Ecological Applications最新文献

Major estuaries globally are experiencing fast-paced changes in hydrology and ecosystem dynamics. However, connecting alteration of river flow regimes to estuarine fish population dynamics remains a challenge, partly due to the untested assumption that flow regimes, fish dynamics, and the resulting flow–ecology relationships are stationary (i.e., have no systematic changes in mean or variance over time). Here, we studied the endangered population segment of Longfin Smelt (Spirinchus thaleichthys) in the San Francisco Estuary, which depends on seasonal river flows to reproduce. We used extensive biomonitoring data (1980–2020) and two time-series modeling techniques, namely multivariate autoregressive state-space (MARSS) models and dynamic linear models (DLMs), to understand how population dynamics respond to interannual flow variation, and whether flow–ecology relationships have changed over time. MARSS outputs showed that population trajectories are best explained by a combination of lateral and vertical dimensions of habitat structure, that is, whether individuals were collected in channels versus shoals, and in pelagic versus benthic environments. In turn, DLMs revealed time-varying, but often positive effects of flow on young-of-the-year abundance in shallow channel and shoal habitats, but no consistent relationships for older individuals (age-1+), likely due to other drivers influencing survival from age-0 and age-1+. Finally, we found that the two modeling approaches showed agreement only in about 30% of the cases. Divergence in the sign and/or magnitude of flow effects suggests that time-averaged approaches may sometimes oversimplify non-stationary relationships between the environment and fish population dynamics. From a conservation standpoint, the gradually weakening but positive flow–ecology relationship (as opposed to a step change in the relationship) suggests that it may still be possible to reverse the steep population declines of Longfin Smelt through a combination of flow and habitat restoration actions. While we focused on a particular endangered population, our quantitative approach is transferable to other taxa and geographies, and could help inform management of flow-dependent resources in systems strongly affected by non-stationarity. We contend that time-varying flow–ecology relationships are needed to better capture ecological realism, and could help design more effective conservation strategies in fast-changing environments.

Climate and land-use change are dramatically altering the frequency, intensity, and extent of ecological disturbances, which threaten the persistence of at-risk species. To curb the pace and scale of disturbances, balance management and conservation priorities, and alleviate associated population declines, managers require high-quality information on species' responses to disturbance and their population trends across broad spatial scales that challenge the capacity of traditional, local-scale monitoring programs. Passive acoustic monitoring is a scalable approach to obtain occurrence data, but the extent to which it can be used to model occupancy dynamics and their environmental drivers remains uncertain. Here, we demonstrate how passive acoustic surveys can be analyzed within a Bayesian dynamic occupancy modeling framework to robustly estimate occupancy dynamics and responses to disturbance in the California spotted owl (Strix occidentalis occidentalis), which is threatened by increasingly large, severe “megafires.” From 2021 to 2024, we collected ~2 million hours of audio from autonomous recording units deployed across seven national forests in the Sierra Nevada, California, USA. Spotted owls were less likely to initially occupy and colonize sites that were severely burned, and more likely to go locally extinct following high-severity fire. Further, we observed declining postfire occupancy trajectories, particularly when sites burned ≥50% high severity. Occupancy trends varied by national forest, but declined by 2% across the entire region. Our findings—which closely align with those from intensive, traditional demographic studies—demonstrate that large-scale passive acoustic monitoring paired with dynamic occupancy models can effectively detect species' responses to disturbance and estimate population trends, offering valuable insights for management across multiple spatial scales. Finally, we provide specific recommendations to help other passive acoustic monitoring programs successfully detect ecological responses to disturbance and track population changes.

