Niche tracking and rapid establishment of distributional equilibrium in the house sparrow show potential responsiveness of species to climate change.

The ability of species to respond to novel future climates is determined in part by their physiological capacity to tolerate climate change and the degree to which they have reached and continue to maintain distributional equilibrium with the environment. While broad-scale correlative climatic measurements of a species' niche are often described as estimating the fundamental niche, it is unclear how well these occupied portions actually approximate the fundamental niche per se, versus the fundamental niche that exists in environmental space, and what fitness values bounding the niche are necessary to maintain distributional equilibrium. Here, we investigate these questions by comparing physiological and correlative estimates of the thermal niche in the introduced North American house sparrow (Passer domesticus). Our results indicate that occupied portions of the fundamental niche derived from temperature correlations closely approximate the centroid of the existing fundamental niche calculated on a fitness threshold of 50% population mortality. Using these niche measures, a 75-year time series analysis (1930-2004) further shows that: (i) existing fundamental and occupied niche centroids did not undergo directional change, (ii) interannual changes in the two niche centroids were correlated, (iii) temperatures in North America moved through niche space in a net centripetal fashion, and consequently, (iv) most areas throughout the range of the house sparrow tracked the existing fundamental niche centroid with respect to at least one temperature gradient. Following introduction to a new continent, the house sparrow rapidly tracked its thermal niche and established continent-wide distributional equilibrium with respect to major temperature gradients. These dynamics were mediated in large part by the species' broad thermal physiological tolerances, high dispersal potential, competitive advantage in human-dominated landscapes, and climatically induced changes to the realized environmental space. Such insights may be used to conceptualize mechanistic climatic niche models in birds and other taxa

Lazarus ecology: Recovering the distribution and migratory patterns of the extinct Carolina parakeet.

The study of the ecology and natural history of species has traditionally ceased when a species goes extinct, despite the benefit to current and future generations of potential findings. We used the extinct Carolina parakeet as a case study to develop a framework investigating the distributional limits, subspecific variation, and migratory habits of this species as a means to recover important information about recently extinct species. We united historical accounts with museum collections to develop an exhaustive, comprehensive database of every known occurrence of this once iconic species. With these data, we combined species distribution models and ordinal niche comparisons to confront multiple conjectured hypotheses about the parakeet's ecology with empirical data on where and when this species occurred. Our results demonstrate that the Carolina parakeet's range was likely much smaller than previously believed, that the eastern and western subspecies occupied different climatic niches with broad geographical separation, and that the western subspecies was likely a seasonal migrant while the eastern subspecies was not. This study highlights the novelty and importance of collecting occurrence data from published observations on extinct species, providing a starting point for future investigations of the factors that drove the Carolina parakeet to extinction. Moreover, the recovery of lost autecological knowledge could benefit the conservation of other parrot species currently in decline and would be crucial to the success of potential de-extinction efforts for the Carolina parakeet

Camera trap arrays improve detection probability of wildlife: Investigating study design considerations using an empirical dataset.

Camera trapping is a standard tool in ecological research and wildlife conservation. Study designs, particularly for small-bodied or cryptic wildlife species often attempt to boost low detection probabilities by using non-random camera placement or baited cameras, which may bias data, or incorrectly estimate detection and occupancy. We investigated the ability of non-baited, multi-camera arrays to increase detection probabilities of wildlife. Study design components were evaluated for their influence on wildlife detectability by iteratively parsing an empirical dataset (1) by different sizes of camera arrays deployed (1-10 cameras), and (2) by total season length (1-365 days). Four species from our dataset that represented a range of body sizes and differing degrees of presumed detectability based on life history traits were investigated: white-tailed deer (Odocoileus virginianus), bobcat (Lynx rufus), raccoon (Procyon lotor), and Virginia opossum (Didelphis virginiana). For all species, increasing from a single camera to a multi-camera array significantly improved detection probability across the range of season lengths and number of study sites evaluated. The use of a two camera array increased survey detection an average of 80% (range 40-128%) from the detection probability of a single camera across the four species. Species that were detected infrequently benefited most from a multiple-camera array, where the addition of up to eight cameras produced significant increases in detectability. However, for species detected at high frequencies, single cameras produced a season-long (i.e, the length of time over which cameras are deployed and actively monitored) detectability greater than 0.75. These results highlight the need for researchers to be critical about camera trap study designs based on their intended target species, as detectability for each focal species responded differently to array size and season length. We suggest that researchers a priori identify target species for which inference will be made, and then design camera trapping studies around the most difficult to detect of those species

Co-occurrence of invasive and native carnivorans affects occupancy patterns across environmental gradients

