for WNV by reverse transcription-polymerase chain reaction, of which 152 from 13 mosquito species representing six genera were positive. Culex tarsalis Coquillett, Culex quinquefasciatus Say, Culex erythrothorax Dyar, Aedes vexans (Meigan), and Psorophora columbiae (Dyar & Knab) accounted for 86% of all detections. Based on the frequency of WNV detection, our data indicate primary and secondary vector roles for Cx. tarsalis and Cx. quinquefasciatus, respectively, with Cx. erythrothorax, Ae. vexans, and Ps. columbiae as occasional vectors of WNV in Dofia Ana County. Other species testing positive for the virus included Aedes aegypti (L.), Anopheles franciscanus McCracken, Culex stigmatosoma Dyar, Culiseta inornata (Williston), Ochlerotatus dorsalis (Meigan), Ochlerotatus sollicitans (Walker), Ochlerotatus trivittatus (Coquillett), and Psorophora signipennis (Coquillett). Although they occurred after initial WNV detections in mosquitoes, in total, 21 seroconversions in sentinel chickens were detected during the study.

Measuring, reporting, and forecasting research impact beyond academia has become increasingly important to demonstrate and understand real-world benefits. This is arguably most important in crisis disciplines such as medicine, environmental sustainability and biodiversity conservation, where application of new knowledge is urgently needed to improve health and environmental outcomes. Increasing focus on impact has prompted the development of theoretical guidance and practical tools tailored to a range of disciplines, but commensurate development of tools for conservation is still needed. In the present article, we review available tools for evaluating research impact applicable to conservation research. From these, and via a survey of conservation professionals, we compiled and ranked a list of 96 impact indicators useful for conservation science. Our indicators apply to a logic chain of inputs, processes, outputs, outcomes, and impacts. We suggest the list can act as a clear guide to realize and measure potential impacts from conservation research within and beyond academia.