Inferring evolutionary signals from ecological data in a plant-pathogen metapopulation

We followed the dynamics of local epidemics in three populations of a natural plant-pathogen system for four sequential years. We characterize the overwintering process with spatial statistics and use a stochastic, spatially explicit, modeling approach with Bayesian parameter estimation to study the spread of the infection during the growing season. Our modeling approach allows us to infer coevolutionary signals from spatiotemporal data on pathogen prevalence. Most importantly, we are able to assess the distribution of resistant hosts within the distribution of all host plants. We show that resistant hosts occur in areas with high pathogen encounter rates, and that the occurrence of resistance correlates with overwintering probability of the pathogen. The estimates for essentially all model parameters are characterized by a large amount of variation over the years and the populations. While the variation in the fraction of resistant hosts and in the force of infection is to a large extent explained by the population, the other model parameters (two parameters describing the shape of the dispersal kernel) vary essentially in an unpredictable manner, suggesting that much of the variation may occur at very fine spatial and temporal scales.     

Inferring Threat from Scientific Collections

Exact formulas for the probability of extinction or change in the conservation status of species are described for data based on frequency of sighting. These formulas generalize an expression previously described for the probability of extinction from (binary) sighting data. The formulas will be used in contexts where sightings are recorded as frequencies, such as when observations are aggregated in time. We argue that computing the probability of extinction or change in conservation status will be most useful for setting conservation priorities and in screening large data sets contained in museum and herbarium collections and in biological resource inventories.     

Inferring Metapopulation Dynamics from Patch-Level Incidence of Florida Scrub Plants

Spatial structure and dynamics of multiple populations may explain species distribution patterns in patchy communities with heterogeneous disturbance regimes, especially when species have poor dispersal. The endemic-rich Florida (U.S.A.) rosemary scrub occupies about 4% of the west portion of Archbold Biological Station and occurs scattered within a matrix of less xeric vegetation. Longer fire-return times and higher frequency of open patches in rosemary scrub provide favorable habitat for many plant species. Occupancy of 123 species of vascular plants and ground lichens in 89 patches was determined by repeated site surveys. About two-thirds of the species occurring at more than 14 patches had a significant logistic regression of presence on time-since-fire, patch size, patch isolation, or their interactions. Species with presence related to the interaction between patch isolation and patch size were primarily herbs and small shrubs specializing in rosemary scrub. These results suggest the importance of spatial characteristics of the landscape for population turnover of these species. An incidence-based metapopulation model was used to predict extinction and colonization probabilities of those species with presence in rosemary scrub patches related to the studied spatial variables. This is the first attempt to apply incidence-based metapopulation models to plants. The results showed stronger effects of patch size and patch isolation on extinction probabilities of herbs than on those of woody species. Because of their effect on spatial heterogeneity and habitat availability, fire suppression and habitat destruction may decrease persistence probabilities for these rosemary scrub specialists, many of which are endangered species.     

Inferring extinction of mammals from sighting records, threats, and biological traits

For species with five or more sightings, quantitative techniques exist to test whether a species is extinct on the basis of distribution of sightings. However, 70% of purportedly extinct mammals are known from fewer than five sightings, and such models do not include some important indicators of the likelihood of extinction such as threats, biological traits, search effort, and demography. Previously, we developed a quantitative method that we based on species' traits in which we used Cox proportional hazards regression to calculate the probability of rediscovery of species regarded as extinct. Here, we used two versions of the Cox regression model to determine the probability of extinction in purportedly extinct mammals and compared the results of these two models with those of stationary Poisson, nonparametric, and Weibull sighting-distribution models. For mammals with five or more sightings, the stationary Poisson model categorized all but two critically endangered (flagged as possibly extinct) species in our data set as extinct, and results with this model were consistent with current categories of the International Union for the Conservation of Nature. The scores of probability of rediscovery for individual species in one version of our Cox regression model were correlated with scores assigned by the stationary Poisson model. Thus, we used this Cox regression model to determine the probability of extinction of mammals with sparse records. On the basis of the Cox regression model, the most likely mammals to be rediscovered were the Montane monkey-faced bat (Pteralopex pulchra), Armenian myotis (Myotis hajastanicus), Alcorn's pocket gopher (Pappogeomys alcorni), and Wimmer's shrew (Crocidura wimmeri). The Cox model categorized two species that have recently disappeared as extinct: the baiji (Lipotes vexillifer) and the Christmas Island pipistrelle (Pipistrellus murrayi). Our new method can be used to test whether species with few records or recent last-sighting dates are likely to be extinct.     

