MMI: Multimodel inference or models with management implications?

Effects of vehicle traffic on space use and road crossings of caribou in the Arctic

Assessing the effects of industrial development on wildlife is a key objective of managers and conservation practitioners. However, wildlife responses are often only investigated with respect to the footprint of infrastructure, even though human activity can strongly mediate development impacts. In Arctic Alaska, there is substantial interest in expanding energy development, raising concerns about the potential effects on barren-ground caribou (Rangifer tarandus granti). While caribou generally avoid industrial infrastructure, little is known about the role of human activity in moderating their responses, and whether managing activity levels could minimize development effects. To address this uncertainty, we examined the influence of traffic volume on caribou summer space use and road crossings in the Central Arctic Herd within the Kuparuk and Milne Point oil fields on the North Slope of Alaska. We first modeled spatiotemporal variation in hourly traffic volumes across the road system from traffic counter data using gradient-boosted regression trees. We then used generalized additive models to estimate nonlinear step selection functions and road-crossing probabilities from collared female caribou during the post-calving and insect harassment seasons, when they primarily interact with roads. Step selection analyses revealed that caribou selected areas further from roads (~1-3 km) during the post-calving and mosquito seasons and selected areas with lower traffic volumes during all seasons, with selection probabilities peaking when traffic was <5 vehicles/h. Using road-crossing models, we found that caribou were less likely to cross roads during the insect seasons as traffic increased, but that response dissipated as insect harassment became more severe. Past studies suggested that caribou exhibit behavioral responses when traffic exceeds 15 vehicles/h, but our results demonstrate behavioral responses at much lower traffic levels. Our results illustrate that vehicle activity mediates caribou responses to road infrastructure, information that can be used in future land-use planning to minimize the behavioral responses of caribou to industrial development in sensitive Arctic landscapes.

Logging, linear features, and human infrastructure shape the spatial dynamics of wolf predation on an ungulate neonate

Humans are increasingly recognized as important players in predator-prey dynamics by modifying landscapes. This trend has been well-documented for large mammal communities in North American boreal forests: logging creates early seral forests that benefit ungulates such as white-tailed deer (Odocoileus virginianus), while the combination of infrastructure development and resource extraction practices generate linear features that allow predators such as wolves (Canis lupus) to travel and forage more efficiently throughout the landscape. Disturbances from recreational activities and residential development are other major sources of human activity in boreal ecosystems that may further alter wolf-ungulate dynamics. Here, we evaluate the influence that several major types of anthropogenic landscape modifications (timber harvest, linear features, and residential infrastructure) have on where and how wolves hunt ungulate neonates in a southern boreal forest ecosystem in Minnesota, USA. We demonstrate that each major anthropogenic disturbance significantly influences wolf predation of white-tailed deer fawns (n = 427 kill sites). In contrast with the "human shield hypothesis" that posits prey use human-modified areas as refuge, wolves killed fawns closer to residential buildings than expected based on spatial availability. Fawns were also killed within recently-logged areas more than expected. Concealment cover was higher at kill sites than random sites, suggesting wolves use senses other than vision, probably olfaction, to detect hidden fawns. Wolves showed strong selection for hunting along linear features, and kill sites were also closer to linear features than expected. We hypothesize that linear features facilitated wolf predation on fawns by allowing wolves to travel efficiently among high-quality prey patches (recently logged areas, near buildings), and also increase encounter rates with olfactory cues that allow them to detect hidden fawns. These findings provide novel insight into the strategies predators use to hunt ungulate neonates and the many ways human activity alters wolf-ungulate neonate predator-prey dynamics, which have remained elusive due to the challenges of locating sites where predators kill small prey. Our research has important management and conservation implications for wolf-ungulate systems subjected to anthropogenic pressures, particularly as the range of overlap between wolves and deer expands and appears to be altering food web dynamics in boreal ecosystems.

Soil seed bank responses to edge effects in temperate European forests

Aim

The amount of forest edges is increasing globally due to forest fragmentation and land-use changes. However, edge effects on the soil seed bank of temperate forests are still poorly understood. Here, we assessed edge effects at contrasting spatial scales across Europe and quantified the extent to which edges can preserve the seeds of forest specialist plants.

