Explore the vast range of titles available.

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.

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.

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.

Environmental and anthropogenic factors affect the population dynamics of migratory species throughout their annual cycles. However, identifying the spatiotemporal drivers of migratory species' abundances is difficult because of extensive gaps in monitoring data. The collection of unstructured opportunistic data by volunteer (citizen science) networks provides a solution to address data gaps for locations and time periods during which structured, design‐based data are difficult or impossible to collect. To estimate population abundance and distribution at broad spatiotemporal extents, we developed an integrated model that incorporates unstructured data during time periods and spatial locations when structured data are unavailable. We validated our approach through simulations and then applied the framework to the eastern North American migratory population of monarch butterflies during their spring breeding period in eastern Texas. Spring climate conditions have been identified as a key driver of monarch population sizes during subsequent summer and winter periods. However, low monarch densities during the spring combined with very few design‐based surveys in the region have limited the ability to isolate effects of spring weather variables on monarchs. Simulation results confirmed the ability of our integrated model to accurately and precisely estimate abundance indices and the effects of covariates during locations and time periods in which structured sampling are lacking. In our case study, we combined opportunistic monarch observations during the spring migration and breeding period with structured data from the summer Midwestern breeding grounds. Our model revealed a nonstationary relationship between weather conditions and local monarch abundance during the spring, driven by spatially varying vegetation and temperature conditions. Data for widespread and migratory species are often fragmented across multiple monitoring programs, potentially requiring the use of both structured and unstructured data sources to obtain complete geographic coverage. Our integrated model can estimate population abundance at broad spatiotemporal extents despite structured data gaps during the annual cycle by leveraging opportunistic data.

Aerial surveys are a critical tool for inference of wildlife populations, yet are limited by current available methods to account for imperfect detection without marking animals. N‐mixture models use count data from replicate surveys for estimation of wildlife abundance while accounting for imperfect detection. This statistical framework was recently developed but is rarely applied to aerial surveys of terrestrial wildlife. We applied an N‐mixture model incorporating temporary emigration to a novel aerial survey design to estimate the availability, detection probability, and abundance of an unmarked population of white‐tailed deer (Odocoileus virginianus) in Michigan, USA, using surveys in 2014 and 2016. We assessed the selection of the sample unit size for the N‐mixture model in a post hoc sensitivity analysis and compared all estimates to a hierarchical distance sampling (HDS) approach. Hierarchical distance sampling is an expansion of conventional distance sampling (CDS), a standard method applied to aerial survey designs for wildlife, where HDS allows for modeling spatial variation in abundance and detection probability using environmental covariates and can account for changing population demographics. We found N‐mixture models produced similar parameter estimates to HDS with both models inferring a population increase between years, which was consistent with independently available estimates for this population (2014 N‐mixture = 402 [CI: 303.8–515.27], and HDS = 391 [CI: 297.67–504.63]; 2016 N‐mixture = 732 [CI: 572.89–924.22], and HDS = 708 [CI: 552.45–893.89]). Estimates from both models were sensitive to the sample unit size selected in an open population framework. We recommend using ecological (e.g., home range size) and statistical (e.g., sample size) considerations when selecting a sample unit size that reflects the movement ecology of a species and evaluating the sensitivity of parameters to sample unit size. In our study, N‐mixture models and HDS models afforded a practical field approach and analytical methodology that resulted in precise estimation in a population of unmarked deer over time. When used with aerial surveys in terrestrial systems where distance sampling methods may be difficult to implement, N‐mixture models can address observer error and estimate populations for conservation and management goals without marking individual animals.

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'.

Background Bone conduction devices are widely used and indicated in cases of conductive, mixed or single sided deafness where conventional hearing aids are not indicated or tolerated. Percutaneous bone-conduction devices gave satisfactory hearing outcomes but were frequently complicated by soft tissue reactions. Transcutaneous bone conduction devices were developed in order to address some of the issues related to the skin-penetrating abutment. The aim of this article is to present a systematic review of the indications, surgical technique and audiological, clinical and functional outcomes of the BAHA Attract device reported so far. Methods A systematic computer-based literature search was performed on the PubMed database as well as Scopus, Cochrane and Google Scholar. Out of 497 articles, 10 studies and 89 reported cases were finally included in our review. Results The vast majority of implanted patients were satisfied with the aesthetics of the device scoring highly at the Abbreviated Profile of Hearing Aid Benefit, Glasgow Benefit Inventory and Client Oriented Scale of Improvement. Overall, hearing outcomes, tested by various means including speech in noise, free field hearing testing and word discrimination scores showed a significant improvement. Complications included seroma or haematoma formation, numbness around the area of the flap, swelling and detachment of the sound processor from the external magnet. Conclusions The functional and audiological results presented so far in the literature have been satisfactory and the complication rate is low compared to the skin penetrating Bone Conduction Devices. Further robust trials will be needed to study the long-term outcomes and any adverse effects.

Long‐term, large‐scale monitoring of wildlife populations is an integral part of conservation research and management. However, some traditional monitoring protocols lack the information needed to account for sources of measurement error in data analyses. Ignoring measurement error, such as partial availability, imperfect detection, and species misidentification, can lead to mischaracterizations of population states and processes. Accounting for measurement error is key to robust monitoring of populations, which can inform a wide variety of decisions, including harvest, habitat restoration, and determination of the legal status of species. We undertook an effort to retroactively minimize bias in a large‐scale, long‐term monitoring program for marine birds in the Salish Sea, Washington, USA, by conducting an auxiliary study to jointly estimate components of measurement error. We built a novel model in a Bayesian framework that simultaneously harnessed human observer and photographic data types to produce estimates necessary to correct for the effects of partial availability, imperfect detection, and species misidentification. Across all 31 species identified in photographs, both observers had instances of undercounting and overcounting birds but tended to undercount (observers undercounted totals across all species on 69.3%–78.9% of transects). We estimated species‐specific correction factors that can be used to correct both historical and future counts from the Salish Sea survey, which has been running since 1992. Our novel modeling framework can be applied in other multispecies monitoring contexts where minimal photographic data can be collected for the purposes of correcting for measurement error in large‐scale, long‐term datasets.

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. A collation of data on North American monarch butterfly summer breeding and overwintering populations from 1994 to 2018, combined with seasonal covariate data, suggests an increasing role of climate change as a driver of butterfly dynamics.

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.