Inferring the nature of anthropogenic threats from long‐term abundance records

Diagnosing the processes that threaten species persistence is critical for recovery planning and risk forecasting. Dominant threats are typically inferred by experts on the basis of a patchwork of informal methods. Transparent, quantitative diagnostic tools would contribute much‐needed consistency, objectivity, and rigor to the process of diagnosing anthropogenic threats. Long‐term census records, available for an increasingly large and diverse set of taxa, may exhibit characteristic signatures of specific threatening processes and thereby provide information for threat diagnosis. We developed a flexible Bayesian framework for diagnosing threats on the basis of long‐term census records and diverse ancillary sources of information. We tested this framework with simulated data from artificial populations subjected to varying degrees of exploitation and habitat loss and several real‐world abundance time series for which threatening processes are relatively well understood: bluefin tuna (Thunnus maccoyii) and Atlantic cod (Gadus morhua) (exploitation) and Red Grouse (Lagopus lagopus scotica) and Eurasian Skylark (Alauda arvensis) (habitat loss). Our method correctly identified the process driving population decline for over 90% of time series simulated under moderate to severe threat scenarios. Successful identification of threats approached 100% for severe exploitation and habitat loss scenarios. Our method identified threats less successfully when threatening processes were weak and when populations were simultaneously affected by multiple threats. Our method selected the presumed true threat model for all real‐world case studies, although results were somewhat ambiguous in the case of the Eurasian Skylark. In the latter case, incorporation of an ancillary source of information (records of land‐use change) increased the weight assigned to the presumed true model from 70% to 92%, illustrating the value of the proposed framework in bringing diverse sources of information into a common rigorous framework. Ultimately, our framework may greatly assist conservation organizations in documenting threatening processes and planning species recovery. Inferencia la Naturaleza de las Amenazas Antropogénicas para los Registros de Abundancia a Largo Plazo     

How to address data privacy concerns when using social media data in conservation science

Social media data are being increasingly used in conservation science to study human–nature interactions. User-generated content, such as images, video, text, and audio, and the associated metadata can be used to assess such interactions. A number of social media platforms provide free access to user-generated social media content. However, similar to any research involving people, scientific investigations based on social media data require compliance with highest standards of data privacy and data protection, even when data are publicly available. Should social media data be misused, the risks to individual users' privacy and well-being can be substantial. We investigated the legal basis for using social media data while ensuring data subjects’ rights through a case study based on the European Union's General Data Protection Regulation. The risks associated with using social media data in research include accidental and purposeful misidentification that has the potential to cause psychological or physical harm to an identified person. To collect, store, protect, share, and manage social media data in a way that prevents potential risks to users involved, one should minimize data, anonymize data, and follow strict data management procedure. Risk-based approaches, such as a data privacy impact assessment, can be used to identify and minimize privacy risks to social media users, to demonstrate accountability and to comply with data protection legislation. We recommend that conservation scientists carefully consider our recommendations in devising their research objectives so as to facilitate responsible use of social media data in conservation science research, for example, in conservation culturomics and investigations of illegal wildlife trade online.     

Firm ownership structure impact on corporate social responsibility: evidence from austerity U.K.

