Dispersal connectivity and reserve selection for marine conservation

Although larval dispersal is crucial for the persistence of most marine populations, dispersal connectivity between sites is rarely considered in designing marine protected area networks. In particular the role of structural characteristics (known as topology) for the network of larval dispersal routes in the conservation of metapopulations has not been addressed. To determine reserve site configurations that provide highest persistence values with respect to their connectivity characteristics, we model nine connectivity topological models derived from graph theory in a demographic metapopulation model. We identify reserve site configurations that provide the highest persistence values for each of the metapopulation connectivity models. Except for the minimally connected and fully connected populations, we observed two general ‘rules of thumb’ for optimising the mean life time for all topological models: firstly place the majority of reserves, so that they are neighbours of each other, on the sites where the number of connections between the populations is highest (hub), secondly when the reserves have occupied the majority of the vertices in the hub, then select another area of high connectivity and repeat. If there are no suitable hubs remaining then distribute the remaining reserves to isolated locations optimising contact with non-reserved sites.     

Spatial uncertainty analysis of population models

This paper describes an approach for conducting spatial uncertainty analysis of spatial population models, and illustrates the ecological consequences of spatial uncertainty for landscapes with different properties. Spatial population models typically simulate birth, death, and migration on an input map that describes habitat. Typically, only a single “reference” map is available, but we can imagine that a collection of other, slightly different, maps could be drawn to represent a particular species’ habitat. As a first approximation, our approach assumes that spatial uncertainty (i.e., the variation among values assigned to a location by such a collection of maps) is constrained by characteristics of the reference map, regardless of how the map was produced. Our approach produces lower levels of uncertainty than alternative methods used in landscape ecology because we condition our alternative landscapes on local properties of the reference map. Simulated spatial uncertainty was higher near the borders of patches. Consequently, average uncertainty was highest for reference maps with equal proportions of suitable and unsuitable habitat, and no spatial autocorrelation. We used two population viability models to evaluate the ecological consequences of spatial uncertainty for landscapes with different properties. Spatial uncertainty produced larger variation among predictions of a spatially explicit model than those of a spatially implicit model. Spatially explicit model predictions of final female population size varied most among landscapes with enough clustered habitat to allow persistence. In contrast, predictions of population growth rate varied most among landscapes with only enough clustered habitat to support a small population, i.e., near a spatially mediated extinction threshold. We conclude that spatial uncertainty has the greatest effect on persistence when the amount and arrangement of suitable habitat are such that habitat capacity is near the minimum required for persistence.     

Habitat selection models to account for seasonal persistence in radio telemetry data

Models for the analysis of habitat selection data incorporate covariates in an independent multinomial selections model (McCracken et al. 1998) Ramsey and Usner 2003 and an extension of that model to include a persistence parameter (2003). In both cases, all parameters are assumed to be fixed through time. Radio telemetry data collected for habitat selection studies typically consist of animal relocations through time, suggesting the need for an extension to these models. We use a Bayesian approach that allows for the habitat selection probabilities, persistence parameter, or both, to change with season. These extensions are particularly important when movement patterns are expected to differ seasonally and/or when availabilities of habitats change throughout the study period due to weather or migration. We implement and compare the models using radio telemetry data for westslope cutthroat trout in two streams in eastern Oregon.     

Determining the drivers of population structure in a highly urbanized landscape to inform conservation planning

Understanding the environmental contributors to population structure is of paramount importance for conservation in urbanized environments. We used spatially explicit models to determine genetic population structure under current and future environmental conditions across a highly fragmented, human‐dominated environment in Southern California to assess the effects of natural ecological variation and urbanization. We focused on 7 common species with diverse habitat requirements, home‐range sizes, and dispersal abilities. We quantified the relative roles of potential barriers, including natural environmental characteristics and an anthropogenic barrier created by a major highway, in shaping genetic variation. The ability to predict genetic variation in our models differed among species: 11–81% of intraspecific genetic variation was explained by environmental variables. Although an anthropogenically induced barrier (a major highway) severely restricted gene flow and movement at broad scales for some species, genetic variation seemed to be primarily driven by natural environmental heterogeneity at a local level. Our results show how assessing environmentally associated variation for multiple species under current and future climate conditions can help identify priority regions for maximizing population persistence under environmental change in urbanized regions.     