Understanding species interactions and their effects on distributions is crucial for assessing the impacts of global change, particularly for invasive species. Co-occurrence models can help investigate these effects when interactions are likely given shared traits. For such an assemblage of invasive and native carnivorans, we examined how patterns of co-occurrence change across space and environmental gradients using a static multispecies occupancy model that accounts for imperfect detectability and models co-occurrence as a function of environmental variables, and also extended it to be temporally dynamic. We focused on invasive raccoons, which pose threats to humans and wildlife globally. In Japan, raccoons prey on many native taxa, but little is known about interactions with sympatric carnivorans. We searched for signals of competitive exclusion of native raccoon dogs (tanuki) and invasive masked palm civets by applying the model to detection data from a broad-scale trapping effort over 6 years. Forest cover was the strongest predictor of occupancy for individual species and raccoon co-occurrences, and raccoon occupancy probability increased with forest cover conditionally depending on the co-occurring carnivoran: only tanuki absence or civet presence had positive responses. However, tanuki occupancy probability increased with forest cover despite any co-occurrence. Thus, we found no evidence of competitive exclusion by raccoons, contrary to our expectations. As parts of the world with invasive raccoons can also have invasive tanuki, our findings may have broad management implications. The model we present should be useful for inferring signals of biotic interactions between species with low detectability over multi-year time frames

Opportunities and challenges for big data ornithology

Recent advancements in information technology and data acquisition have created both new research opportunities and new challenges for using big data in ornithology. We provide an overview of the past, present, and future of big data in ornithology, and explore the rewards and risks associated with their application. Structured data resources (e.g., North American Breeding Bird Survey) continue to play an important role in advancing our understanding of bird population ecology, and the recent advent of semistructured (e.g., eBird) and unstructured (e.g., weather surveillance radar) big data resources has promoted the development of new empirical perspectives that are generating novel insights. For example, big data have been used to study and model bird diversity and distributions across space and time, explore the patterns and determinants of broad-scale migration strategies, and examine the dynamics and mechanisms associated with geographic and phenological responses to global change. The application of big data also holds a number of challenges wherein high data volume and dimensionality can result in noise accumulation, spurious correlations, and incidental endogeneity. In total, big data resources continue to add empirical breadth and detail to ornithology, often at very broad spatial extents, but how the challenges underlying this approach can best be mitigated to maximize inferential quality and rigor needs to be carefully considered. Los avances recientes en la tecnolog´ıa de la informaci ´on y la adquisici ´on de datos han creado tanto nuevas oportunidades de investigaci ´on como desaf´ıos para el uso de datos masivos (big data) en ornitolog´ıa. Brindamos una visi ´on general del pasado, presente y futuro de los datos masivos en ornitolog´ıa y exploramos las recompensas y desaf´ıos asociados a su aplicaci ´ on. Los recursos de datos estructurados (e.g., Muestreo de Aves Reproductivas de Am´erica del Norte) siguen jugando un rol importante en el avance de nuestro entendimiento de la ecolog´ıa de poblaciones de las aves, y el advenimiento reciente de datos masivos semi-estructurados (e.g., eBird) y desestructurados (e.g., radar de vigilancia clima´tica) han promovido el desarrollo de nuevas perspectivas emp´ıricas que esta´n generando miradas novedosas. Por ejemplo, los datos masivos han sido usados para estudiar y modelar la diversidad y distribuci ´on de las aves a trav´es del tiempo y del espacio, explorar los patrones y los determinantes de las estrategias de migraci ´on a gran escala, y examinar las dina´micas y los mecanismos asociados con las respuestas geogra´ficas y fenol ´ ogicas al cambio global. La aplicaci ´on de datos masivos tambi´en contiene una serie de desaf´ıos donde el gran volumen de datos y la dimensionalidad pueden generar una acumulaci ´on de ruido, correlaciones espurias y endogeneidad incidental. En total, los recursos de datos masivos contin ´uan agregando amplitud y detalle emp´ırico a la ornitolog´ıa, usualmente a escalas espaciales muy amplias, pero necesita considerarse cuidadosamente c ´omo los desaf´ıos que subyacen este enfoque pueden ser mitigados del mejor modo para maximizar su calidad inferencial y rigor