Corporate social responsibility (CSR) has become an increasingly important sustainable development issue in U.K. The main contribution of this study is to examine how firm ownership structure impacts good corporate governance and CSR in U.K. during austerity conditions. Following the financial crisis of 2007–2008, the U.K. government introduced austerity conditions which impacted firm CSR activities. From the initial sample of more than 250 firms, 50 consistently remain listed on the FTSE4good index during 2008–2012 and are analysed. The definition of CSR distinguishes voluntary and mandatory CSR construct. Findings indicate Board ownership structure and satisfactory firm performance impact on the level of voluntary CSR. Board ownership results suggest increased institutional and non-CEO shareholdings support a higher level of voluntary CSR engagement, whilst increased CEO shareholdings lead to a lower level of investment in voluntary CSR. In terms of satisfactory firm performance, results suggest positive attainment discrepancy supports a higher level of voluntary CSR, whereas greater potential organisational slack leads to a lower level of voluntary CSR investment. Effective governance and voluntary CSR association is more pronounced under conditions of high attainment discrepancy and low organisational slack. The findings suggest implications for adapting firm decision-making latitude and government policy between austerity and prosperity conditions.     

A complex social structure with fission–fusion properties can emerge from a simple foraging model

Precisely how ecological factors influence animal social structure is far from clear. We explore this question using an agent-based model inspired by the fission–fusion society of spider monkeys (Ateles spp). Our model introduces a realistic, complex foraging environment composed of many resource patches with size varying as an inverse power law frequency distribution with exponent β. Foragers do not interact among them and start from random initial locations. They have either a complete or a partial knowledge of the environment and maximize the ratio between the size of the next visited patch and the distance traveled to it, ignoring previously visited patches. At intermediate values of β, when large patches are neither too scarce nor too abundant, foragers form groups (coincide at the same patch) with a similar size frequency distribution as the spider monkey’s subgroups. Fission–fusion events create a network of associations that contains weak bonds among foragers that meet only rarely and strong bonds among those that repeat associations more frequently than would be expected by chance. The latter form subnetworks with the highest number of bonds and a high clustering coefficient at intermediate values of β. The weak bonds enable the whole social network to percolate. Some of our results are similar to those found in long-term field studies of spider monkeys and other fission–fusion species. We conclude that hypotheses about the ecological causes of fission–fusion and the origin of complex social structures should consider the heterogeneity and complexity of the environment in which social animals live.     

Ecology,feeding competition and social structure in baboons

Predictions of the model of van Schaik (1989) of female-bonding in primates are tested by systematically comparing the ecology, level of within-group contest competition for food (WGC), and patterns of social behaviour found in two contrasting baboon populations. Significant differences were found in food distribution (percentage of the diet from clumped sources), feeding supplant rates and grooming patterns. In accord with the model, the tendencies of females to affiliate and form coalitions with one another, and to be philopatric, were strongest where ecological conditions promoted WGC. Group fission in the population with strong WGC was “horizontal” with respect to female dominance rank, and associated with female-female aggression during a period of elevated feeding competition. In contrast, where WGC was low, females’ grooming was focused on adult males rather than other females. Recent evidence suggests that group fission here is initiated by males, tends to result in the formation of one-male groups, and is not related to feeding competition but to male-male competition for mates. An ecological model of baboon social structure is presented which incorporates the effects of female-female competition, male-male competition, and predation pressure. The model potentially accounts for wide variability in group size, group structure and social relationships within the genus Papio. Socio-ecological convergence between common baboons and hamadryas baboons, however, may be limited in some respects by phylogenetic inertia. Received: 22 April 1994/Accepted after revision: 9 December 1995     

Cyclicity in the structure of female baboon social networks

There is an established and very influential view that primate societies have identifiable, persistent social organizations. It assumes that association patterns reflect long-term strategic interests that are not qualitatively perturbed by short-term environmental variability. We used data from two baboon troops in markedly different habitats over three consecutive seasons to test this assumption. Our results demonstrate pronounced cyclicity in the extent to which females maintained differentiated relationships. When food was plentiful, the companionships identified by social network analysis in the food-scarce season disappeared and were replaced by casual acquaintanceships more representative of mere gregariousness. Data from the fourth, food-scarce, season at one site indicated that few companions were re-united. It is likely that this reflected stochastic variation in individual circumstances. These results suggest that attention could profitably be paid to the effects of short-term local contingencies on social dynamics, and has implications for current theories of primate cognitive evolution. This contribution is part of the special issue “Social Networks: new perspectives” (Guest Editors: J. Krause, D. Lusseau and R. James).     

