Ecosphere最新文献

Climate change is poised to exert a significant impact on species distribution in the future, and Taxus cuspidata as an endangered species is no exception. Predicting the potential distribution of T. cuspidata is essential for decision-makers to develop conservation policies and explicitly implement conservation measures. In this study, a combined model was employed to predict potentially suitable habitats for T. cuspidata based on extant data of T. cuspidata distributions in northeastern China. Our findings suggest that mean diurnal range (bio2) and isothermality (bio3) were identified as dominant factors influencing T. cuspidata distribution. Under future climate scenarios, suitable habitat areas increased only in the SSP126 scenario in the 2070s, declining in all other scenarios. In all climate scenarios, the centroid of suitable habitats ultimately shows a trend in northward movement. Decreases in suitable habitat predominantly occurred in Yanbian Korean Autonomous Prefecture, Baishan city, and Tonghua city. Overall, this study highlights a projected habitat reduction due to climate change. Recommendations entail the strategic establishment of nature reserves and the implementation of initiatives aimed at population replenishment.

Understanding the demographic drivers of reintroduced and translocated populations is critical for species establishment and persistence. Given the large number of resources required to conduct ongoing reintroduction and reinforcement programs, additional information on population responses to management practices could help identify key actions that best benefit a species while ensuring economic feasibility. An integrated population model (IPM) can be used to assess and forecast the demographic consequences of different management practices and the interrelationship between management effects, population dynamics, and environmental change. We used an IPM to inform the potential impacts of nest management practices and global climate change on the vital rates of the Texas nesting colony of the endangered Kemp's ridley sea turtle. We used the IPM to estimate population parameters including adult survival, population growth rate, and abundance of nesting females in Texas. Additionally, given potential future scenarios for management and environmental change, we used the IPM to forecast population change and estimate the probability of persistence of the Texas population under future environmental and management scenarios. Future scenarios for nest management that had some level of management (i.e., not all nests were left in situ) resulted in population growth rates that were likely stable or increasing. Scenarios in which all nests were left in situ resulted in stable or decreasing population growth rates as well as a probability of extirpation from Texas >1 (p(N = 0 in 2100) = 0.25–0.72). Despite uncertainty in forecasted abundance and population growth rates under all scenarios, our model still predicted stable or increasing population growth rates with a continuation of current nest management practices. In contrast, we found that reduced population growth was expected with reduced nest management effort, and population decline and extirpation was expected with no management. Our IPM framework provides an example of how future management scenarios can be evaluated critically to determine population changes through time under future environmental uncertainty.

The ability to conduct cost-effective wildlife monitoring at scale is rapidly increasing due to the availability of inexpensive autonomous recording units (ARUs) and automated species recognition, presenting a variety of advantages over human-based surveys. However, estimating abundance with such data collection techniques remains challenging because most abundance models require data that are difficult for low-cost monoaural ARUs to gather (e.g., counts of individuals, distance to individuals), especially when using the output of automated species recognition. Statistical models that do not require counting or measuring distances to target individuals in combination with low-cost ARUs provide a promising way of obtaining abundance estimates for large-scale wildlife monitoring projects but remain untested. We present a case study using avian field data collected in the forests of Pennsylvania during the spring of 2020 and 2021 using both traditional point counts and passive acoustic monitoring at the same locations. We tested the ability of the Royle–Nichols and time-to-detection models to estimate the abundance of two species from detection histories generated by applying a machine-learning classifier to ARU-gathered data. We compared abundance estimates from these models with estimates from the same models fit using point-count data and to two additional models appropriate for point counts, the N-mixture model and distance models. We found that the Royle–Nichols and time-to-detection models can be used with ARU data to produce abundance estimates similar to those generated by a point-count-based study but with greater precision. ARU-based models produced confidence or credible intervals that were on average 31.9% (±11.9 SE) smaller than their point-count counterpart. Our findings were consistent across two species with differing relative abundance and habitat use patterns. The higher precision of models fit using ARU data is likely due to higher cumulative detection probability, which itself may be the result of greater survey effort using ARUs and machine-learning classifiers to sample significantly more time for focal species at any given point. Our results provide preliminary support for the use of ARUs in abundance-based study applications, and thus may afford researchers a better understanding of habitat quality and population trends, while allowing them to make more informed conservation recommendations and actions.

