A multispecies hierarchical model to integrate count and distance-sampling data.

Integrated community models-an emerging framework in which multiple data sources for multiple species are analyzed simultaneously-offer opportunities to expand inferences beyond the single-species and single-data-source approaches common in ecology. We developed a novel integrated community model that combines distance sampling and single-visit count data; within the model, information is shared among data sources (via a joint likelihood) and species (via a random-effects structure) to estimate abundance patterns across a community. Parameters relating to abundance are shared between data sources, and the model can specify either shared or separate observation processes for each data source. Simulations demonstrated that the model provided unbiased estimates of abundance and detection parameters even when detection probabilities varied between the data types. The integrated community model also provided more accurate and more precise parameter estimates than alternative single-species and single-data-source models in many instances. We applied the model to a community of 11 herbivore species in the Masai Mara National Reserve, Kenya, and found considerable interspecific variation in response to local wildlife management practices: Five species showed higher abundances in a region with passive conservation enforcement (median across species: 4.5× higher), three species showed higher abundances in a region with active conservation enforcement (median: 3.9× higher), and the remaining three species showed no abundance differences between the two regions. Furthermore, the community average of abundance was slightly higher in the region with active conservation enforcement but not definitively so (posterior mean: higher by 0.20 animals; 95% credible interval: 1.43 fewer animals, 1.86 more animals). Our integrated community modeling framework has the potential to expand the scope of inference over space, time, and levels of biological organization, but practitioners should carefully evaluate whether model assumptions are met in their systems and whether data integration is valuable for their applications.

Quantifying the conservation status and abundance trends of wildlife communities with detection-nondetection data.

Effective conservation requires understanding species' abundance patterns and demographic rates across space and time. Ideally, such knowledge should be available for whole communities because variation in species' dynamics can elucidate factors leading to biodiversity losses. However, collecting data to simultaneously estimate abundance and demographic rates of communities of species is often prohibitively time intensive and expensive. We developed a multispecies dynamic N-occupancy model to estimate unbiased, community-wide relative abundance and demographic rates. In this model, detection-nondetection data (e.g., repeated presence-absence surveys) are used to estimate species- and community-level parameters and the effects of environmental factors. To validate our model, we conducted a simulation study to determine how and when such an approach can be valuable and found that our multispecies model outperformed comparable single-species models in estimating abundance and demographic rates in many cases. Using data from a network of camera traps across tropical equatorial Africa, we then used our model to evaluate the statuses and trends of a forest-dwelling antelope community. We estimated relative abundance, rates of recruitment (i.e., reproduction and immigration), and apparent survival probabilities for each species' local population. The antelope community was fairly stable (although 17% of populations [species-park combinations] declined over the study period). Variation in apparent survival was linked more closely to differences among national parks than to individual species' life histories. The multispecies dynamic N-occupancy model requires only detection-nondetection data to evaluate the population dynamics of multiple sympatric species and can thus be a valuable tool for examining the reasons behind recent biodiversity loss.

La conservación efectiva requiere del entendimiento de los patrones de abundancia de las especies a lo largo del tiempo y el espacio. Sería ideal que dicho conocimiento estuviera disponible para todas las comunidades ya que la variación en la dinámica de las especies puede esclarecer los factores que llevan a la pérdida de la biodiversidad. Sin embargo, la recolección de información para estimar simultáneamente las tasas demográficas y de abundancia de las comunidades de especies con frecuencia es cara y consume tiempo. Desarrollamos un modelo multiespecies dinámico de ocupación-N para estimar la tasa demográfica y de abundancia relativas sin sesgos y en toda la comunidad. En este modelo usamos información de detección-no detección (p. ej.: censos repetidos de presencia-ausencia) para estimar los parámetros a nivel comunitario y de especie y los efectos de los factores ambientales. Para validar nuestro modelo, realizamos un estudio de simulación para determinar cómo y cuándo dicha estrategia puede ser valiosa y descubrimos que nuestro modelo multiespecies superó a los modelos comparables de una sola especie en la estimación de las tasas demográficas y de abundancia en muchos casos. Usamos nuestro modelo con datos de una red de cámaras trampa ubicadas a lo largo de África ecuatorial para evaluar los estados y tendencias de una comunidad forestal de antílopes. Estimamos la abundancia relativa, tasa de reclutamiento (es decir, reproducción e inmigración) y las probabilidades de supervivencia aparente para la población local de cada especie. La comunidad de antílopes fue bastante estable (aunque el 17% de las poblaciones [combinaciones especie-parque] declinaron durante el periodo de estudio). La variación en la supervivencia aparente estuvo vinculada con mayor cercanía a las diferencias entre los parques nacionales que a la historia de vida de cada especie individual. El modelo multiespecies dinámico de ocupación-N requiere solamente información de detección-no detección para evaluar las dinámicas poblacionales de muchas especies simpátricas y por lo tanto puede ser una herramienta valiosa para examinar las razones detrás de la pérdida reciente de la biodiversidad.

Changes in climate drive recent monarch butterfly dynamics.