When good neighbors don't need fences: temporal landscape partitioning among baboon social groups

Intraspecific competition is a key factor shaping space-use strategies and movement decisions in many species, yet how and when neighbors utilize shared areas while exhibiting active avoidance of one another is largely unknown. Here, we investigated temporal landscape partitioning in a population of wild baboons (Papio cynocephalus). We used global positioning system (GPS) collars to synchronously record the hourly locations of five baboon social groups for ～900 days, and we used behavioral, demographic, and life history data to measure factors affecting use of overlap areas. Annual home ranges of neighboring groups overlapped substantially, as predicted (baboons are considered non-territorial), but home ranges overlapped less when space use was assessed over shorter time scales. Moreover, neighboring groups were in close spatial proximity to one another on fewer days than predicted by a null model, suggesting an avoidance-based spacing pattern. At all time scales examined (monthly, biweekly, and weekly), time spent in overlap areas was greater during time periods when groups fed on evenly dispersed, low-quality foods. The percent of fertile females in social groups was negatively correlated with time spent in overlap areas only during weekly time intervals. This suggests that broad temporal changes in ecological resources are a major predictor of how intensively overlap areas are used, and groups modify these ecologically driven spacing patterns at short time scales based on female reproductive status. Together, these findings offer insight into the economics of territoriality by highlighting the dynamics of spacing patterns at differing time scales.     

Using culturomics and social media data to characterize wildlife consumption

Wildlife provides food, medicine, clothing, and other necessities for humans, but overexploitation can disrupt the sustainability of wildlife resources and severely threaten global biodiversity. Understanding the characteristics of consumer behavior is helpful for wildlife managers and policy makers, but the traditional survey methods are laborious and time-consuming. In contrast, culturomics may more efficiently identify the features of wildlife consumption. As a case study of the culturomics approach, we examined tiger bone wine consumption in China based on social media and Baidu search engine data. Tiger bone wine is one of the most purchased tiger products; its consumption is closely related to tiger poaching, which greatly threatens wild tiger survival. We searched a popular social media website for the term “tiger bone wine” and focused on posts that were originally created from 1 January 2012 to 31 December 2018. We filtered and classified posts related to the purchase, sale, or consumption of tiger bone wine and extracted information on providers, consumption motivations, year of production, and place of origin of the tiger bone wines based on the texts and photos of these posts. We found 756 posts related to tiger bone wine consumption, 113 of which mentioned providers of tiger bone wine, including friends (53%), elder relatives (37%), peer relatives (7%), and others (3%). Out of the 756 posts, 266 indicated the motivations of tiger bone wine consumption. Tiger bone wines were consumed as a tonic (34%), medicine (23%), game product (30%), and a symbol of wealth (28%). Some posts indicated ≥2 consumption motivations. These findings were consistent with the search queries from Baidu index. Such information could help develop targeted strategies for tiger conservation. The culturomics approach illustrated by our study is a rapid and cost-efficient way to characterize wildlife consumption.     

Hierarchical Bayesian Modelling of plant colonisation by winged aphids: Inferring dispersal processes by linking aerial and field count data

Understanding and modelling insect pest dispersal is an important prerequisite for designing integrated pest management programs. Nevertheless, studies investigating the dispersal of small insects in natural conditions remain scarce mainly because of the difficulty of tracking the movements of these organisms. Here we propose to use Hierarchical Bayesian Modelling (HBM) framework to gain knowledge on hidden processes that cannot be observed directly in natura, such as insect landing and insect mortality, through the definition of latent variables. An HBM describing crop colonization by winged aphids was fitted to a large dataset of field observations issued from a long term survey at a wide scale of both aerial and field densities of the bird cherry-oat aphid Rhopalosiphum padi. This study provides the first evidence that suction trap data are reliable proxies of aphid colonizing rates in cereal fields in autumn and can be a nice alternative to the very time-consuming crop sampling. The proportion of winged aphids landing in cereal fields is shown to vary between regions according to the degree of investment of local R. padi population in sexual reproduction. Results also indicate that under autumnal field conditions, less than 5% of winged aphids survive more than 10 days after landing. This HBM provides the basis of a predictive model for aphid crop colonization that fully accounts for all sources of uncertainty. It should be of great value to improve the trust of users in any decision making systems.     