A roadmap for pyrodiversity science

Abstract: Background: Contemporary and projected shifts in global fire regimes highlight the importance of understanding how fire affects ecosystem function and biodiversity across taxa and geographies. Pyrodiversity, or heterogeneity in fire history, is often an important driver of biodiversity, though it has been largely overlooked until relatively recently. In this paper, we synthesise previous research to develop a theoretical framework on pyrodiversity–biodiversity relationships and propose future research and conservation management directions. Theoretical Framework: Pyrodiversity may affect biodiversity by diversifying available ecological niches, stabilising community networks and/or supporting diverse species pools available for post‐fire colonisation. Further, pyrodiversity's effects on biodiversity vary across different spatial, temporal and organismal scales depending on the mobility and other life history traits of the organisms in question and may be mediated by regional eco‐evolutionary factors such as historical fire regimes. Developing a generalisable understanding of pyrodiversity effects on biodiversity has been challenging, in part because pyrodiversity can be quantified in various ways. Applying the Pyrodiversity Concept: Exclusion of Indigenous fire stewardship, fire suppression, increased unplanned ignitions and climate change have led to dramatic shifts in fire regimes globally. Such shifts include departures from historic levels of pyrodiversity and add to existing challenges to biodiversity conservation in fire‐prone landscapes. Managers navigating these challenges can be aided by targeted research into observed contemporary pyrodiversity–biodiversity relationships as well as knowledge of historical reference conditions informed by both Indigenous and local ecological knowledge and western science. Future Research Directions: Several promising avenues exist for the advancement of pyrodiversity science to further both theoretical and practical goals. These lines of investigation include but are not limited to (1) testing the increasing variety of pyrodiversity metrics and analytical approaches; (2) assessing the spatial and temporal scale‐dependence of pyrodiversity's influence; (3) reconstructing historical pyrodiversity patterns and developing methods for predicting and/or promoting future pyrodiversity; and (4) expanding the focus of pyrodiversity science beyond biodiversity to better understand its influence on ecosystem function and processes more broadly

Addressing data integration challenges to link ecological processes across scales

Data integration is a statistical modeling approach that incorporates multiple data sources within a unified analytical framework. Macrosystems ecology – the study of ecological phenomena at broad scales, including interactions across scales – increasingly employs data integration techniques to expand the spatiotemporal scope of research and inferences, increase the precision of parameter estimates, and account for multiple sources of uncertainty in estimates of multiscale processes. We highlight four common analytical challenges to data integration in macrosystems ecology research: data scale mismatches, unbalanced data, sampling biases, and model development and assessment. We explain each problem, discuss current approaches to address the issue, and describe potential areas of research to overcome these hurdles. Use of data integration techniques has increased rapidly in recent years, and given the inferential value of such approaches, we expect continued development and wider application across ecological disciplines, especially in macrosystems ecology

Designing count‐based studies in a world of hierarchical models

Abstract: Advances in hierarchical modeling have improved estimation of ecological parameters from count data, especially those quantifying population abundance, distribution, and dynamics by explicitly accounting for observation processes, particularly incomplete detection. Even hierarchical models that account for incomplete detection, however, cannot compensate for data limitations stemming from poorly planned sampling. Ecologists therefore need guidance for planning count‐based studies that follow established sampling theory, collect appropriate data, and apply current modeling approaches to answer their research questions. We synthesize available literature relevant to guiding count‐based studies. Considering the central historical and ongoing contributions of avian studies to ecological knowledge, we focus on birds as a case study for this review, but the basic principles apply to all populations whose members are sufficiently observable to be counted. The sequence of our review represents the thought process in which we encourage ecologists to engage 1) the research question(s) and population parameters to measure, 2) sampling design, 3) analytical framework, 4) temporal design, and 5) survey protocol. We also provide 2 hypothetical demonstrations of these study plan components representing different research questions and study systems. Mirroring the structure of hierarchical models, we suggest researchers primarily focus on the ecological processes of interest when designing their approach to sampling, and wait to consider logistical constraints of data collection and observation processes when developing the survey protocol. We offer a broad framework for researchers planning count‐based studies, while pointing to relevant literature elaborating on particular tools and concepts

Differential response of three large mammal species to human recreation in the Rocky Mountains of Colorado, USA

Outdoor recreation benefits local economies, environmental education, and public health and wellbeing, but it can also adversely affect local ecosystems. Human presence in natural areas alters feeding and reproductive behaviors, physiology, and population structure in many wildlife species, often resulting in cascading effects through entire ecological communities. As outdoor recreation gains popularity, existing trails are becoming overcrowded and new trails are being built to accommodate increasing use. Many recreation impact studies have investigated effects of the presence or absence of humans while few have investigated recreation effects on wildlife using a gradient of disturbance intensity. We used camera traps to quantify trail use by humans and mid- to large-sized mammals in an area of intense outdoor recreation–the Upper East River Valley, Colorado, USA. We selected five trails with different types and intensities of human use and deployed six cameras on each trail for five weeks during a COVID-enhanced 2020 summer tourism season. We used occupancy models to estimate detectability and habitat use of the three most common mammal species in the study area and determined which human activities affect the habitat use patterns of each species. Human activities affected each species differently. Mule deer (Odocoileus hemionus) tended to use areas with more vehicles, more predators, and greater distances from the trailhead, and they were more likely to be detected where there were more bikers. Coyotes (Canis latrans) and red foxes (Vulpes vulpes) were most likely to use areas where their prey species occurred, and foxes were more likely to be detected where the vegetation was shorter. Humans and their recreational activities differentially influence different species. More generally, these results reinforce that it is unlikely that a single management policy is suitable for all species and management should thus be tailored for each target species