Ecological Applications最新文献

In tropical grazer assemblies with abundant large predators, smaller herbivores have been shown to be limited by predation and food quality, while the larger species are regulated by food abundance. Much less is known about herbivore resource partitioning in temperate grazing ecosystems, where humans typically regulate large animal abundances. The Oostvaardersplassen ecosystem in the Netherlands is a unique multispecies assemblage of cattle, horses, red deer, and geese developed after the initial introduction of a few individuals in 1983. During the first 35 years, this herbivore assemblage without predation or human regulation gradually changed into an increasing dominance of the smaller herbivore species. Carrying capacity was reached around 2008, after which numbers started fluctuating depending on winter conditions. From 2018, management changed and population numbers became regulated for biodiversity and animal welfare reasons; however, population numbers still remained close to carrying capacity for several years. We used eDNA metabarcoding of dung to quantify the diet composition of cattle, horses, red deer, and geese, annually in early winter from 2018 to 2021 and calculated their niche overlap. We found strong interspecific diet overlap. Horse and cattle diets remained mostly stable with fluctuating densities of the different species, while only red deer diet showed density dependence. Interspecific niche overlap decreased with increasing red deer population size, the most abundant species. When calculated as total energy expenditure, we found that niche overlap was more linked to red deer abundance than to total herbivore energy intake. We suggest that red deer changed their diet mainly in response to their own population size, reducing their niche overlap in relation to their population increase. In this case, resource competition reduced resource availability and forced herbivores to consume different plant taxa. We conclude that in this predator-free temperate ecosystem, inter- and intraspecific resource competition are key factors structuring this assemblage of different size herbivores. We find a general competitive advantage of the more diet-flexible red deer over horses and cattle, but with also clear signs of resource partitioning.

While terrestrial biomes are routinely defined by their dominant vegetation structure and coupled primary productivity regimes, connecting primary productivity regimes with the properties of aquatic vegetation in rivers is uncommon. Gross primary production (GPP) and ecosystem respiration (ER) indicate riverine ecosystem processes that are useful for monitoring river response to human alterations, global change, and restoration, but how aquatic vegetation structure and biomass influence GPP and ER is poorly known. We related patterns in the time series of daily GPP and ER to submerged aquatic vegetation biomass at 11 reaches on the Klamath River, California, downstream of four dams prior to their removal in 2024. Rooted macrophytes dominated vegetation assemblages at upriver sites and transitioned to filamentous algae downriver. Fluxes of GPP and ER were high compared to those of other rivers, with the magnitude and timing of mean ER, mean GPP, peak GPP, GPP variability, and green-up varying among sites. While total autotrophic biomass correlated strongly with mean summer GPP ($��r�=�$ 0.80), evidence of macrophytes driving mean summer GPP was weaker ( 0.60). Relationships between mean summer ER and macrophyte biomass and total autotrophic biomass ($��r�=�$ 0.93 and 0.87, respectively) were stronger than relationships between biomass and GPP. This strong relationship between ER and biomass was due to ER increasing late in summer, possibly because respiration of autotrophic biomass produced earlier in the season, or from vegetation structure creating patches of increased respiration of imported or stored carbon. Assessments of relationships between submerged aquatic vegetation and river metabolism may help inform predictions about changing ecosystem structure and function that influence water quality, including ecosystem response to the removal of four large hydroelectric dams on the Klamath River.