Declines in the abundance and diversity of insects pose a substantial threat to terrestrial ecosystems worldwide. Yet, identifying the causes of these declines has proved difficult, even for well-studied species like monarch butterflies, whose eastern North American population has decreased markedly over the last three decades. Three hypotheses have been proposed to explain the changes observed in the eastern monarch population: loss of milkweed host plants from increased herbicide use, mortality during autumn migration and/or early-winter resettlement and changes in breeding-season climate. Here, we use a hierarchical modelling approach, combining data from >18,000 systematic surveys to evaluate support for each of these hypotheses over a 25-yr period. Between 2004 and 2018, breeding-season weather was nearly seven times more important than other factors in explaining variation in summer population size, which was positively associated with the size of the subsequent overwintering population. Although data limitations prevent definitive evaluation of the factors governing population size between 1994 and 2003 (the period of the steepest monarch decline coinciding with a widespread increase in herbicide use), breeding-season weather was similarly identified as an important driver of monarch population size. If observed changes in spring and summer climate continue, portions of the current breeding range may become inhospitable for monarchs. Our results highlight the increasingly important contribution of a changing climate to insect declines.

Biotic and abiotic drivers of dispersion dynamics in a large-bodied tropical vertebrate, the Western Bornean orangutan.

Understanding of animal responses to dynamic resource landscapes is based largely on research on temperate species with small body sizes and fast life histories. We studied a large, tropical mammal with an extremely slow life history, the Western Bornean orangutan (Pongo pygmaeus wurmbii), across a heterogeneous natural landscape encompassing seven distinct forest types. Our goals were to characterize fluctuations in abundance, test hypotheses regarding the relationship between dispersion dynamics and resource availability, and evaluate how movement patterns are influenced by abiotic conditions. We surveyed abundance in Gunung Palung National Park, West Kalimantan, Indonesia, for 99 consecutive months and simultaneously recorded weather data and assessed fruit availability. We developed a Bayesian hierarchical distance sampling model to estimate population dispersion and assess the roles of fruit availability, rainfall, and temperature in driving movement patterns across this heterogeneous landscape. Orangutan abundance varied dramatically over space and time. Each forest type was important in sustaining more than 40% of the total orangutans on site during at least one month, as animals moved to track asynchronies in fruiting phenology. We conclude that landscape-level movements buffer orangutans against fruit scarcity, peat swamps are crucial fallback habitats, and orangutans' use of high elevation forests is strongly dependent on abiotic conditions. Our results show that orangutans can periodically occupy putative-sink habitats and be virtually absent for extended periods from habitats that are vitally important in sustaining their population, highlighting the need for long-term studies and potential risks in interpreting occurrence or abundance measures as indicators of habitat importance.

Interrelated impacts of climate and land-use change on a widespread waterbird.

Together climate and land-use change play a crucial role in determining species distribution and abundance, but measuring the simultaneous impacts of these processes on current and future population trajectories is challenging due to time lags, interactive effects and data limitations. Most approaches that relate multiple global change drivers to population changes have been based on occurrence or count data alone. We leveraged three long-term (1995-2019) datasets to develop a coupled integrated population model-Bayesian population viability analysis (IPM-BPVA) to project future survival and reproductive success for common loons Gavia immer in northern Wisconsin, USA, by explicitly linking vital rates to changes in climate and land use. The winter North Atlantic Oscillation (NAO), a broad-scale climate index, immediately preceding the breeding season and annual changes in developed land cover within breeding areas both had strongly negative influences on adult survival. Local summer rainfall was negatively related to fecundity, though this relationship was mediated by a lagged interaction with the winter NAO, suggesting a compensatory population-level response to climate variability. We compared population viability under 12 future scenarios of annual land-use change, precipitation and NAO conditions. Under all scenarios, the loon population was expected to decline, yet the steepest declines were projected under positive NAO trends, as anticipated with ongoing climate change. Thus, loons breeding in the northern United States are likely to remain affected by climatic processes occurring thousands of miles away in the North Atlantic during the non-breeding period of the annual cycle. Our results reveal that climate and land-use changes are differentially contributing to loon population declines along the southern edge of their breeding range and will continue to do so despite natural compensatory responses. We also demonstrate that concurrent analysis of multiple data types facilitates deeper understanding of the ecological implications of anthropogenic-induced change occurring at multiple spatial scales. Our modelling approach can be used to project demographic responses of populations to varying environmental conditions while accounting for multiple sources of uncertainty, an increasingly pressing need in the face of unprecedented global change.

Integrating distance sampling and presence-only data to estimate species abundance.

