Ecosphere最新文献

Climate change is rapidly transforming the coastal margins of Greenland. At the same time, there is increasing recognition that marine-terminating glaciers provide unique and critical habitats to ice-associated top predators. We investigated the connection between a top predator occupying glacial fjord systems in Northwest Greenland and the properties of Atlantic-origin water and marine-terminating glaciers through a multiyear interdisciplinary project. Using passive acoustic monitoring, we quantified the summer presence and autumn departure of narwhals (Monodon monoceros) at glacier fronts in Melville Bay and modeled what glacier fjord physical attributes are associated with narwhal occurrence. We found that narwhals are present at glacier fronts after Greenland Ice Sheet peak summer runoff and they remain there during the period when the water column is becoming colder and fresher. Narwhals occupied glacier fronts when ocean temperatures ranged from −0.6 to 0.8°C and salinities between 33.2 and 34.0 psu at around 200 m depth and they departed on their southbound migration between October and November. Narwhals' departure was approximately 4 weeks later in 2019 than in 2018, after an extreme 2019 summer heatwave event that also delayed sea ice formation by 2 months. Our study provides further support for the niche conservative narwhal's preference for cold ocean temperatures. These results may inform projections about how future changes will impact narwhal subpopulations, especially those occupying Greenland glacial fjords.

Ecological zonation in coastal forests is driven by sea level rise and storm-surge events. Mature trees that can survive moderately saline conditions show signs of stress when soil salinity increases above its tolerance levels. As leaf burn, foliar damage, and defoliation reduce tree canopy cover, light gaps form within the crown. At the forest-marsh edge, canopy cover loss is most severe; trunks of dead trees without canopies form “ghost forests.” Canopy thinning and light from the edge alter conditions for understory vegetation, promoting the growth of shrubs and facilitating establishment and spread of invasive species that were previously limited by light competition. In this research, we present an analysis of illuminance and temperature in a coastal forest transitioning to a salt marsh. Light sensors above the ground surface were used to measure light attenuation of trees and understory vegetation and to observe the effect of reduced canopies at the forest-marsh edge. Farther from the marsh, where salinity is lower and trees are healthy, dense canopies attenuate light. We estimate that during the growing season, tree canopies intercept 50% of illuminance on average. Closer to the marsh, canopy thinning, and tree death allow greater light penetration from above, as well as from the adjacent marsh. These illuminance values are further increased by light penetration from the forest-marsh edge (edge effect). Here, higher illuminance may permit Phragmites australis expansion. At intermediate locations, trees intercept between 32% and 49% of light and the understory shrub Morella cerifera intercepts a further 45% of penetrating light based on comparisons of illuminance above and below shrub canopies. Light penetration from the edge can also be felt. The presence of M. cerifera reduces the air temperature close to the soil surface, creating a cooler summer microclimate. The tree health state is reflected in the canopy size. The canopy patterns and the edge effect are responsible for light availability distribution along forest-marsh gradients, consequently affecting the understory vegetation biomass. We conclude that during forest retreat driven by sea level rise, tree dieback increases light availability favoring the temporary encroachment of Ph. australis and M. cerifera in the understory.

Non-native annual grasses can dramatically alter fire frequency and reduce forage quality and biodiversity in the ecosystems they invade. Effective management techniques are needed to reduce these undesirable invasive species and maintain ecosystem services. Well-timed management strategies, such as grazing, that are applied when invasive grasses are active prior to native plants can control invasive species spread and reduce their impact; however, anticipating the timing of key phenological stages that are susceptible to management over vast landscapes is difficult, as the phenology of these species can vary greatly over time and space. To address this challenge, we created range-wide phenology forecasts for two problematic invasive annual grasses: cheatgrass (Bromus tectorum), and red brome (Bromus rubens). We tested a suite of 18 mechanistic phenology models using observations from monitoring experiments, volunteer science, herbarium records, timelapse camera imagery, and downscaled gridded climate data to identify the models that best predicted the dates of flowering and senescence of the two invasive grass species. We found that the timing of flowering and senescence of cheatgrass and red brome were best predicted by photothermal time models that had been adjusted for topography using gridded continuous heat-insolation load index values. Phenology forecasts based on these models can help managers make decisions about when to schedule management actions such as grazing to reduce undesirable invasive grasses and promote forage production, quality, and biodiversity in grasslands; to predict the timing of greatest fire risk after annual grasses dry out; and to select remote sensing imagery to accurately map invasive grasses across topographic and latitudinal gradients. These phenology models also have the potential to be operationalized for within-season or within-year decision support.

