Ecosphere最新文献

Wildlife crossing structures and road calming measures are frequently implemented to improve habitat connectivity and mitigate the risks of wildlife–vehicle collisions on roads. Although Southeast Asia is a known biodiversity hotspot, majority of studies assessing effectiveness of such structures were conducted outside the region. Existing studies also tended to be non-comparative and focused on crossing structures in silo. We addressed this gap by simultaneously surveying and comparing the usage frequency of mammals across three crossing types—culverts, roads and rope bridges—along a road surrounded by forests in Singapore. This allowed us to evaluate the preferences of mammals, assess usage patterns and investigate factors influencing the use of different crossing types. Using camera traps and closed-circuit televisions, we documented 1133 independent crossing events across a 9-month study period from March to November 2021. Fourteen mammal species were detected across all crossing types, with wild boar (Sus scrofa) being the most prevalent species (68.2%). Locally critically endangered species such as the Malayan porcupine (Hystrix brachyura), Sunda pangolin (Manis javanica), and Raffles' banded langur (Presbytis femoralis femoralis) were also recorded to use culverts, roads, and rope bridges for crossings, respectively. Although many species used multiple crossing types, most species had a preferred crossing method. Between culverts and roads, factors that influenced crossing preferences included group size for wild boars and time of day and presence of fences for other mammals. Even though such culverts were intended for drainage, they were still widely used by mammals. Overall, all three crossing methods were crucial in facilitating the movement of animals between habitats. Thus, a variety of infrastructure and measures to accommodate the diversity of wildlife moving across forest patches in a fragmented landscape is recommended.

City leaders are setting ambitious plans to achieve critical urban sustainability goals such as reducing urban heat, mitigating flooding during storms, and conserving biodiversity, and increasingly rely on urban forests as a key nature-based solution to such challenges. Current paradigms of urban forest management typically prioritize goals like increasing tree canopy cover that are often viewed as proxies for increased ecosystem service provision, in a general sense. However, urban foresters, the professionals with responsibility to manage urban forests, are increasingly faced with the complex challenge of managing for new goals related to ecosystem services, biodiversity, or people–nature relationships, as cities increasingly set goals centered on such outcomes, without robust guidelines to follow. We ask: How can urban foresters align their street tree management actions with specific urban sustainability goals? We conducted a structured expert elicitation of urban forest professionals in three cities: Vancouver, British Columbia, Canada; Honolulu, Hawaiʻi, United States; and Washington, DC, United States. A socio-ecological lens was used to examine urban foresters as agents of change in urban ecosystems. Participants assessed the impacts of 40 direct management actions on five goals: (1) canopy cover increase and tree risk reduction, (2) urban heat reduction, (3) people–nature relationships, (4) wildlife habitat, and (5) stormwater interception and infiltration. While certain actions (e.g., in the mature tree maintenance phase) were selected as needed to advance every goal, experts identified numerous actions which aligned with one or several goals, but not all. Preplanting actions, specifically site selection and species selection, presented the greatest opportunities to advance specific goals, suggesting that aligning this phase with city sustainability goals is critical. Participants were highly confident in being able to advance all goals through street tree management, but were more confident in being able to advance the goals of increasing canopy cover while reducing tree risk and of mitigating urban heat, possibly because these goals more closely align with traditional canopy cover goal setting. This research underscores the necessity of considering site-level ecosystem management actions to advance strategic sustainability goals, while also revealing the complexity of the role and responsibilities of professionals who manage urban ecosystems.

As human development has expanded, so has the prevalence of anthropogenic noise, which can interfere with acoustic channels used by wildlife. However, since landscapes contain natural sources of persistent noise from features such as rivers, animals may be preadapted to cope with novel human-generated noise. Comparing how animals respond to anthropogenic and natural noise with similar properties can provide insight into how and why animals may respond to increasing noise pollution. Here, we studied whether Mexican free-tailed bats (Tadarida brasiliensis) alter activity and call features in response to playback of traffic and river noise relative to ambient acoustics. We observed higher call activity during ambient and traffic playback compared with river playback conditions. These findings suggest that T. brasiliensis may select areas based on acoustic properties and not by associating river sounds with more favorable habitat. We also observed that T. brasiliensis modified call duration and frequency in response to river playbacks in a manner consistent with a behavioral switch from foraging to navigating. Overall, we found evidence that temporal patterning of noise may strongly influence bat activity and that signaling in noise may involve trade-offs among call features.