Location

Temperate European deciduous forests along a 2,300-km latitudinal gradient.

Time period

2018-2021.

Major taxa studied

Vascular plants.

Methods

Through a greenhouse germination experiment, we studied how edge effects alter the density, diversity, composition and functionality of forest soil seed banks in 90 plots along different latitudes, elevations and forest management types. We also assessed which environmental conditions drive the seed bank responses at the forest edge versus interior and looked at the relationship between the seed bank and the herb layer species richness.

Results

Overall, 10,108 seedlings of 250 species emerged from the soil seed bank. Seed density and species richness of generalists (species not only associated with forests) were higher at edges compared to interiors, with a negative influence of C : N ratio and litter quality. Conversely, forest specialist species richness did not decline from the interior to the edge. Also, edges were compositionally, but not functionally, different from interiors. The correlation between the seed bank and the herb layer species richness was positive and affected by microclimate.

Main conclusions

Our results underpin how edge effects shape species diversity and composition of soil seed banks in ancient forests, especially increasing the proportion of generalist species and thus potentially favouring a shift in community composition. However, the presence of many forest specialists suggests that soil seed banks still play a key role in understorey species persistence and could support the resilience of our fragmented forests.

Local variation in the timing and advancement of lake ice breakup and impacts on settling dynamics in a migratory waterbird

Timing of ice-out is important to fundamental hydrological and ecological processes in freshwater ecosystems at high northern latitudes. While earlier ice-out in lakes during the last century is a well-documented phenomenon across the Northern Hemisphere, local variation in the rate of advancement of ice-out has received little attention. Here, records of ice-out date in 1991-2020 from 37 small lakes in a boreal catchment area in southeastern Finland were used to study variation in the timing of ice-out and its advancement. In addition, data of settling phenology of migratory common goldeneyes (Bucephala clangula) at the study lakes were used to examine how between-year and within-season variation in the timing of ice-out affects lake settlement of the species. Overall, ice-out date (IOD, the timing of ice break-up in the spring) advanced 9.8 days during the 30-year study period, April temperature being more important than winter temperature (severity) in determining the IOD. Rate of the advancement of IOD in individual lakes varied from 1.5 to 16.1 days, having advanced more in relatively larger lakes. Lakes at higher elevations had later mean IOD than lakes at lower elevations. Within-season differences among the lakes in IOD increased from 1991 to 2020, this variation being mainly driven by temperature during the ice melting period. Lakes with late mean IOD were settled later in a season by breeding common goldeneyes than lakes with early IOD. The faster the ice melting progressed within a season, the faster common goldeneyes settled the breeding lakes. The results demonstrate how global warming differently affects IOD in boreal lakes even within the same catchment area. More research in the landscape context is needed to enhance our understanding of changes in IOD in boreal lakes and how differently advancing IOD affects local dynamics of species dependent on open water.

Conceptual and methodological advances in habitat-selection modeling: guidelines for ecology and evolution

Habitat selection is a fundamental animal behavior that shapes a wide range of ecological processes, including animal movement, nutrient transfer, trophic dynamics and population distribution. Although habitat selection has been a focus of ecological studies for decades, technological, conceptual and methodological advances over the last 20 yr have led to a surge in studies addressing this process. Despite the substantial literature focused on quantifying the habitat-selection patterns of animals, there is a marked lack of guidance on best analytical practices. The conceptual foundations of the most commonly applied modeling frameworks can be confusing even to those well versed in their application. Furthermore, there has yet to be a synthesis of the advances made over the last 20 yr. Therefore, there is a need for both synthesis of the current state of knowledge on habitat selection, and guidance for those seeking to study this process. Here, we provide an approachable overview and synthesis of the literature on habitat-selection analyses (HSAs) conducted using selection functions, which are by far the most applied modeling framework for understanding the habitat-selection process. This review is purposefully non-technical and focused on understanding without heavy mathematical and statistical notation, which can confuse many practitioners. We offer an overview and history of HSAs, describing the tortuous conceptual path to our current understanding. Through this overview, we also aim to address the areas of greatest confusion in the literature. We synthesize the literature outlining the most exciting conceptual advances in the field of habitat-selection modeling, discussing the substantial ecological and evolutionary inference that can be made using contemporary techniques. We aim for this paper to provide clarity for those navigating the complex literature on HSAs while acting as a reference and best practices guide for practitioners.