Biological control, the practice of using one species (biocontrol agent) to control the population of another (a host or prey species, hereafter target), can be a successful method to reduce pest species in agricultural and natural systems worldwide. Successful biocontrol agents often share a deep evolutionary history with their targets that results in high target specificity and synchronized phenology. However, with rapidly changing climatic conditions, users of biocontrol agents have questioned how climate change will affect both well-established and more recent biological control relationships. Using a meta-analysis of data collected from a systematic literature review, we evaluated the evidence for the impact of changing temperatures on the efficacy of biocontrol agents and corresponding responses in their targets. Overall, most studies of climate change impacts on biocontrol agents take place in the laboratory and focus on arthropod agents that are parasitoids. Results from our meta-analysis reveal that changes in temperature are projected to impact biocontrol agents and their targets similarly, with no overall significant changes to biocontrol agent or target performance. However, our results also show that temperature responses vary widely across study systems, as illustrated by case studies showing both positive, neutral, and negative effects of temperature on biocontrol agent efficacy, as well as variation in responses across the three core biological control measures of success: survival, reproduction, and efficacy. Our work highlights important knowledge gaps including how climate change will affect both biocontrol agents and their targets simultaneously. Additionally, we find that most current studies of climate impacts examined temperature relationships, predominantly of agricultural biocontrol agents. Increasing the breadth of studies is crucial for understanding the potential for climate change to affect the success of current and future biological control programs.

Habitat suitability models (HSMs) are popular statistical tools used to inform decision-making for conservation planning, using species location data to characterize species–environment relationships and identify important habitats. Suitable habitats may vary according to behavior-specific resource requirements (e.g., foraging, resting), yet HSMs generally ignore behavior because obtaining spatially explicit behavioral data from wild animals is challenging. As such, suitable habitats may be incorrectly identified, and processes determining habitat selection may be misinterpreted. Despite offering unprecedented behavioral insight, contemporary multi-sensor biologgers remain underutilized in this context. We incorporated behavior into HSMs using biologging data collected from adult flatback turtles Natator depressus (n = 42) at a macrotidal study site in Western Australia and subsequently identified and characterized suitable habitat for key in-water behaviors. Foraging and resting locations derived from high-resolution motion sensor data (e.g., accelerometer, magnetometer) coupled with animal-borne video and GPS data were combined with 10 environmental features (i.e., bathymetry, aspect, slope, terrain ruggedness, distance from the coast and currents from a bespoke hydrodynamic model of the study site). A series of random forest HSMs were implemented for each behavior, accounting for temporal variation in habitat use. Bathymetry, distance from the coast, and currents best determined both foraging and resting suitability, with observed differences in habitat selection between behaviors. Overall, spatiotemporal patterns of most suitable foraging and resting habitat were similar, with shallow (10–15 m deep) nearshore (5–10 km from the coast) waters most suitable for both behaviors; however, habitats nearest to the coast (<5 km) were more suitable for foraging than resting. Overall, for foraging and resting, as water level increased turtles selected increasingly nearshore habitats where current speed was low and more variable direction. Overlap between most suitable habitats and current spatial zoning at the study site varied both seasonally and with water level, likely reflecting strong tidal influence on distribution and hence highlighting considerable opportunity for dynamic management. Our approach facilitates mechanistic insight into habitat selection and is generalizable across behaviors, taxa, and study systems, advancing the application of biologging tools to enhance the utility of HSMs and providing crucial context for decision-makers in threatened species management.

High biodiversity and agricultural productivity are commonly regarded as mutually exclusive. However, functionally diverse communities may provide valuable services to agroecosystems and therefore offer the possibility of win–win strategies. We developed a dynamic mechanistic community model of the bird–arthropod food web associated with African cocoa agroforestry, structurally informed by metabarcoding data on bird diets, and fitted to trapping data on species abundances. Our novel framework models rates of change and uses space-for-time substitution, thus providing insights into community dynamics without the need for long time-series data. We used our fitted model to predict equilibrium community composition under varying intensities of shade management and pesticide use. Our results indicate that low-intensity farming favors forest bird species, with at least two times the biomass of this bird group compared to any other at high shade cover. Low-intensity farms also favored potential pollinator abundance, while biomass of the main pest species of cocoa, brown capsid, was 25% lower at high shade than at low shade. Our model quantified the net effect of each taxon on the other taxa in the food web: most bird to arthropod interactions were negative, indicating important pest control services provided by birds. Furthermore, our simulations of pesticide application revealed that the long-term effect of pesticide use on biomass of taxa varied according to shade cover. Importantly, pesticide application resulted in the decline of non-pest taxa through trophic cascades: forest birds were the taxa that declined the fastest, and this trend was exacerbated in low shade farms. To achieve a decline of less than 50% in non-pest taxa, pesticide application could only reach 10% in the sunniest farms and 20% in shady farms, which results in a maximal reduction of 19% in pest biomass. By looking at the efficacy of agricultural management through the lens of community interactions, our holistic, quantitative approach demonstrates that low-intensity agriculture may provide a win–win for biodiversity and ecosystem services.