Risk spreading, connectivity, and optimal reserve spacing

Two important processes determining the dynamics of spatially structured populations are dispersal and the spatial covariance of demographic fluctuations. Spatially explicit approaches to conservation, such as reserve networks, must consider the tension between these two processes and reach a balance between distances near enough to maintain connectivity, but far enough to benefit from risk spreading. Here, we model this trade-off. We show how two measures of metapopulation persistence depend on the shape of the dispersal kernel and the shape of the distance decay in demographic covariance, and we consider the implications of this trade-off for reserve spacing. The relative rates of distance decay in dispersal and demographic covariance determine whether the long-run metapopulation growth rate, and quasi-extinction risk, peak for adjacent patches or intermediately spaced patches; two local maxima in metapopulation persistence are also possible. When dispersal itself fluctuates over time, the trade-off changes. Temporal variation in mean distance that propagules are dispersed (i.e., propagule advection) decreases metapopulation persistence and decreases the likelihood that persistence will peak for adjacent patches. Conversely, variation in diffusion (the extent of random spread around mean dispersal) increases metapopulation persistence overall and causes it to peak at shorter inter-patch distances. Thus, failure to consider temporal variation in dispersal processes increases the risk that reserve spacings will fail to meet the objective of ensuring metapopulation persistence. This study identifies two phenomena that receive relatively little attention in empirical work on reserve spacing, but that can qualitatively change the effectiveness of reserve spacing strategies: (1) the functional form of the distance decay in covariance among patch-specific demographic rates and (2) temporal variation in the shape of the dispersal kernel. The sensitivity of metapopulation recovery and persistence to how covariance of vital rates decreases with distance suggests that estimating the shape of this function is likely to be as important for effective reserve design as estimating connectivity. Similarly, because temporal variation in dispersal dynamics influences the effect of reserve spacing, approaches to reserve design that ignore such variation, and rely instead on long-term average dispersal patterns, are likely to lead to lower metapopulation viability than is actually achievable.     

Predicting [corrected] extinction risk in spite of predator-prey oscillations.

Most population viability analyses (PVA) assume that the effects of species interactions are subsumed by population-level parameters. We examine how robust five commonly used PVA models are to violations of this assumption. We develop a stochastic, stage-structured predator-prey model and simulate prey population vital rates and abundance. We then use simulated data to parameterize and estimate risk for three demographic models (static projection matrix, stochastic projection matrix, stochastic vital rate matrix) and two time series models (diffusion approximation [DA], corrupted diffusion approximation [CDA]). Model bias is measured as the absolute deviation between estimated and observed quasi-extinction risk. Our results highlight three generalities about the application of single-species models to multi-species conservation problems. First, our collective model results suggest that most single-species PVA models overestimate extinction risk when species interactions cause periodic variation in abundance. Second, the DA model produces the most (conservatively) biased risk forecasts. Finally, the CDA model is the most robust PVA to population cycles caused by species interactions. CDA models produce virtually unbiased and relatively precise risk estimates even when populations cycle strongly. High performance of simple time series models like the CDA owes to their ability to effectively partition stochastic and deterministic sources of variation in population abundance.     

Modeling carcass removal time for avian mortality assessment in wind farms using survival analysis

In monitoring studies at wind farms, the estimation of bird and bat mortality caused by collision must take into account carcass removal by scavengers or decomposition. In this paper we propose the use of survival analysis techniques to model the time of carcass removal. The proposed method is applied to data collected in ten Portuguese wind farms. We present and compare results obtained from semiparametric and parametric models assuming four main competing lifetime distributions (exponential, Weibull, log-logistic and log-normal). Both homogeneous parametric models and accelerated failure time models were used. The fitted models enabled the estimation of the carcass persistence rates and the calculation of a scavenging correction factor for avian mortality estimation. Additionally, we discuss the impact that the distributional assumption can have on parameter estimation. The proposed methodology integrates the survival probability estimation problem with the analysis of covariate effects. Estimation is based on the most suitable model while simultaneously accounting for censored observations, diminishing scavenging rate estimation bias. Additionally, the method establishes a standardized statistical procedure for the analysis of carcass removal time in subsequent studies.     