Monitoring Plant Diversity to Support Agri-Environmental Schemes: Evaluating Statistical Models Informed by Satellite and Local Factors in Southern European Mountain Pastoral Systems

The spatial monitoring of plant diversity in the endangered species-rich grasslands of European mountain pastoral systems is an important step for fairer and more efficient Agri-Environmental policy schemes supporting conservation. This study assessed the underlying support for a spatially explicit monitoring of plant species richness at parcel level (policy making scale) in Southern European mountain grasslands, with statistical models informed by Sentinel-2 satellite and environmental factors. Twenty-four grassland parcels were surveyed for species richness in the Peneda-Gerês National Park, northern Portugal. Using a multi-model inference approach, three competing hypotheses guided by the species-scaling theoretical framework were established: species–area (P1), species–energy (P2) and species–spectral heterogeneity (P3), each representing a candidate spatial pathway to predict species richness. To evaluate the statistical support of each spatial pathway, generalized linear models were fitted and model selection based on Akaike information criterion (AIC) was conducted. Later, the performance of the most supported spatial pathway(s) was assessed using a leave-one-out cross validation. A model guided by the species–energy hypothesis (P2) was the most parsimonious spatial pathway to monitor plant species richness in mountain grassland parcels (P2, AICc = 137.6, ∆AIC = 0.0, wi = 0.97). Species–area and species–spectral heterogeneity pathways (P1 and P3) were less statistically supported (ΔAICc values in the range 5.7–10.0). The underlying support of the species–energy spatial pathway was based on Sentinel-2 satellite data, namely on the near-infrared (NIR) green ratio in the spring season (NIR/Greenspring) and on its ratio of change between spring and summer (NIR/Greenchange). Both predictor variables related negatively to species richness. Grassland parcels with lower values of near-infrared (NIR) green ratio and lower seasonal amplitude presented higher species richness records. The leave-one-out cross validation indicated a moderate performance of the species–energy spatial pathway in predicting species richness in the grassland parcels covered by the dataset (R2 = 0.44, RMSE = 4.3 species, MAE = 3.5 species). Overall, a species–energy framework based on Sentinel 2 data resulted in a promising spatial pathway for the monitoring of species richness in mountain grassland parcels and for informing decision making on Agri-Environmental policy schemes. The near-infrared (NIR) green ratio and its change in time seems a relevant variable to deliver predictions for plant species richness and further research should be conducted on that.

Intraspecific Competition, Habitat Quality, Niche Partitioning, and Causes of Intrasexual Territoriality for a Reintroduced Carnivoran

Animals exploring a new environment develop cognitive maps using diverse sensory input and, thereby, gain information needed to establish home ranges. Experiencing, and learning information about, resources should be advantageous to the resident of a home range while lack of such information should put invaders into the home range at a disadvantage. Conspecifics, especially, should avoid the home ranges of one another to ensure that they do not experience reduced resource availability caused by resource depression or depletion. Yet, encountering conspecific competitors of different sexes may elicit responses that can lead to spacing on a landscape that has different costs and benefits on males and females. We tested the hypothesis that female fishers (Pekania pennanti) avoid competition from both males and female conspecifics whereas male fishers avoid competition only from other males. We reintroduced fishers onto our study site in the presence or absence of competitors’ home ranges during late 2009 through 2011. Using satellite transmitters (Argos) and land-based (VHF) telemetry, we monitored fishers and estimated their locations, movements and use of the surrounding landscape during their first 500 days after release. All fishers settled in relatively high-quality habitat but females that encountered the home ranges of conspecifics moved farther, explored larger areas, and settled farther from their release locations than did females that did not encounter a conspecific’s home range. Male fishers exhibited diverse responses upon encountering the home ranges of conspecifics. Thus, female fishers avoid conspecific competition from all fishers, but males tolerate, or impose, competition with females, apparently to increase mating opportunities. These observations are consistent with the movements and strategies of other solitary carnivores.