Prevention and early detection of invasive species are championed as the most cost-effective and efficient strategies for reducing or preventing negative impacts on ecosystems. Spotted lanternfly (SLF), Lycorma delicatula, is a recently introduced invasive insect whose range in the United States has been expanding rapidly since it was first discovered in Pennsylvania in 2014. Feeding by this planthopper can cause severe impacts on agricultural production, particularly grapes (Vitis spp.). Human visual surveys are the most common search method employed for detection but can be ineffective due to the insect's cryptic egg masses and low density during early stages of infestation. Therefore, finding alternative early detection methods has become a priority for agencies tasked with addressing SLF management. This study experimentally tested whether trained detector dogs could improve the probability of detecting SLF in both agricultural and forest settings. We surveyed transects in 20 vineyards and their adjacent wooded areas in Pennsylvania and New Jersey, USA, and used a multiscale occupancy model to estimate detection probability achieved by human observers and detection dogs as a function of SLF infestation level, weather, and habitat covariates. We modeled transect-level occupancy of SLF as a function of infestation level, habitat type, topographic position index, and distance to forests. Occupancy probability of SLF was higher on vines within vineyards than in forests, and occupancy declined with increasing distance from forests, which is informative for future search efforts. Detection probability of SLF was lower at forested sites but was higher at high infestation sites. Detection dogs had a lower detection probability than humans in the vineyards, but the detection probability of dogs was >3× greater than that of humans in forested sites. Our study suggests that detection dogs are more effective than human visual searches as an early detection method for SLF in forested areas, and utilizing detector dogs could strengthen SLF early detection efforts. This study demonstrates the potential applicability of using canine-assisted search strategies combined with occupancy models to enhance the surveillance and prevention of other difficult-to-detect invasive species.

Species of conservation concern are often habitat specialists, posing significant risk to those species when specific plant communities are threatened. As a result, practitioners habitually focus conservation efforts on these communities while ignoring ecological mechanisms that explain the wildlife–plant relationships. In doing so, practitioners may overlook alternative vegetation communities that could maintain wildlife populations under alternative conditions (e.g., climate change). Here, we term these areas surrogate habitat, defined as vegetation communities or resource sites that provide similar critical resources as conventional sites, and assess their potential for conservation using a case study of greater sage-grouse (Centrocercus urophasianus) on Parker Mountain, Utah (1998–2009). Sage-grouse are a sagebrush-obligate species and a species of conservation concern. Range-wide conservation efforts have long emphasized management of seasonal habitats within semiarid sagebrush ecosystems, specifically management of mesic or wet meadow sites that provide brood-rearing habitat required for population persistence. Despite this requirement, no conventional mesic habitat exists on Parker Mountain, yet it supports one of Utah's largest sage-grouse populations. Rather, the Parker sagebrush system abuts quaking aspen (Populus tremuloides) stands that may provide brood-rearing habitat analogous to wet meadow sites. It is unclear, however, to what extent sage-grouse use these aspen stands because sage-grouse commonly avoid tall structures (e.g., trees) and their associated avian predators. Thus, we tested whether (1) sage-grouse selected for surrogate habitat (i.e., aspen edge) and (2) selection behaviors related to surrogate habitat had demographic effects on the population. As we predicted, sage-grouse selected for these areas, and the sage-grouse that spent increased time closer to aspen edges did not experience increased mortality. Together, this demonstrates that the aspen–sagebrush edge provided a surrogate for the wet meadows used by other populations. More broadly, this suggests that conservation practitioners should move beyond simplistic wildlife–habitat associations toward a more holistic view of animal ecology focused on the wildlife–resource association, an approach that becomes particularly useful in areas where conventional obligate habitat may be degraded or lost. This work also implores us to examine alternative habitat potential rather than applying one-size-fits-all models to threatened species conservation.

To understand how and where biodiversity is threatened, it is imperative to build historical baselines that accurately characterize the present and past states of biodiversity across environments. Botanical collections provide important ecological, evolutionary, and biogeographic information on the diversity and distributions of plant taxa, yet biases in collection efforts across spatial, temporal, and taxonomic scales are well known. Here, we characterize and quantify trends in botanical collections made from across different abiotic, biotic, and sociopolitical boundaries within the present-day state of New Mexico. Using a biodiversity informatics approach applied toward a regional case study, we identify opportunities for efficiently improving natural history collection coverage and analyses of botanical diversity. Accurate representation of botanical biodiversity, preserved for future generations through vouchered plant specimens deposited in herbaria, depends on collection decisions made now. This work aims to provide a useful workflow for synthesizing digitized regional botanical collections as researchers prioritize current and future resources in the face of global change.

We applied a previously published livestock foot-and-mouth disease (FMD) model to estimate host connectivity using a transmission kernel based on contact tracing and measured subsequent to an animal movement ban in the 2001 United Kingdom epidemic. Connectivity within county-level farm landscapes were evaluated by considering the transmission kernel, host species composition, farm-level susceptibility, farm-level transmissibility, and distances between farms. The objectives were to (1) determine the impact of connectivity of the initially infected farm on the size of FMD outbreaks in four counties of differing connectivity; (2) compare FMD spread in counties that have different mean farm connectivity levels over all farms in the county; and (3) determine how connectivity relationships affect an example control measure. We used a spatially explicit stochastic model to simulate FMD outbreaks on livestock farm operations in four UK counties. Nine farms were selected for seeding infection, with each representing a different level of farm connectivity, measured by the sum of transmission rates between itself and all other farms. In each simulated outbreak, one farm was seeded with infection. In addition to the culling of infected premises within 24 h of being reported, as implemented in the FMD model, control of epidemic spread was implemented by culling farms within 1 and 3 km radii around infected premises. Increased levels of connectivity of the initially infected farm had a significant incremental effect on the epidemic length and the number of farms, cattle, and sheep lost. However, at higher levels of farm connectivity, these incremental effects were observed to plateau. Results showed significant variation in the level of overall farm connectivity between the counties, and counties with higher overall farm connectivity experienced increased simulated losses. Connectivity of the initially infected farm and mean connectivity among all farms in a county were strongly associated with effects of cull size, with disease control more effective at lower levels of farm connectivity. Host connectivity provides early information on the host-pathogen landscape and could be used as an assessment tool for predicting epidemic risks, as well as enabling preemptive control strategies to limit the size of disease outbreaks.