The Arctic has experienced greatly decreased sea ice and increased ocean temperatures in recent decades but there is a paucity of biological time-series data allowing assessment of resulting temporal variation in the region's marine ecosystems. Seabirds, as highly mobile and highly visible, upper trophic-level predators, can be valuable monitors of modifications in marine ecosystems, especially for regions lacking commercial fisheries or regular oceanographic sampling. Since 1975, we have studied annually an Arctic Alaskan colony of Mandt's black guillemot (Cepphus grylle mandtii), an ice-obligate diving seabird, specializing on Arctic cod (Boreogadus saida), the primary forage fish of the ice-associated cryopelagic ecosystem. Using multi-state capture–mark–recapture models, matrix population models, and perturbation analysis, we quantified the environmental and demographic drivers of population change from 1980 to 2019 for the individually marked population. The colony increased rapidly, from <20 to >200 breeding pairs from 1975 to 1990 in response to increased availability of nesting cavities, before experiencing intermittent declines to <50 pairs in 2021. Immigration and apparent survival were the primary demographic parameters affecting population growth with sea ice extent in late summer and fall the primary environmental driver. The initial growth occurred during a period of primarily negative winter Arctic Oscillations (WAO) and extensive summer sea ice. The decline began when an extremely positive WAO in 1989/1990 initiated changes in atmospheric and oceanographic circulation causing major reductions in summer sea ice throughout the region. The three-decade decline in the population saw plateaus or minor growth with increasing frequency of negative WAOs and increasing declines following two previously identified “tipping points” in sea ice loss. Breeding success at the study colony declined with decreased availability of Arctic cod due to sea ice loss and increasing sea surface temperature and is presumed to have occurred at the source colonies for immigrants where similar oceanographic changes were occurring. Quasi-extinction of the colony (reduction to <25 pairs) is predicted within the next two decades. The sensitivity of Mandt's black guillemot to multi-decadal changes in the Arctic's cryopelagic ecosystem makes it an excellent sentinel species for the region with its recent collapse having dire implications for the Arctic Ocean's constituent species.

Mediterranean landscapes were drastically affected by high levels of abandonment of agricultural and other land practices during the last century. These changes in land use can have significant effects on diversity patterns by altering disturbance and competition equilibria within plant communities at the landscape level. Particularly, such changes have been found to affect the patterns of phylogenetic diversity and structure by causing nonrandom losses of species through filtering effects and landscape homogenization. By investigating diversity patterns across a region submitted to high levels of land use changes, located in a (sub-) mountainous area of northwestern Greece (northern Pindus), we aimed at understanding the patterns of phylogenetic diversity and structure in relation to land abandonment and the subsequent recovery of natural vegetation. We sampled 250 vegetation plots equally divided in grasslands and forests, distributed across the different classes of land use occurring in the general study area based on the period since the last change in land use. Standardized metrics of Faith's phylogenetic diversity, mean phylogenetic distance, and mean nearest taxon distance were used to investigate phylogenetic diversity patterns across communities and different land-use regimes. A Principal Coordinates of Phylogenetic Structure analysis was employed to evaluate the variation in lineage composition among communities, and boosted regression trees were used to identify the relative influence of community differentiation (as captured by the classification of sampling plots in ecologically and floristically distinct vegetation communities), plant life strategies (competition, stress tolerance, and disturbance), and climatic, topographic, and soil variables on phylogenetic diversity metrics. Community differentiation was identified as the main driver of phylogenetic patterns. Additionally, phylogenetic diversity and structure were observed as having a statistically significantly negative correlation with disturbance, a statistically significantly positive correlation with stress tolerance, and a weaker positive correlation with competition. Phylogenetic clustering was observed for the early successional grassland communities submitted to stronger effects of disturbance, while phylogenetic randomness (or rarely overdispersion) was observed in forest communities submitted to stronger effects of competition. Finally, phylogenetic clustering of grassland communities was more evident shortly after land abandonment.