Sound methods to determine species occurrence and abundance are crucial for successful wildlife management and conservation. When species communities cannot be readily detected using camera traps or acoustic monitoring, ground survey methods such as spotlighting on foot are commonly used. While able to provide precise detection and density estimates, these methods can be laborious and time consuming and are restricted to surveying small areas. Advances in drone technology now allow for the detection of heat signatures of endothermal wildlife using thermal cameras from the sky, which we contrast to traditional ground surveys. We found that drone and ground surveys achieve similar detection probabilities for nocturnal arboreal mammals of southeastern Australia. Drones achieved high detection rates for targeted arboreal wildlife occurrence and consistently recorded more species and individuals than ground-based surveys via spotlighting. Ground surveys often missed specialist species like the endangered southern greater glider (Petauroides volans) when populations had low densities. Drone-derived density estimates for surveyed areas of 100–200 ha were significantly lower than those extrapolated from 10-ha ground survey results. Thermal drone surveys present a promising tool for measuring and monitoring nocturnal arboreal wildlife populations due to their ability to cover larger areas with comparable detection rates to ground surveys. Drone surveys provide comprehensive information on species assemblage, density, and distribution across management compartment-scale survey areas, offering valuable insights into species occurrence and population status. Drones were particularly effective in areas with dense vegetation or that were otherwise inaccessible for ground-based surveys, enhancing the ability to estimate populations, quantify recovery following large-scale disturbances, and to discover previously undocumented populations. Drone-based wildlife survey methods have the potential to reduce uncertainty in compartment-scale population estimates for improved wildlife monitoring and conservation.

Marine protected areas (MPAs) often lack adequate data on the status of marine assemblages to support evidence-based management. Stereo baited remote underwater video (BRUV) systems offer a powerful, low-cost tool for collecting ecological data, yet they remain underutilized in the North East Atlantic, especially compared to more invasive methods such as fisheries surveys. Here, we demonstrate how a spatially comprehensive stereo-BRUV survey can generate benchmark data to support MPA management at an ecosystem scale, using an ecologically distinct oceanic archipelago as a case study. The archipelago's habitats were found to support high abundances of regionally targeted commercial species, including benthic catsharks (Scyliorhinidae) and European spiny lobster (Palinurus elephas), with ~12,000 individuals recorded representing 64 species and 44 families. Deeper, topographically complex reefs were found to support higher levels of richness and biomass, with sediment-specific increases in depth also driving demersal abundance. Stereo technology was additionally able to provide body size data for 43 species, with remoteness and shelter from exposure found to be common drivers of increased body size for indicator taxa. Survey results represent a contemporary benchmark for measuring changes in local MPA management, fisheries practices, and climate change impacts. The results also illustrate how spatially robust sampling methods and stereo-BRUV systems can facilitate more holistic, fisheries-independent data collection in UK and European waters.