Climate change threatens biodiversity as populations can persist if they migrate or adapt to the rapidly changing conditions of the Anthropocene. However, the metabolic mechanisms underlying plant population persistence under the long-term trends of increasing temperature and drought remain unclear. Here, we investigate the persistence and adaptation of yarrow (Achillea millefolium L.) populations over 100 years of climate change. We resurveyed historical sites spanning a broad climatic gradient (from 1 m to 3200 m above sea level) and analyzed metabolic diversity in a common-garden experiment. We report that nine out of ten populations persisted locally, showing phenotypic and metabolic differentiation. The only population potentially extirpated is that of the hottest and driest site. A complex interaction between increasing temperatures and changing precipitation patterns shaped plant growth across populations. Populations from warmer sites in coastal and mountain regions grew taller than 100 years ago, whereas populations from drier sites in lowlands and foothills became shorter. Furthermore, we document differentiation in metabolic diversity involving plant defenses and stress response. These findings suggest that ongoing adaptation is constrained by long-term changes in temperature and precipitation as well as by local biotic interactions. Preserving locally adapted populations and their metabolic diversity is key for conservation efforts in the face of accelerating climate change.

Studies of community assembly often explore the role of niche selection in limiting the diversity of functional traits (underdispersion) or increasing the diversity of functional traits (overdispersion) within local communities. While these patterns have primarily been explored with morphological functional traits related to environmental tolerances and resource acquisition, plant metabolomics may provide an additional functional dimension of community assembly to expand our understanding of how niche selection changes along environmental gradients. Here, we examine how the functional diversity of leaf secondary metabolites and traditional morphological plant traits changes along local environmental gradients in three temperate forest ecosystems across North America. Specifically, we asked whether co-occurring tree species exhibit local-scale over- or underdispersion of metabolomic and morphological traits, and whether differences in trait dispersion among local communities are associated with environmental gradients of soil resources and topography. Across tree species, we find that most metabolomic traits are not correlated with morphological traits, adding a unique dimension to functional trait space. Within forest plots, metabolomic traits tended to be overdispersed while morphological traits tended to be underdispersed. Additionally, local environmental gradients had site-specific effects on metabolomic and morphological trait dispersion patterns. Taken together, these results show that different suites of traits can result in contrasting patterns of functional diversity along environmental gradients and suggest that multiple community assembly mechanisms operate simultaneously to structure functional diversity in temperate forest ecosystems.

Species distribution models (SDMs) have become increasingly popular for making ecological inferences, as well as predictions to inform conservation and management. In predictive modeling, practitioners often use correlative SDMs that only evaluate a single spatial scale and do not account for differences in life stages. These modeling decisions may limit the performance of SDMs beyond the study region or sampling period. Given the increasing desire to develop transferable SDMs, a robust framework is necessary that can account for known challenges of model transferability. Here, we propose a comparative framework to develop transferable SDMs, which was tested using satellite telemetry data from green turtles (CheloniaChelonia mydas). This framework is characterized by a set of steps comparing among different models based on (1) model algorithm (e.g., generalized linear model vs. Gaussian process regression) and formulation (e.g., correlative model vs. hybrid model), (2) spatial scale, and (3) accounting for life stage. SDMs were fitted as resource selection functions and trained on data from the Gulf of Mexico with bathymetric depth, net primary productivity, and sea surface temperature as covariates. Independent validation datasets from Brazil and Qatar were used to assess model transferability. A correlative SDM using a hierarchical Gaussian process regression (HGPR) algorithm exhibited greater transferability than a hybrid SDM using HGPR, as well as correlative and hybrid forms of hierarchical generalized linear models. Additionally, models that evaluated habitat selection at the finest spatial scale and that did not account for life stage proved to be the most transferable in this study. The comparative framework presented here may be applied to a variety of species, ecological datasets (e.g., presence-only, presence-absence, mark-recapture), and modeling frameworks (e.g., resource selection functions, step selection functions, occupancy models) to generate transferable predictions of species–habitat associations. We expect that SDM predictions resulting from this comparative framework will be more informative management tools and may be used to more accurately assess climate change impacts on a wide array of taxa.