Quantifying effects of snow depth on caribou winter range selection and movement in Arctic Alaska

BACKGROUND

Caribou and reindeer across the Arctic spend more than two thirds of their lives moving in snow. Yet snow-specific mechanisms driving their winter ecology and potentially influencing herd health and movement patterns are not well known. Integrative research coupling snow and wildlife sciences using observations, models, and wildlife tracking technologies can help fill this knowledge void.

METHODS

Here, we quantified the effects of snow depth on caribou winter range selection and movement. We used location data of Central Arctic Herd (CAH) caribou in Arctic Alaska collected from 2014 to 2020 and spatially distributed and temporally evolving snow depth data produced by SnowModel. These landscape-scale (90 m), daily snow depth data reproduced the observed spatial snow-depth variability across typical areal extents occupied by a wintering caribou during a 24-h period.

RESULTS

We found that fall snow depths encountered by the herd north of the Brooks Range exerted a strong influence on selection of two distinct winter range locations. In winters with relatively shallow fall snow depth (2016/17, 2018/19, and 2019/20), the majority of the CAH wintered on the tundra north of the Brooks Range mountains. In contrast, during the winters with relatively deep fall snow depth (2014/15, 2015/16, and 2017/18), the majority of the CAH caribou wintered in the mountainous boreal forest south of the Brooks Range. Long-term (19 winters; 2001-2020) monitoring of CAH caribou winter distributions confirmed this relationship. Additionally, snow depth affected movement and selection differently within these two habitats: in the mountainous boreal forest, caribou avoided areas with deeper snow, but when on the tundra, snow depth did not trigger significant deep-snow avoidance. In both wintering habitats, CAH caribou selected areas with higher lichen abundance, and they moved significantly slower when encountering deeper snow.

CONCLUSIONS

In general, our findings indicate that regional-scale selection of winter range is influenced by snow depth at or prior to fall migration. During winter, daily decision-making within the winter range is driven largely by snow depth. This integrative approach of coupling snow and wildlife observations with snow-evolution and caribou-movement modeling to quantify the multi-facetted effects of snow on wildlife ecology is applicable to caribou and reindeer herds throughout the Arctic.

Psychological drivers of risk-reducing behaviors to limit human-wildlife conflict

Conflicts between people and wild animals are increasing globally, often with serious consequences for both. Local regulations or ordinances are frequently used to promote human behaviors that minimize these conflicts (risk-reducing behaviors), but compliance with ordinances can be highly variable. While efforts to increase compliance could be improved through applications of conservation psychology, little is known about the relative influence of different factors motivating compliance. Using concepts from psychology and risk theory, we conducted a longitudinal study pairing data from mail surveys with direct observations of compliance with a wildlife ordinance requiring residents to secure residential garbage from black bears (Ursus americanus). We assessed the relative influence of beliefs and attitudes toward bears and bear proofing, perceived behavioral control, perceived risks and benefits assigned to bears, norms, trust in management, previous experience with conflicts, and demographics on compliance behavior (i.e., bear proofing). Data on previous experience were obtained through direct observation and survey reports. We found that higher compliance rates were associated with more observed conflicts on a respondent's block. Counter to expectations, however, residents were less compliant when they were more trusting of the management agency and perceived more benefits from bears. We suggest that messages have the potential to increase compliance when they empower residents by linking successful management of conflicts to individual actions and emphasize how reducing conflicts could maintain benefits provided by wildlife. Modifying existing educational materials to account for these psychological considerations and evaluating their impact on compliance behavior over time are important next steps in changing human behaviors relevant to the globally important problem of human-wildlife conflict.

Dormant season grazing on northern mixed grass prairie agroecosystems: Does protein supplement intake, cow age, weight and body condition impact beef cattle resource use and residual vegetation cover?