Climate change, nutrition pollution, and land use alterations influence the primary production of lakes. While light-microscopy counting remains the standard for estimating phytoplankton community composition, its expense and time-consuming nature necessitate cost-effective alternatives for seston analysis. Furthermore, estimating the contribution of seston constituents other than primary producers, or non-algal particles, is not possible with light-microscopy counting. Biotracer approach using computational methods and chemotaxonomic biomarkers such as carotenoid pigments and fatty acids have been used as an alternative in seston analysis when species-level taxonomy is not required. However, a comprehensive testing of how well carotenoid and fatty acids can be used in estimating a wide range of seston phytoplankton communities using different estimation methods is lacking. To assess the accuracy of a suite of state-of-the-art biotracer-based computational methods, namely CHEMTAX, FASTAR (Fatty Acid Source-Tracking Algorithm in R), MixSIAR, and QFASA (Quantitative Fatty Acid Signature Analysis), lake water samples were collected in 2016, 2018, 2019, 2020, and 2021 for seston composition analysis in a boreal eutrophic lake with light-microscopy counting serving as the reference for seston composition. Absolute errors between the biotracer-based estimates were calculated to evaluate method performance. A small laboratory experiment to assess the reliability of estimating the contribution of non-algal particles using the computational methods with fatty acids was also conducted. The closest alignment to light-microscopy counting in terms of absolute error was achieved when both carotenoids and fatty acids were used together in the QFASA method. For CHEMTAX, FASTAR, and MixSIAR, using carotenoids alone produced the closest results. Additionally, the estimation methods accurately assessed the proportion of non-algal particles in the seston when using fatty acid profiles, a capability not possible with light-microscopy counting. Our findings demonstrate that the biotracer approach provides a viable and cost-effective alternative to light-microscopy counting when group-level information of phytoplankton community composition suffices. Furthermore, we show that non-algal particles can be effectively estimated together with phytoplankton when using fatty acids.

The processes that influence community assembly, such as competition for resources and environmental filtering, are often scale dependent and vary across ecotones. Trait-based ecology provides a useful framework for testing which ecological processes most strongly influence local community composition, especially across environmental gradients where species diversity varies. Where environmental filtering dominates, species distributions are expected to be defined by strong turnover along environmental gradients, with more similar species occupying more similar habitats. Where interspecific competition dominates, species are expected to diverge in relative abundance and resource utilization at sites, so species can co-occur. Here, we integrate measurements of functional traits, microhabitat usage, isotopic composition (δ¹⁵N and δ¹³C), and abundance to test the importance of environmental filtering and resource/habitat partitioning in shaping a montane ground beetle species assemblage (Carabidae: Nebriini: Nebria) in the isolated, volcanic peaks of the northern Cascades Range, USA. Across species of Nebria, body size, pronotal shape, temperature preference, and isotopic enrichment varied across habitats ([gravel, rocks 10 cm–50 cm in diameter], large rocks [>50 cm in diameter], vegetation-covered rocks, and alpine [snowfields and talus]), and habitat/microhabitat features were reliable predictors of species presence. Resource consumption among mid-elevation species on Mt. Rainier—the peak with the greatest species diversity—is highly overlapping. Species turnover and nestedness varied significantly across habitat gradients and peaks throughout this region and varied nearly significantly across sites. Across habitat types and sites, more similar species are more likely to coexist. These results suggest that environmental filtering is the primary process structuring this species assemblage, although we find detailed evidence for microhabitat niche partitioning among species of Nebria at the site scale.

Animals adjust their habitat use patterns in response to changes in their physiological needs and environmental conditions. Understanding the mechanisms underlying resource selection and space use across time and space reveals effects of the environment on animals' decisions. We explored the effects of habitat availability and heterogeneity on the seasonal and annual space use and resource selection of two free-roaming feral burro (Equus asinus) populations in the United States within distinct climate and habitat conditions: the Sonoran Desert and the Colorado Plateau. As an introduced yet protected species in the United States, understanding burros' interactions with habitat elements is important for their conservation and management, as well as the conservation of sympatric wildlife. We used GPS locations of female burros (72 animals across both study areas) to delineate annual and seasonal ranges and resource selection patterns. We evaluated effects of mean and CV of habitat covariates, including forage, distance to water, and topography, representing availability and heterogeneity of resources, on seasonal and annual range size of burros. Moreover, we explored how burro seasonal and annual resource selection patterns were affected by availability and heterogeneity of resources. In the Sonoran Desert study area, burros had smaller seasonal and annual ranges and constant resource selection patterns across a year, likely due to a freshwater lake in the area, making water a nonlimiting resource. Human presence was the greatest factor affecting range size and resource selection in the Sonoran Desert, where burros selected for areas near roads and human recreation. In the Colorado Plateau study area, where resources were more seasonal, we found larger range sizes and fluctuating resource selection patterns compared to the Sonoran Desert population. Spatial variation in forage, water, and topography significantly affected range size of burros inhabiting the Colorado Plateau study area. Productive habitats with available water support smaller ranges and a more consistent pattern of resource selection. Our results highlight the positive effect of habitat heterogeneity and the negative effect of habitat productivity on range size of animals. Our findings contribute to an improved understanding of habitat requirements for free-roaming burros that currently live under various climate and habitat conditions globally.