Computer vision models show great promise for assisting researchers with rapid processing of ecological data from many sources, including images from camera traps. Access to user-friendly workflows offering collaborative features, remote and local access, and data control will enable greater adoption of computer vision models and accelerate the time between data collection and analysis for many conservation and research programs. We present Njobvu-AI, a no-code tool for multiuser image labeling, model training, image classification, and review. Using this tool, we demonstrate training and deploying a YOLO multiclass detector model using a modest dataset of 33,664 camera trap images of 37 animal species from Nkhotakota Wildlife Reserve, Malawi. We then applied our model to an independent dataset and evaluated its performance in terms of filtering empty images, species classification, species richness, and per-image animal counts. Our model filtered over 3 million empty images and had similar sensitivity but lower specificity than the MegaDetector model at differentiating empty images from those with animals. Classification performance was high for species with >1000 training images (average precision, recall, and F1 >0.70) and moderate overall (macro-averaged precision = 0.64, recall = 0.76, F1 = 0.63). Site-level species richness using predicted detections with and without manual review were highly concordant, especially when a score threshold of 0.95 was applied ($��R^2�$ = 0.91). Counts of animals per image were predicted accurately for many species but underestimated by up to 22% for those in large groups. This workflow represents an option for researchers to implement custom computer vision models for even modest-sized ecological datasets in an all-in-one, collaborative, no-code platform.

Biodiversity can confer temporal stability to ecosystem processes through asynchrony in species' abundances and may promote asynchrony and stability of commercial fishing harvests derived from exploited species. However, the linkages between asynchrony in the population dynamics of commercially harvested species and asynchrony of associated harvests have been difficult to resolve due to ecological, social, and economic dynamics that mediate resource extraction. Here, we explored coupled human-ecological relationships and emergent asynchrony using commercial fishing harvest data and fisheries-independent trawl surveys in two regions (Maryland and Virginia) of Chesapeake Bay, USA, from 2002 to 2018. For each region, we sought to identify how seasonal (within-year) asynchrony among harvested fish species contributed to (1) seasonal asynchrony in the harvests of these species and (2) within-year stability and economic value of harvests. We found that, in Maryland, seasonal closure of striped bass (Morone saxatilis) fishing resulted in asynchrony by forcing switching to alternative stocks. In Virginia, seasonal migration of harvested species to and from the Chesapeake Bay promoted harvest compensation and therefore harvest asynchrony. However, this effect was negated by the concurrent effects of an increase in the evenness of species dynamics on harvest compensation, reflecting changes in fishing patterns, primarily following declines in the biomass of Atlantic croaker (Micropogonias undulatus). Our findings show that both social (direct management actions and behavioral responses) and emergent properties of ecological systems can influence asynchrony in dynamics of exploited populations and commercial harvests, with implications for their continued management and sustainability.

Vessel disturbance is one of many anthropogenic threats that are negatively impacting coastal cetacean populations worldwide. Noise pollution from vessels can cause varying levels of disturbance in cetaceans, depending on several factors such as vessel type and speed. Pacific harbour porpoises (Phocoena phocoena vomerina) are distributed throughout coastal waters of the North Pacific Ocean, with large aggregations observed near the entrance to the Port of Prince Rupert in British Columbia, Canada. This area serves as an important year-round foraging ground for harbour porpoises. However, it is also one of the fastest growing container ports in North America, with planned increases in activity. Harbour porpoises are highly sensitive to vessel-related acoustic disturbances, but the effects of vessel activity on their foraging rates remain unclear. In this study, we used a combination of land-based surveys, passive acoustic monitoring (PAM) devices (C-PODs and F-PODs), and automatic identification system (AIS) data to investigate the relationship between vessel activity and harbour porpoise echolocation activity—both foraging and non-foraging—in the heavily trafficked Chatham Sound, adjacent to the Port. Our results show that an increase in the total number of vessels negatively affected both foraging and non-foraging echolocation activity, with less echolocation observed in the presence of more ferries and tugs. Similarly, vessels traveling at higher speeds (>6 m/s kn) had a negative effect on echolocation activity. Tugboats and passenger vessels, in particular, had a wider range of effects on all harbour porpoise echolocation activity. Our findings indicate that implementing a vessel slowdown (~5 m/s) along the approach to the Port of Prince Rupert would reduce disturbances to harbour porpoises and likely benefit other coexisting species that rely on quiet oceans for communication and foraging.