Seasonal changes in the structure of rhesus macaque social networks

Social structure emerges from the patterning of interactions between individuals and plays a critical role in shaping some of the main characteristics of animal populations. The topological features of social structure, such as the extent to which individuals interact in clusters, can influence many biologically important factors, including the persistence of cooperation, and the rate of spread of disease. Yet, the extent to which social structure topology fluctuates over relatively short periods of time in relation to social, demographic, or environmental events remains unclear. Here, we use social network analysis to examine seasonal changes in the topology of social structures that emerge from socio-positive associations in adult female rhesus macaques (Macaca mulatta). Behavioral data for two different association types (grooming and spatial proximity) were collected for females in two free-ranging groups during two seasons: the mating and birth seasons. Stronger dyadic bonds resulted in social structures that were more tightly connected (i.e., of greater density) in the mating season compared to the birth season. Social structures were also more centralized around a subset of individuals and more clustered in the mating season than those in the birth season, although the latter differences were mostly driven by differences in density alone. Our results suggest a degree of temporal variation in the topological features of social structure in this population. Such variation may feed back on interactions, hence affecting the behaviors of individuals, and may therefore be important to take into account in studies of animal behavior.     

Space use and social structure in meadow voles,Microtus pennsylvanicus

Summary Free-ranging, sexually mature meadow voles (Microtus pennsylvanicus) were tracked by using radiotelemetry from June through August in Front Royal, Virginia, U.S.A. Estimates of intraspecific spacing were derived from the concurrent movements of up to 16 voles. Positions were recorded hourly for 24 h, twice per week. A total of 16 male and 15 female voles were studied during sixteen 24-h sessions.The daily ranges of males (192.3±109.7 m²) were larger and more variable than those of females (68.6±39.4 m²). Males also changed locations more frequently (Fig. 2).Adult females usually maintained territories free of other females; males overlapped considerably among themselves (Fig. 2). Males temporarily moved into the areas occupied by estrous females, indicating intrasexual competition among males for access to receptive females (Fig. 3). M. pennsylvanicus appears to be promiscuous, is socially organized into territorial, maternal-young units during the breeding season, and fits the female territorial model of population regulation.     

Understanding the effects of different social data on selecting priority conservation areas

Conservation success is contingent on assessing social and environmental factors so that cost‐effective implementation of strategies and actions can be placed in a broad social–ecological context. Until now, the focus has been on how to include spatially explicit social data in conservation planning, whereas the value of different kinds of social data has received limited attention. In a regional systematic conservation planning case study in Australia, we examined the spatial concurrence of a range of spatially explicit social values and land‐use preferences collected using a public participation geographic information system and biological data. We used Zonation to integrate the social data with the biological data in a series of spatial‐prioritization scenarios to determine the effect of the different types of social data on spatial prioritization compared with biological data alone. The type of social data (i.e., conservation opportunities or constraints) significantly affected spatial prioritization outcomes. The integration of social values and land‐use preferences under different scenarios was highly variable and generated spatial prioritizations 1.2–51% different from those based on biological data alone. The inclusion of conservation‐compatible values and preferences added relatively few new areas to conservation priorities, whereas including noncompatible economic values and development preferences as costs significantly changed conservation priority areas (48.2% and 47.4%, respectively). Based on our results, a multifaceted conservation prioritization approach that combines spatially explicit social data with biological data can help conservation planners identify the type of social data to collect for more effective and feasible conservation actions.