Dormant season livestock grazing reduces reliance on harvested feeds, but typically requires protein supplementation to maintain animal performance. Individual variation in supplement intake can impact animal performance; however, it is unknown if this variation leads to individual or herd-level effects on grazing behavior, resource utilization, and grazing impacts to native rangelands. To examine effects of protein supplementation on dormant season cattle resource use and, subsequently, post-grazing habitat conditions, we examined cattle grazing behavior, resource utilization and biomass removal of vegetation on a native rangeland in Montana. A commercial herd of 272 (yr 1) and 302 (yr 2) cows grazed a 329-ha rangeland pasture from November to January. Intake of a 30% crude protein supplement was measured for each individual. Five individuals within each of six age groups were equipped with GPS collars. Time spent grazing declined with supplement intake ([Formula: see text] = -0.05 ± 0.02; P < 0.01). Distance traveled per day had a positive asymptotic association with supplement intake ([Formula: see text] = 0.35 ± 0.09; P < 0.01). On average, resource utilization by cattle grazing dormant season forage decreased with terrain ruggedness ([Formula: see text] = -0.09 ± 0.03), but was unrelated to aspect, temperature and wind speed. Notably, we observed high individual variability in resource utilization for elevation, distance from supplement and water. A post-hoc analysis suggested that individual attributes (age, body weight, supplement intake) influenced cattle resource use. At moderate stocking rates, dormant season livestock grazing did not affect residual vegetation conditions (P values > 0.22). However, residual cover of forbs and litter increased with relative grazing intensity ([Formula: see text] = 1.04 ± 0.41; [Formula: see text] = 3.06 ± 0.89; P ≤ 0.05). In summary, high individual variability in grazing resource utilization of cattle suggests individual-level factors could be the dominant drivers in grazing behavior and landscape use.

Species Identity and Size are Associated with Rat Lungworm Infection in Gastropods

Angiostrongylus cantonensis, the rat lungworm, is an emerging zoonotic pathogen that cycles between definitive rat and intermediate gastropod hosts. Zoonotic infection occurs when humans intentionally or accidentally consume infectious larvae in a gastropod host, and may manifest as neuroangiostrongyliasis, characterized by eosinophilic meningitis, severe neurological impairment, and even death. Thus, the risk of A. cantonensis zoonoses may be related to the distribution of A. cantonensis larvae across gastropod hosts. We screened 16 gastropod species from 14 communities on the island of O'ahu, Hawai'i, USA, to characterize the distribution of A. cantonensis among species and across host size. Prevalence (proportion of the population infected) and infection intensity (density of worms in host tissue) varied among gastropod species. Prevalence also varied with gastropod host size, but this relationship differed among host species. Most host species showed a positive increase in the probability of infection with host size, suggesting that within species relatively larger hosts had higher prevalence. The density of worms in an infected snail was unrelated to host size. These results suggest that variation in A. cantonensis infection is associated with demographic structure and composition of gastropod communities, which could underlie heterogeneity in the risk of human angiostrongyliasis across landscapes.

Resampling-based methods for biologists

Ecological data often violate common assumptions of traditional parametric statistics (e.g., that residuals are Normally distributed, have constant variance, and cases are independent). Modern statistical methods are well equipped to handle these complications, but they can be challenging for non-statisticians to understand and implement. Rather than default to increasingly complex statistical methods, resampling-based methods can sometimes provide an alternative method for performing statistical inference, while also facilitating a deeper understanding of foundational concepts in frequentist statistics (e.g., sampling distributions, confidence intervals, p-values). Using simple examples and case studies, we demonstrate how resampling-based methods can help elucidate core statistical concepts and provide alternative methods for tackling challenging problems across a broad range of ecological applications.