Bolstering the supply of animal-mediated ecosystem services is an emerging priority in human-altered landscapes. Such services are driven not only by environmental factors that shape communities of species that provide the service but also by the ecological context that affects the behavior of these species. In this study, we used a field experiment to investigate an ecosystem service that depends on resource use behavior—the removal of littered food waste by birds and squirrels in urban green spaces. We first explore how landscape-scale urbanization affects the composition of the litter-removing species community. We then examine two facets of waste removal provisioning—the amount of food removed and the speed of removal—and how they vary across ecological contexts represented by green space type (picnic areas, urban parks, and forest preserves), bird and squirrel abundance, number of people, amount of existing litter, and weather conditions. We found that although landscape-scale urbanization affected the composition of species within green spaces, service provisioning was context-dependent. Littered food removal services were provided at higher rates in park and picnic sites than in forest preserves and the abundance of eastern gray squirrels (Sciurus carolinensis) was a main driver of littered food removal services. Where squirrels were abundant, more food was removed, and food removal began and was completed more quickly. When squirrel abundance is accounted for, removal from picnic areas is higher than park sites, indicating context dependence in this service is likely driven by squirrel behavioral responses to ambient food waste levels in these habitats. This study highlights the role of common urban species in providing a valuable ecosystem service and the importance of ecological context in its supply. Efforts to account for animal-mediated ecosystem services in human-altered landscapes should address the potential for services to be driven by a single species and context-dependent factors that influence behavior.

Throughout the Arctic, ice-affiliated marine mammals constitute local subsistence resources but detrimental effects of declines in their sea ice habitats create a need for harvest sustainability assessments in light of climate change. At the same time, empirical data required for thorough population analysis of these species are often sparse at best, as illustrated by the focal species in this study, ringed seals in Svalbard: the last population survey took place two decades ago (2002–2003), demographic data are limited to age, sex, and reproductive status of a small subset of shot individuals, and harvest reporting is patchy and incomplete. Data sparsity is one of the main reasons why potential biological removal (PBR) became a commonly used tool for assessing sustainability of marine mammal harvests. Herein, we calculated PBR for Svalbard ringed seals using both recommended default parameters and population-specific parameters obtained from an integrated population model (IPM). PBR estimates were highly uncertain, suggesting the number of sustainably harvestable individuals could lie anywhere between 0 and 91, with a substantial chance of any harvest being unsustainable under current environmental conditions and trends. Subsequent population viability analyses (PVAs) further confirmed that the current harvest was likely unsustainable, even in a scenario in which sea ice conditions would not deteriorate (and therefore lower pup survival) further. However, uncertainty in population projections was high, and forecasts thus not ideal for formulating management advice. Better forecasts will require more frequent population surveys and obtaining more knowledge regarding the links between vital rates and environmental conditions, both of which may be facilitated by the adoption of novel technology (e.g., drone monitoring, genetic studies). The modeling framework created in this study can be readily updated with new data as they become available, and can serve as a tool for adaptive management of this and other marine mammal populations.

Urbanization is rapidly transforming coastal landscapes around the world, altering the structure and function of marine, intertidal, and terrestrial ecosystems. In tandem, coastal areas are hotspots for human recreation, leading to shifts in wildlife behavior and activity patterns. Together, urban development and recreational use of wildlife habitats can shape wildlife behavior, abundance, and ecosystem dynamics at different spatial and temporal scales. In this study, we explore the impact of urbanization and human and domestic dog activity on the structure of vertebrate scavenging assemblages and the ecosystem functions they provide in sandy beach ecosystems across 40 km of the central California coast, USA. We surveyed vertebrate scavenging assemblages using baited camera traps on 17 beaches spanning a gradient of coastal urbanization. We found that urbanization extent within small spatial scales (i.e., 1 or 3 km radii of each site) and the rate of beach visitation by domestic dogs or humans were the best additive predictors of assemblage structure. We identified pronounced urbanization-associated shifts in the composition of vertebrate scavenger guilds but found that the rate of carrion processing was more strongly influenced by domestic dog habitat use and diel period. Scavenging activity was substantially lower on beaches with more domestic dogs, suggesting that dogs interfere with critical scavenging ecosystem functions on sandy beaches. Our results underscore the pervasive and nuanced effects of urbanization and recreation on the dynamics of land–sea connectivity and suggest a need for comprehensive consideration of cross-ecosystem linkages in ongoing shoreline conservation and development efforts.

Environmental parameters along elevational gradients affect the number of butterflies and the variety of species. However, which variables play significant roles and how they operate can be difficult to untangle. Here, we examine the relationships between observed butterfly richness (overall and subgroups) at different elevation gradients and remotely sensed environmental variables (vegetation productivity, surface temperature, landscape heterogeneity, and moisture stress) using generalized linear models. We surveyed butterflies with a fixed-point count method in 19 elevation bands within 1600–5200 m above sea level in Manang district, trans-Himalayan region, north-central Nepal. The number of butterflies in each elevation band was studied and estimated, then interpolated across the lowest and highest elevation to estimate butterfly species richness. Then, the selection of models was performed on butterfly richness and elevations to test the best model support based on the lowest value of the Akaike information criterion and a multimodel averaging for other environmental variables. Altogether, 94 butterfly species, representing 20 subfamilies and six families, were recorded throughout the study periods. We obtained cubic model support for overall species richness, Papilionidae, and Hesperiidae, quadratic to Nymphalidae and Pieridae, and the linear model to Lycaenidae. In our study, vegetation productivity was found to have a significant positive impact on butterfly communities. Our study further suggests species richness of Papilionidae and Hesperiidae has a strong positive correlation with surface temperature and landscape heterogeneity and negative associations with moisture stress but other subgroups of butterfly communities including overall species richness showed insignificant relationships with these variables. This study provides significant information related to the responses of montane butterflies to environmental variables along elevational gradients from the Himalayas Nepal. However, further detailed studies on the functional behaviors of butterflies potentially offer more insights into their distribution patterns and ecological relationship in the montane environment.