Despite a long tradition in ecology of studying tree species assembly and its potential drivers in tropical forest communities, little information exists with respect to lianas (woody climbers), the second most abundant life form of woody plants in tropical forests. Lianas influence forest diversity and stability and provide critical resources for forest fauna. Using a unique dataset of a 30-ha plot in Thailand, where tree and liana individuals were fully mapped, we investigated the degree to which local species assemblages of trees and lianas of different size classes (i.e., seedlings, established individuals, and large individuals) are related to local environmental conditions. We asked (1) What are the spatial patterns and environmental drivers of local tree and liana species assemblages? (2) How do such patterns and drivers differ among size classes? (3) Which species associate with these assemblages? Local assemblages of established trees showed substantial structuring by environmental variables, whereas we found only weakly structured assemblages of tree seedlings, large trees, and lianas of all size classes. Our results indicated that the biotic and abiotic drivers of local species assemblages differed strongly between tree and liana communities and across size classes. Species assemblages of trees were mainly driven by soil nutrients, leading to patchy assemblages associated with high base saturation (Alfisols) and assemblages associated with lower levels of base saturation and higher aluminum (Ultisols), whereas tree seedling assemblages were only weakly structured by riparian zones. In contrast, species assemblages of established and large lianas were primarily associated with forest canopy structure, separating low-canopy forests from high-canopy forests, whereas soil nutrients were the only factors associated with liana seedling assemblages. The weak environmental structuring of tree seedlings and large trees suggests that other mechanisms, such as stochastic disturbances, competition for space, or animal seed dispersal, may play an important role in structuring tree communities in this seasonal tropical forest. The weak patterns observed in liana communities across all life stages raise questions about the underlying mechanisms of liana community assembly, and further research should focus on liana niches, their dispersal mechanisms, and host tree relations.

Nitrogen isotope analyses of amino acids (δ¹⁵N-AA) are being increasingly used to decipher trophic dynamics. Interpretation of δ¹⁵N-AA in consumers relies on the assumption that consumer physiological status and nutritional status of prey have negligible influences on the trophic discrimination factor (TDF), hence a constant TDF value is used in trophic position (TP) equations. Recent experiments have shown that this is not always the case and there is also a need to validate derived TP estimates in the field. We take advantage of the uniquely long time series of environmental monitoring data and archived (frozen) samples from the species-poor Baltic Sea. We analyzed δ¹⁵N-AA in similar sized individuals of cod and in its prey herring from four decades, 1980–2018; including time periods where dramatic reduction in condition status of cod has occurred. We expected that TDF in trophic AAs would increase during periods of poor cod condition, resulting in inflated TP estimates. We found that calculated TP and empirical estimates of TDF (difference in δ¹⁵N in trophic AAs between cod and herring) for cod increased in recent decades and that this was linked to condition status, herring (prey) lipid content and the hypoxic state of the ecosystem. Statistically adjusting TP for condition and prey lipid content as well as environmental stress (hypoxia) resulted in lower cod TP which better resembled the observed decrease in herring TP in recent decades. TP calculated from stomach analysis data in cod individuals over the same period showed no trend over time and confirmed that adjusted TP estimates mirror the real dietary TP better than unadjusted. By simultaneously measuring condition/nutritional status in both predator and prey it is possible to adjust for them as confounding variables and decipher actual consumer TP, partly overcoming the issues of unknown and variable TDF-values. Our study also highlights the importance of including environmental stressors (here hypoxia) when interpreting TP and reconstructing food webs.