Scientist's guide to developing explanatory statistical models using causal analysis principles

Recent discussions of model selection and multimodel inference highlight a general challenge for researchers: how to convey the explanatory content of a hypothesized model or set of competing models clearly. The advice from statisticians for scientists employing multimodel inference is to develop a well-thought-out set of candidate models for comparison, though precise instructions for how to do that are typically not given. A coherent body of knowledge, which falls under the general term causal analysis, now exists for examining the explanatory scientific content of candidate models. Much of the literature on causal analysis has been recently developed, and we suspect may not be familiar to many ecologists. This body of knowledge comprises a set of graphical tools and axiomatic principles to support scientists in their endeavors to create "well-formed hypotheses," as statisticians are asking them to do. Causal analysis is complementary to methods such as structural equation modeling, which provides the means for evaluation of proposed hypotheses against data. In this paper, we summarize and illustrate a set of principles that can guide scientists in their quest to develop explanatory hypotheses for evaluation. The principles presented in this paper have the capacity to close the communication gap between statisticians, who urge scientists to develop well-thought-out coherent models, and scientists, who would like some practical advice for exactly how to do that.

Roosting Habitat Use by Sandhill Cranes and Waterfowl on the North and South Platte Rivers in Nebraska

Migration ecology and habitat use of spring migrating birds using the Central Platte River is a well-explored topic, yet less is known about use of the North and South Platte rivers (NSPR) in western Nebraska. The efficiency and effectiveness of conservation efforts in the NSPR could be greatly improved with access to information about where and when birds roost and landscape prioritization tools. We used aerial surveys to determine population distribution and migration phenology of sandhill cranes Antigone canadensis, Canada geese Branta canadensis, and ducks using the NSPR for roosting during the mid-February to mid-April spring migration. We used these data and geospatial information to identify important river reaches for these species and habitat covariates that discriminate between those used at lower and higher densities. We found that sandhill cranes and waterfowl generally roosted in different segments of the NSPR and, subsequently, different factors were associated with high densities. Sandhill crane density was positively correlated with distance from obstructions greater than 1 m high and negatively correlated with area of unvegetated sandbar within 1 km. Density of Canada geese and ducks was high in segments positively associated with wetland and sand pit habitats. Human disturbance variables such as roads and bridges in this rural region had little effect on identification of roosting areas used by high densities of all groups. On the basis of our results, habitat conservation efforts that specifically target sandhill cranes will not have similar positive effects on waterfowl use and distribution in the NSPR. Our identification of the most important river segments should allow managers to better target land acquisition or management resources to areas that will have the greatest effect on either waterfowl or sandhill cranes during spring migration.

Determinants of home-range size of imperiled New England cottontails (Sylvilagus transitionalis) and introduced eastern cottontails (Sylvilagus floridanus)

In fragmented habitat, population persistence depends in part on patch quality and patch size relative to home-range size. The imperiled New England cottontail (Sylvilagus transitionalis (Bangs, 1895)) is an obligate user of shrublands in the northeastern United States, a highly fragmented and declining ecosystem. New England cottontail conservation efforts have targeted habitat creation; however, efforts are hindered by a limited knowledge of seasonal space use and its relationship to habitat quality, which could help inform minimum patch-size requirements and implications of competition with non-native eastern cottontails (Sylvilagus floridanus (J.A. Allen, 1890)). To address these uncertainties, we modeled home-range areas for both species as a function of season, patch size, sex, and two indicators of forage and cover availability. Home range was generally inversely correlated with measures of forage and cover resources and the response differed by season and species and did not vary with patch size. Instead, inclusion of matrix habitat within home ranges increased with decreasing patch size, placing individuals within smaller patches at a high risk of mortality. These risks may be mitigated in patches >7 ha and absent in patches >20–25 ha where predicted inclusion of matrix is lower or absent.

Impact of prey occupancy and other ecological and anthropogenic factors on tiger distribution in Thailand's western forest complex