Estimating the size of animal populations plays an important role in evidence-based conservation and management. Some methods for estimating population size rely on animals being individually identifiable. Traditionally, this has been done by marking physically captured animals, but increasingly, animals with distinctive natural markings are surveyed noninvasively using cameras. Animal reidentification from photographs is usually done manually, which is expensive, laborious, and requires considerable skill. An alternative is to develop computer vision methods that can support or replace the manual identification task. We developed an automated approach using deep learning to identify whether a pair of photographs is of the same individual or not. The core of the approach is a similarity learning network that uses paired convolutional neural networks with a triplet loss function to summarize image pairs and decide whether they are from the same individual. Prior to the main matching step, two additional convolutional neural networks perform image segmentation, cropping the animal object within the image, and orientation prediction, deciding which side of the animal was photographed. We applied the approach to four species, with images of the same individual often spanning several years: systematic surveys of bottlenose dolphins (Tursiops truncatus, 2008–2019) and harbor seals (Phoca vitulina, 2015–2019), a citizen science dataset of western leopard toads (Sclerophrys pantherina, unknown dates), and a publicly available repository of humpback whale images (Megaptera novaeangliae, unknown dates). For these species, our best-performing models were able to identify whether a pair of images were from the same individual or different individuals in 95.8%, 94.6%, 88.2%, and 83.8% of the cases, respectively. We found that triplet loss functions outperformed binary cross-entropy loss functions and that data augmentation and additional manual curation of training data provided small but consistent improvements in performance. These results demonstrate the potential of deep learning to replace or, more likely, support and facilitate manual individual identification efforts.

Climate change is motivating a reassessment of how seeds are selected for reforestation, as rapid environmental change can lead to local maladaptation in trees. Genetic association studies and past seed source climate both have the potential to help identify appropriate planting stock, but these techniques have not been compared and tested as part of an operational planting program. In this study, we combined an analysis of single nucleotide polymorphisms (SNPs) associated with environmental gradients in sugar pine (Pinus lambertiana) with an analysis of post-fire seedling survival and growth in a restoration experiment. Our genotype–environment association (GEA) tests of 92 individuals from varying climates within CA revealed 829 SNPs (out of 300,604) with significant association with climate gradients, especially April snowpack. Of these, 323 either had annotations that suggested potential functional importance or were identified by two different methods. We then built Bayesian models of survival and growth for all seedlings in a separate post-fire planting experiment, to test the relative predictive ability of source elevation (a common proxy for source climate) versus the proportion of seedling alleles expected to be locally advantageous based on GEA. Across three sites within the King Fire scar in Eldorado National Forest in 2017, 2018, and 2019, 1774 seedlings were planted. Of these, 206 had enough green needles in 2020 to allow sample collection, and 161 were successfully genotyped. We found that source elevation was generally better at predicting seedling performance than genotype indices, perhaps because of the limited scope of the association analysis. Seed sources from 500 to 1800 ft (152.4–548.6 m) lower in elevation, and one seed zone further south generally performed as well or better than local seed sources. This result, and those of similar previous studies, suggest that “climate matching” using past climate information for existing seed sourcing units is a reasonable starting point for finding seedlings suited to already-altered planting site climate conditions. However, further tests with more extensive genomic and performance data may improve the utility of genotype information for seed selection.

Many methods have been proposed to model the spatial distribution of a species. While some methods have been specifically designed for this purpose, others are well-known statistical tools that can be used in many scientific fields. In this paper, I propose a new ecological niche model, called the Environmental String Model (ESM), that is based on the concept of environmental string, which is defined as being a combination of environmental variables, with as many nodes as environmental variables. There are two types of environmental strings: (1) the abundance-known string and (2) the abundance-unknown string (or target string) for which an estimation of abundance is searched. The novelty of the model is that it assesses the abundance associated with a target string from nearby abundance-known strings, which preserve the local multidimensional relationships with the target string. The model does not provide an abundance estimate in the absence of data from a similar environment and it can therefore deal with truncated spatial distributions or niches. It is tested in the North Atlantic Ocean on two key copepod species, Calanus finmarchicus and Calanus helgolandicus, which have been monitored by the Continuous Plankton Recorder (CPR) survey for decades. I investigate the influence of variables on model performance. I show that the model reconstructs the mean spatial distribution and seasonal fluctuations in both Calanus well. When compared with generalized linear models (GLMs), generalized additive models (GAMs) and generalized regression neural network (GRNN), the ESM gives the best performance. I propose a number of indicators to evaluate the robustness of estimated abundance in space and time and show how the model may be extended to presence/absence or presence-only data. I think that the ESM could be used to fill gaps in any sampling program such as the CPR survey and many satellite databases (e.g., ocean color and photosynthetically active radiation).