Interhemispheric comparison of recruitment to intertidal communities: pattern persistence and scales of variation

Recruitment variation can be a major source of fluctuation in populations and communities, making it difficult to generalize results. Determining the scales of variation and whether spatial patterns in the supply of individuals are persistent over time can provide insight into spatial generality and the application of conservation and metacommunity models. We examined these issues using eight-year-long data sets of monthly recruitment of intertidal mussels (Mytilus spp., Perumytilus purpuratus, Semimytilus algosus, Brachidontes granulata) and barnacles (Balanus glandula, Chthamalus dalli, Jehlius cirratus, Notochthamalus scabrosus) at sites spanning > 900 km along the coasts of Oregon-northern California (OR-NCA, 45.47-39.43 degrees N) and central Chile (CC, 29.5-34.65 degrees S). We evaluated four general "null" hypotheses: that despite different phylogenies and great spatial separation of these taxa, their similar life history strategies and environmental settings lead to similar patterns of recruitment (1) between hemispheres, (2) in time, (3) in space, and (4) at larger and smaller spatial scales. Hypothesis 1 was rejected: along the OR-NCA coast, rates of recruitment were between two and three orders of magnitude higher, and patterns of seasonality were generally stronger and more coherent across space and time than along CC. Surprisingly, however, further analysis revealed regularities in both time and space for all species, supporting hypotheses 2 and 3. Temporal decorrelation scales were 1-3 months, and characteristic spatial scales of recruitment were approximately 250 km. Contrary to hypothesis 4, for the ecologically dominant species in both hemispheres, recruitment was remarkably persistent at larger mesoscales (kilometers) but was highly stochastic at smaller microscales (meters). Across species, increased recruitment variation at large scales was positively associated with increased persistence. Our results have several implications. Although the two regions span distinct latitudinal ranges, potential forcing processes behind these patterns include similar large-scale climates and topographically locked hydrographic features, such as upwelling. Further, spatial persistence of the recruitment patterns of most species at the mesoscale supports the view that marine protected areas can be powerful conservation and management tools. Finally, persistent and yet contrasting spatial patterns of recruitment among competing species suggest that recent metacommunity models might provide useful representations of the mechanisms involved in species coexistence.     

Population Viability Estimates: Theory and Practice for a Wild Gorilla Population

The logic of demographic modeling, the apparent simplicity of its quantifiably substantiated answers, and the ready availability of software correlate with increasing use of demographic modeling as the means of applying biology to the conservation of potentially endangered populations. I investigated that use by considering a small population (about 300 individuals) of a large, forest-dwelling mammal of the tropics, the Virunga gorilla ( Gorilla gorilla ) of Zaire, Uganda, and Rwanda. Because censuses of forest populations are so inaccurate and data on variance of some parameters takes so long to collect, models might not be broadly applicable. Therefore, simple demographic indices of potential extinction should replace sophisticated models. The current best index could be problematic, however, because it is based on detecting adult mortality, perhaps the most difficult demographic parameter to measure. Models of the Virunga gorilla population that incorporate aspects of demographic heterogeneity valuably indicate genetic and demographic persistence for several hundred years. Deterministic change in habitat is a greater threat than stochastic demographic variation, and yet our ecological ignorance is such that we could not begin to model the consequences of removal of even the main food plant. We must add to our ability to model outcomes of demographic perturbation a far greater understanding of the processes by which the perturbations occur. Demography allows us to model demographic response to demographic change, but we usually need ecology to tell us how the threat produced the demographic change in the first place. In a time of change, accurate prediction requires ecological understanding of process as well as demographic understanding of outcome.     

Measuring impacts on species with models and metrics of varying ecological and computational complexity

Approaches to assess the impacts of landscape disturbance scenarios on species range from metrics based on patterns of occurrence or habitat to comprehensive models that explicitly include ecological processes. The choice of metrics and models affects how impacts are interpreted and conservation decisions. We explored the impacts of 3 realistic disturbance scenarios on 4 species with different ecological and taxonomic traits. We used progressively more complex models and metrics to evaluate relative impact and rank of scenarios on the species. Models ranged from species distribution models that relied on implicit assumptions about environmental factors and species presence to highly parameterized spatially explicit population models that explicitly included ecological processes and stochasticity. Metrics performed consistently in ranking different scenarios in order of severity primarily when variation in impact was driven by habitat amount. However, they differed in rank for cases where dispersal dynamics were critical in influencing metapopulation persistence. Impacts of scenarios on species with low dispersal ability were better characterized using models that explicitly captured these processes. Metapopulation capacity provided rank orders that most consistently correlated with those from highly parameterized and data-rich models and incorporated information about dispersal with little additional computational and data cost. Our results highlight the importance of explicitly considering species’ ecology, spatial configuration of habitat, and disturbance when choosing indicators of species persistence. We suggest using hybrid approaches that are a mixture of simple and complex models to improve multispecies assessments.     