Despite conservation efforts, large mammals such as tigers (Panthera tigris) and their main prey, gaur (Bos gaurus), banteng (Bos javanicus), and sambar (Rusa unicolor), are highly threatened and declining across their entire range. The only large viable source population of tigers in mainland Southeast Asia occurs in Thailand's Western Forest Complex (WEFCOM), an approximately 19,000 km2 landscape of 17 contiguous protected areas.We used an occupancy modeling framework, which accounts for imperfect detection, to identify the factors that affect tiger distribution at the approximate scale of a female tiger's home range, 64 km2, and site use at a scale of 1-km2. At the larger scale, we estimated the proportion of sites at WEFCOM that were occupied by tigers; at the finer scale, we identified the key variables that influence site-use and developed a predictive distribution map. At both scales, we examined key anthropogenic and ecological factors that help explain tiger distribution and habitat use, including probabilities of gaur, banteng, and sambar occurrence from a companion study.Occupancy estimated at the 64-km2 scale was primarily influenced by the combined presence of all three large prey species, and 37% or 5,858 km2 of the landscape was predicted to be occupied by tigers. In contrast, site use estimated at the scale of 1 km2 was most strongly influenced by the presence of sambar.By modeling occupancy while accounting for imperfect probability of detection, we established reliable benchmark data on the distribution of tigers in WEFCOM. This study also identified factors that limit tiger distributions; which managers can then target to expand tiger distribution and guide recovery elsewhere in Southeast Asia.

Partitioning global change: Assessing the relative importance of changes in climate and land cover for changes in avian distribution

Understanding the relative impact of climate change and land cover change on changes in avian distribution has implications for the future course of avian distributions and appropriate management strategies. Due to the dynamic nature of climate change, our goal was to investigate the processes that shape species distributions, rather than the current distributional patterns. To this end, we analyzed changes in the distribution of Eastern Wood Pewees (Contopus virens) and Red-eyed Vireos (Vireo olivaceus) from 1997 to 2012 using Breeding Bird Survey data and dynamic correlated-detection occupancy models. We estimated the local colonization and extinction rates of these species in relation to changes in climate (hours of extreme temperature) and changes in land cover (amount of nesting habitat). We fit six nested models to partition the deviance explained by spatial and temporal components of land cover and climate. We isolated the temporal components of environmental variables because this is the essence of global change. For both species, model fit was significantly improved when we modeled vital rates as a function of spatial variation in climate and land cover. Model fit improved only marginally when we added temporal variation in climate and land cover to the model. Temporal variation in climate explained more deviance than temporal variation in land cover, although both combined only explained 20% (Eastern Wood Pewee) and 6% (Red-eyed Vireo) of temporal variation in vital rates. Our results showing a significant correlation between initial occupancy and environmental covariates are consistent with biological expectation and previous studies. The weak correlation between vital rates and temporal changes in covariates indicated that we have yet to identify the most relevant components of global change influencing the distributions of these species and, more importantly, that spatially significant covariates are not necessarily driving temporal shifts in avian distributions.

Temporal Variation in Breeding Season Survival and Cause-Specific Mortality of Lesser Prairie-Chickens

The lesser prairie-chicken Tympanuchus pallidicinctus has experienced significant declines in distribution and abundance since the early 1900s. A severe and prolonged drought from 2009 to 2013 resulted in further declines in population numbers and despite improved environmental and habitat conditions since 2013, populations of lesser prairie chickens have shown little improvement. To investigate whether breeding season survival of lesser prairie-chickens in eastern New Mexico could be driving this response, we developed the following objectives: 1) estimate male and female breeding-season survival; 2) determine whether male and female survival varies temporally among lekking, nesting, and brood-rearing periods; and 3) determine cause-specific mortality during the breeding season. We captured and radiocollared 76 lesser prairie-chickens (50 male, 26 female) during spring of 2014 and 2015 and estimated their survival throughout the breeding season (15 March–31 August). Male survival was nearly double that of females in both years (0.79–0.81 and 0.38–0.45, respectively). Males had similar survival across all periods (lekking, postlekking, late summer: 0.89–0.95). Females had the greatest period-specific survival during lekking and brood rearing (0.87 ± 0.08 and 0.85 ± 0.10, respectively) relative to the nesting period (0.58 ± 0.11). Mammalian predation was the primary cause of mortality in both years. Our results indicate that in New Mexico 1) lesser prairie-chicken breeding season survival was consistent with geographically similar studies, 2) females have lower survival during the nesting period, and 3) female lesser prairie-chicken survival was lower than male survival regardless of time period. Management actions that provide and protect high-quality nesting habitat may help ensure that female survival is maximized during the nesting period.

A brief introduction to mixed effects modelling and multi-model inference in ecology

The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.