Integrated models combine multiple data types within a unified analysis to estimate species abundance and covariate effects. By sharing biological parameters, integrated models improve the accuracy and precision of estimates compared to separate analyses of individual data sets. We developed an integrated point process model to combine presence-only and distance sampling data for estimation of spatially explicit abundance patterns. Simulations across a range of parameter values demonstrate that our model can recover estimates of biological covariates, but parameter accuracy and precision varied with the quantity of each data type. We applied our model to a case study of black-backed jackals in the Masai Mara National Reserve, Kenya, to examine effects of spatially varying covariates on jackal abundance patterns. The model revealed that jackals were positively affected by anthropogenic disturbance on the landscape, with highest abundance estimated along the Reserve border near human activity. We found minimal effects of landscape cover, lion density, and distance to water source, suggesting that human use of the Reserve may be the biggest driver of jackal abundance patterns. Our integrated model expands the scope of ecological inference by taking advantage of widely available presence-only data, while simultaneously leveraging richer, but typically limited, distance sampling data.

Can hyena behaviour provide information on population trends of sympatric carnivores?

Mammalian carnivores are declining worldwide owing to human activities. Behavioural indicators have the potential to help identify population trends and inform conservation actions, although this area of research is understudied. We investigate whether behaviour is linked to abundance in a community of carnivores in the Masai Mara National Reserve, Kenya. Anthropogenic disturbance increased exponentially in parts of the Reserve between 1988 and 2017, mainly owing to daily incursions by large numbers of livestock and tourists. Previous research showed that hyena behaviour changed markedly during this period. Through a series of vignettes, we inquire whether hyena behaviours correlate with changes in abundance of hyenas themselves, or those of other carnivore species in the region. We find that changes in spotted hyena behaviour in disturbed areas, but not in undisturbed areas, can be linked to changes in their demography (vignette 1). We also find that declines in observed lion-hyena interactions, as well as increases in spotted hyena abundance, are probably caused by competitive release of hyenas from declining lion abundance (vignette 2). Finally, we demonstrate that in some cases, hyena behaviour and demography is linked to the density and distribution of sympatric carnivores, and that behavioural changes in hyenas can provide information on shifts within the carnivore community (vignettes 3 and 4). Our vignettes reveal intriguing relationships between behaviour and demography that should be explored in future research. Pairing behavioural studies with more traditional monitoring efforts can yield useful insights regarding population and community trends, and aid wildlife conservation and management. This article is part of the theme issue 'Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation'.

Disentangling data discrepancies with integrated population models.

A common challenge for studying wildlife populations occurs when different survey methods provide inconsistent or incomplete inference on the trend, dynamics, or viability of a population. A potential solution to the challenge of conflicting or piecemeal data relies on the integration of multiple data types into a unified modeling framework, such as integrated population models (IPMs). IPMs are a powerful approach for species that inhabit spatially and seasonally complex environments. We provide guidance on exploiting the capabilities of IPMs to address inferential discrepancies that stem from spatiotemporal data mismatches. We illustrate this issue with analysis of a migratory species, the American Woodcock (Scolopax minor), in which individual monitoring programs suggest differing population trends. To address this discrepancy, we synthesized several long-term data sets (1963-2015) within an IPM to estimate continental-scale population trends, and link dynamic drivers across the full annual cycle and complete extent of the woodcock's geographic range in eastern North America. Our analysis reveals the limiting portions of the life cycle by identifying time periods and regions where vital rates are lowest and most variable, as well as which demographic parameters constitute the main drivers of population change. We conclude by providing recommendations for resolving conflicting population estimates within an integrated modeling approach, and discuss how strategies (e.g., data thinning, expert opinion elicitation) from other disciplines could be incorporated into ecological analyses when attempting to combine multiple, incongruent data types.

Multispecies hierarchical modeling reveals variable responses of African carnivores to management alternatives.

Carnivore communities face unprecedented threats from humans. Yet, management regimes have variable effects on carnivores, where species may persist or decline in response to direct or indirect changes to the ecosystem. Using a hierarchical multispecies modeling approach, we examined the effects of alternative management regimes (i.e., active vs. passive enforcement of regulations) on carnivore abundances and group sizes at both species and community levels in the Masai Mara National Reserve, Kenya. Alternative management regimes have created a dichotomy in ecosystem conditions within the Reserve, where active enforcement of regulations maintains low levels of human disturbance in the Mara Triangle and passive enforcement of regulations in the Talek region permits multiple forms of human disturbance. Our results demonstrate that these alternative management regimes have variable effects on 11 observed carnivore species. As predicted, some species, such as African lions and bat-eared foxes, have higher population densities in the Mara Triangle, where regulations are actively enforced. Yet, other species, including black-backed jackals and spotted hyenas, have higher population densities in the Talek region where enforcement is passive. Multiple underlying mechanisms, including behavioral plasticity and competitive release, are likely causing higher black-backed jackals and spotted hyena densities in the disturbed Talek region. Our multispecies modeling framework reveals that carnivores do not react to management regimes uniformly, shaping carnivore communities by differentially producing winning and losing species. Some carnivore species require active enforcement of regulations for effective conservation, while others more readily adapt (and in some instances thrive in response) to lax management enforcement and resulting anthropogenic disturbance. Yet, high levels of human disturbance appear to be negatively affecting the majority of carnivores, with potential consequences that may permeate throughout the rest of the ecosystem. Community approaches to monitoring carnivores should be adopted as single species monitoring may overlook important intra-community variability.