Population Persistence in Florida Torreya: Comparing Modeled Projections of a Declining Coniferous Tree

Abstract: The Florida torreya (   Torreya taxifolia ) is a coniferous tree endemic to a 35-km stretch of bluffs and ravines along the east side of the Apalachicola River in northern Florida and southern Georgia. This formerly locally abundant tree has declined as a result of disease during the 1950s and is on the U.S. endangered species list. With no seed production in the wild, this species is headed toward extinction. We conducted a survey on roughly 200 trees from 1988 to 1996 and used these data to estimate the likelihood of population persistence during the next several decades. We compared a stage-class transition matrix model ( RAMAS) and an individual-based stochastic model ( TORSIM) of growth and mortality to project future populations. Given the current lack of seed production in the wild, all models predict extinction. The question of concern is the imminence of this predicted extinction. Differing predicted times to extinction would suggest different immediate management recommendations. Both models predicted an over 90% likelihood of persistence during the next 50 years. Predictions differed in that the transition matrix model was less optimistic than the individual-based model regarding persistence. Model sensitivity analysis showed that the results were robust to significant decreases in growth and sprouting probabilities. Submodels identified different persistence likelihoods in different populations. Nonetheless, unless management of the population can facilitate maturation and seed production, extinction of this species in the wild is inevitable.     

Net energy versus efficiency maximizing by foraging ring-billed gulls

Summary Ring-billed gulls (Larus delawarensis) breeding at Dog Lake, Manitoba often feed by following tractors pulling cultivating implements around fields. Tractor-following gulls always land immediately behind the cultivating implement, where they feed on earthworms or grain. Afeer a feeding bout on the ground (patch residence time), gulls fly up, pursue the tractor and repeat the cycle. We use net energy maximizing (energy gained per unit time) and efficiency maximizing (energy gained per unit energy expended) models to make quantitative predictions of patch residence time, and compare these predictions to observations. If we assume flight speed to be constrained at the observed mean, the observations fall between the predictions of the two models, and the models explain approximately equal and highly significant proportions of the overall variation in patch residence time. If both flight speed and patch residence time are allowed to vary in the models, the efficiency maximizing model more closely predicts observed patch residence times, and the net energy maximizing model more closely predicts observed flight speeds. We discuss whether breeding ringbilled gulls may be truly intermediate between net energy and efficiency maximizing, and how measurements of flight speed may be useful in further investigations.     

Assessing hypotheses about nesting site occupancy dynamics

Hypotheses about habitat selection developed in the evolutionary ecology framework assume that individuals, under some conditions, select breeding habitat based on expected fitness in different habitat. The relationship between habitat quality and fitness may be reflected by breeding success of individuals, which may in turn be used to assess habitat quality. Habitat quality may also be assessed via local density: if high-quality sites are preferentially used, high density may reflect high-quality habitat. Here we assessed whether site occupancy dynamics vary with site surrogates for habitat quality. We modeled nest site use probability in a seabird subcolony (the Black-legged Kittiwake, Rissa tridactyla) over a 20-year period. We estimated site persistence (an occupied site remains occupied from time t to t+1) and colonization through two subprocesses: first colonization (site creation at the timescale of the study) and recolonization (a site is colonized again after being deserted). Our model explicitly incorporated site-specific and neighboring breeding success and conspecific density in the neighborhood. Our results provided evidence that reproductively "successful" sites have a higher persistence probability than "unsuccessful" ones. Analyses of site fidelity in marked birds and of survival probability showed that high site persistence predominantly reflects site fidelity, not immediate colonization by new owners after emigration or death of previous owners. There is a negative quadratic relationship between local density and persistence probability. First colonization probability decreases with density, whereas recolonization probability is constant. This highlights the importance of distinguishing initial colonization and recolonization to understand site occupancy. All dynamics varied positively with neighboring breeding success. We found evidence of a positive interaction between site-specific and neighboring breeding success. We addressed local population dynamics using a site occupancy approach integrating hypotheses developed in behavioral ecology to account for individual decisions. This allows development of models of population and metapopulation dynamics that explicitly incorporate ecological and evolutionary processes.     