Ecoinformatics (Big Data) for Agricultural Entomology: Pitfalls, Progress, and Promise

Ecoinformatics, as defined in this review, is the use of preexisting data sets to address questions in ecology. We provide the first review of ecoinformatics methods in agricultural entomology. Ecoinformatics methods have been used to address the full range of questions studied by agricultural entomologists, enabled by the special opportunities associated with data sets, nearly all of which have been observational, that are larger and more diverse and that embrace larger spatial and temporal scales than most experimental studies do. We argue that ecoinformatics research methods and traditional, experimental research methods have strengths and weaknesses that are largely complementary. We address the important interpretational challenges associated with observational data sets, highlight common pitfalls, and propose some best practices for researchers using these methods. Ecoinformatics methods hold great promise as a vehicle for capitalizing on the explosion of data emanating from farmers, researchers, and the public, as novel sampling and sensing techniques are developed and digital data sharing becomes more widespread.

Considerations for assessing model averaging of regression coefficients

Model choice is usually an inevitable source of uncertainty in model-based statistical analyses. While the focus of model choice was traditionally on methods for choosing a single model, methods to formally account for multiple models within a single analysis are now accessible to many researchers. The specific technique of model averaging was developed to improve predictive ability by combining predictions from a set of models. However, it is now often used to average regression coefficients across multiple models with the ultimate goal of capturing a variable's overall effect. This use of model averaging implicitly assumes the same parameter exists across models so that averaging is sensible. While this assumption may initially seem tenable, regression coefficients associated with particular explanatory variables may not hold equivalent interpretations across all of the models in which they appear, making explanatory inference about covariates challenging. Accessibility to easily implementable software, concerns about being criticized for ignoring model uncertainty, and the chance to avoid having to justify choice of a final model have all led to the increasing popularity of model averaging in practice. We see a gap between the theoretical development of model averaging and its current use in practice, potentially leaving well-intentioned researchers with unclear inferences or difficulties justifying reasons for using (or not using) model averaging. We attempt to narrow this gap by revisiting some relevant foundations of regression modeling, suggesting more explicit notation and graphical tools, and discussing how individual model results are combined to obtain a model averaged result. Our goal is to help researchers make informed decisions about model averaging and to encourage question-focused modeling over method-focused modeling.

When Winners Become Losers: Predicted Nonlinear Responses of Arctic Birds to Increasing Woody Vegetation

Climate change is facilitating rapid changes in the composition and distribution of vegetation at northern latitudes, raising questions about the responses of wildlife that rely on arctic ecosystems. One widely observed change occurring in arctic tundra ecosystems is an increasing dominance of deciduous shrub vegetation. Our goals were to examine the tolerance of arctic-nesting bird species to existing gradients of vegetation along the boreal forest-tundra ecotone, to predict the abundance of species across different heights and densities of shrubs, and to identify species that will be most or least responsive to ongoing expansion of shrubs in tundra ecosystems. We conducted 1,208 point counts on 12 study blocks from 2012–2014 in northwestern Alaska, using repeated surveys to account for imperfect detection of birds. We considered the importance of shrub height, density of low and tall shrubs (i.e. shrubs >0.5 m tall), percent of ground cover attributed to shrubs (including dwarf shrubs <0.5 m tall), and percent of herbaceous plant cover in predicting bird abundance. Among 17 species considered, only gray-cheeked thrush (Catharus minimus) abundance was associated with the highest values of all shrub metrics in its top predictive model. All other species either declined in abundance in response to one or more shrub metrics or reached a threshold where further increases in shrubs did not contribute to greater abundance. In many instances the relationship between avian abundance and shrubs was nonlinear, with predicted abundance peaking at moderate values of the covariate, then declining at high values. In particular, a large number of species were responsive to increasing values of average shrub height with six species having highest abundance at near-zero values of shrub height and abundance of four other species decreasing once heights reached moderate values (≤ 33 cm). Our findings suggest that increases in shrub cover and density will negatively affect abundance of only a few bird species and may potentially be beneficial for many others. As shrub height increases further, however, a considerable number of tundra bird species will likely find habitat increasingly unsuitable.