Utility of Measuring Abundance versus Consistent Occupancy in Predicting Biodiversity Persistence

Abstract:  The primary goals of reserve selection are to represent all chosen units of biodiversity and to ensure their long-term persistence while minimizing costs. We considered two simple proxies of species persistence: a time series of point-count data to calculate abundance and a time series of presence–absence data to calculate permanence (a measure of consistent occupancy over time). Using two 10-year intervals of data from the North American Breeding Bird Survey, we compared the performance of each measure at predicting persistence 18 years later. For nonrare species, abundance and permanence predicted persistence similarly well. We performed complementarity-based reserve selections with data on species abundance and permanence (from 1970 to 1979) and then evaluated the effectiveness of the reserve networks at maintaining species populations and efficiency in land use (data from 1997 to 2006). Abundance proved a better predictor of future local persistence than permanence, which justifies the relatively larger financial and temporal costs of collecting a time series of point-count data to estimate abundance. If future extinction events were used as a measure of reserve-network effectiveness, the performance of abundance and permanence did not differ markedly. Nevertheless, when future abundance, which is a more sensitive measure of network effectiveness, was used, abundance was significantly better than permanence at selecting longer-term, high-quality, species-specific habitat but required larger reserves to do so .     

Strong self-limitation promotes the persistence of rare species

Theory has recognized a combination of niche and neutral processes each contributing, with varying importance, to species coexistence. However, long-term persistence of rare species has been difficult to produce in trait-based models of coexistence that incorporate stochastic dynamics, raising questions about how rare species persist despite such variability. Following recent evidence that rare species may experience significantly different population dynamics than dominant species, we use a plant community model to simulate the effect of disproportionately strong negative frequency dependence on the long-term persistence of the rare species in a simulated community. This strong self-limitation produces long persistence times for the rare competitors, which otherwise succumb quickly to stochastic extinction. The results suggest that the mechanism causing species to be rare in this case is the same mechanism allowing those species to persist.     

Comparison of statistical and theoretical habitat models for conservation planning: the benefit of ensemble prediction

Selection of a modeling approach is an important step in the conservation planning process, but little guidance is available. We compared two statistical and three theoretical habitat modeling approaches representing those currently being used for avian conservation planning at landscape and regional scales: hierarchical spatial count (HSC), classification and regression tree (CRT), habitat suitability index (HSI), forest structure database (FS), and habitat association database (HA). We focused our comparison on models for five priority forest-breeding species in the Central Hardwoods Bird Conservation Region: Acadian Flycatcher, Cerulean Warbler, Prairie Warbler, Red-headed Woodpecker, and Worm-eating Warbler. Lacking complete knowledge on the distribution and abundance of each species with which we could illuminate differences between approaches and provide strong grounds for recommending one approach over another, we used two approaches to compare models: rank correlations among model outputs and comparison of spatial correspondence. In general, rank correlations were significantly positive among models for each species, indicating general agreement among the models. Worm-eating Warblers had the highest pairwise correlations, all of which were significant (P < 0.05). Red-headed Woodpeckers had the lowest agreement among models, suggesting greater uncertainty in the relative conservation value of areas within the region. We assessed model uncertainty by mapping the spatial congruence in priorities (i.e., top ranks) resulting from each model for each species and calculating the coefficient of variation across model ranks for each location. This allowed identification of areas more likely to be good targets of conservation effort for a species, those areas that were least likely, and those in between where uncertainty is higher and thus conservation action incorporates more risk. Based on our results, models developed independently for the same purpose (conservation planning for a particular species in a particular geography) yield different answers and thus different conservation strategies. We assert that using only one habitat model (even if validated) as the foundation of a conservation plan is risky. Using multiple models (i.e., ensemble prediction) can reduce uncertainty and increase efficacy of conservation action when models corroborate one another and increase understanding of the system when they do not.     

Effect of predation threat on repeatability of individual crab behavior revealed by mark-recapture

The persistence of behavioral types in situ and the drivers of persistence are central to predicting the ecological effects of intraspecific behavioral variation. We surveyed individual refuge use of mud crabs (Panopeus herbstii), a behavior related to the strength of a trait-mediated trophic cascade in oyster reefs, in the absence and presence of toadfish (Opsanus tau) predation threat. We then released these crabs into the field and using mark-recapture, measured the repeatability of this behavior in the absence and presence of threat, and how behavioral change was affected by time in the field (a month on average, up to 81 days), crab size, and sex. Because crabs exhibited some evidence of a circatidal rhythm in refuge use, we also tested how tidal height during observation influenced behavioral change. Predation threat increased refuge use, and small crabs used the refuge more than large crabs, particularly under threat. In recaptured crabs, refuge use was more repeatable under threat. Neither time in the field, crab size, crab sex, nor tidal height had any effect on behavioral change. Our results support the non-mutually exclusive hypotheses that (1) prey organisms in the presence, rather than absence, of predation threat should exhibit less behavioral variability because the fear of dying (a severe fitness consequence) should take precedence over less immediately important influences on behavior (e.g., hunger) and that (2) individual behaviors tied to fixed traits (e.g., the body size dependence of refuge use under threat in this study), rather than variable traits, should be more repeatable over time.     

Minimise long-term loss or maximise short-term gain?: Optimal translocation strategies for threatened species

Translocation is a useful management option for conservation of threatened animal species. It can be used to increase the range of a species, augment the numbers in a critical population, or establish new populations and hence spread the risk of extinction through local catastrophes. As it is an important and expensive conservation tool, translocation management decisions must be carefully considered, with the objective of the translocation project in mind. By analysing the translocation problem within a decision-theory framework, we find optimal management decisions that are rational and transparent. We illustrate our approach using a case study of the bridled nailtail wallaby (Onychogalea fraenata). Our particular translocation question is: if we have a set number of wallabies to translocate in each time period and two translocation sites, how many wallabies should we put at each site given the state of each population to maximise the benefit to the species? We model the translocated populations with first-order Markov chain stochastic population models, and use stochastic dynamic programming to determine the optimal management decisions. We look at two sites with different growth rates – one increasing and one decreasing – and compare the optimal strategies for two different objective functions. The first is a long-term persistence objective function, which maximises the persistence of translocated populations a large number of time steps after the end of the translocation program. The second maximises total population size at the end of the translocation program. Although these objective functions are similar, they generate surprisingly different optimal translocation strategies. When maximising the long-term persistence of the translocated populations, translocation decisions are not important as long as an increasing population is established. This indicates that site quality – rather than the number and timing of translocations – primarily determines the long-term persistence of populations. When maximising total population size, the optimal strategy is to add to the increasing population unless it is above a size where it is likely to reach its carrying capacity over the planning timeframe. As translocation decisions are important in fulfilling the objective, this objective function is more useful in creating practical advice for translocation managers. The discrepancy between the optimal strategies given by the two objectives demonstrates the importance of careful consideration when specifying the goals of a project. This observation applies not only to translocation programs, but any project where clear decision-making is needed.     

Modeling the effect of photosynthetic vegetation properties on the NDVI--LAI relationship

Developing a relationship between the normalized difference vegetation index (NDVI) and the leaf area index (LAI) is essential to describe the pattern of spatial or temporal variation in LAI that controls carbon, water, and energy exchange in many ecosystem process models. Photosynthetic vegetation (PV) properties can affect the estimation of LAI, but no models integrate the effects of multiple species. We developed four alternative NDVI-LAI models, three of which integrate PV effects: no PV effects, leaf-level effects, canopy-level effects, and effects at both levels. The models were fit to data across the natural range of variation in NDVI for a widespread High Arctic ecosystem. The weight of evidence supported the canopy-level model (Akaike weight, wr = 0.98), which includes species-specific canopy coefficients that primarily scale fractional PV cover to LAI by accounting for the area of unexposed PV. Modeling the canopy-level effects improved prediction of LAI (R2 = 0.82) over the model with no PV effect (R2 = 0.71) across the natural range of variation in NDVI but did not affect the site-level estimate of LAI. Satellite-based methods to estimate species composition, a variable in the model, will need to be developed. We expect that including the effects of PV properties in NDVI-LAI models will improve prediction of LAI where species composition varies across space or changes over time.     

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.