Bark beetles of the genus Dendroctonus are some of the most important disturbance agents in North American forests, having colonized conifers for millions of years. The selection pressure posed by tree-killing bark beetles pushed trees to develop an arsenal of defensive strategies to which beetles have adapted in their turn. Recent surges in bark beetle-related tree mortality have highlighted the potential of novel climatic and landscape conditions to push tightly calibrated relationships beyond historical norms. One such example is an unprecedented outbreak of the native eastern larch beetle (ELB), Dendroctonus simplex LeConte (Coleoptera: Curculionidae; Scolytinae), that has killed eastern larch (tamarack), Larix laricina (Du Roi) K. Koch, trees across more than 460,000 ha of forest in the Great Lakes Region since 2001. The ability of a bark beetle to attack healthy trees is dependent on sufficient local beetle numbers to overwhelm host defenses and a behavioral switch to target those trees that are avoided at lower population levels. ELB was not previously considered an aggressive tree colonizer, but extended growing seasons have contributed to recent eruptions in local populations of the species. We combined a dendrochronological analysis of tree cores with observational data collected from 2011 to 2013 in Beltrami Island State Forest, Minnesota, to understand tree defensive capacity and beetle outbreak dynamics in this understudied system. We found that preformed defenses visible in tamarack xylem were limited and did not determine host preference of ELB during our study. Beetles colonized the largest trees with the thickest phloem regardless of defensive capacity. Preformed resin defenses measured in tree phloem were correlated with reduced beetle reproductive success but were unrelated to resin metrics from tree xylem. With this work, the interaction between ELB and tamarack serves as a model to explore how climate change may alter species associations within native forest systems and the management challenges associated with underestimating historically benign pests.

The spatial configuration of alpine meadow micro-patches (<5 m²) on the Qinghai-Tibet Plateau (QTP) serves as a critical indicator for early warning of ecological degradation. While unmanned aerial vehicle (UAV) remote sensing enables micro-patch detection, two methodological challenges persist: unclear response thresholds of landscape indices to spatial extent variations and diminished ecological interpretability due to redundancy in multidimensional indices. This study develops a novel scale-adaptive framework integrating spatial extent effect analysis with principal component analysis-driven (PCA-driven) dimensionality reduction. Based on 34 landscape indices derived from UAV imagery (0.02-m resolution), we systematically quantified sensitivity thresholds through spatial autocorrelation–heterogeneity trade-off analysis across 2–50-m spatial extents. The results showed that (1) Six critical indices, including number of patches (NP) and mean patch size (AREA_MN), exhibited significant sensitivity to spatial extent variations. The spatial extent effect curves identified 10–21-m as the optimal domain, with 17-m spatial extent optimally balancing spatial autocorrelation and heterogeneity. (2) PCA reduced dimensionality to three factors (area-based aggregation, spatial shape, and edge-separation features), explaining 84% cumulative variance. Four indices—AREA_MN, mean patch euclidean nearest neighbor distance (ENN_MN), perimeter-area fractal dimension (PAFRAC), and mean patch fractal dimension (FRAC_MN)—were identified as key characterization indices, establishing an early-warning diagnostic system for degradation. This framework provides a replicable protocol for micro-patch dynamics monitoring in fragile ecosystems, supporting targeted restoration policies on the QTP and analogous regions.

Drylands make up approximately 40% of terrestrial ecosystems and hold up to 20% of the global soil organic carbon pool. Most semiarid drylands are, to some extent, grazed by livestock. However, the impact of livestock grazing on carbon cycle dynamics over large spatial and temporal scales remains uncertain, especially as the effects of climate change become more pronounced. Thus far, there has been little work, which has explored how site-specific land management may interact with localized shifts in climate to affect biogeochemical processes in dryland ecosystems globally, particularly in the tropics. We used DAYCENT, an ecosystem simulation model, to explore how grazing intensity and projected climate change may impact biogeochemical dynamics in dryland sites in North America, South America, Asia, and Africa. Our simulation results showed a site-specific biogeochemical response to livestock grazing and climate change, even across ecologically similar dryland systems. In sites that had smaller projected shifts in climate (i.e., the North and South American sites), heavy grazing decreased soil carbon inputs, outputs, and storage. In the other two sites, particularly in the African site, shifts in climate had the largest impact on simulated biogeochemical processes, with a projected 20% decrease in the soil organic carbon pool in the African site by the end of the century. Our study highlights the importance of considering how localized shifts in climate may affect dryland ecosystem function as this may overwhelm land management effects over longer time scales. Our work also suggests that more research is needed to better understand how small-scale, site-specific sensitivity to climate change and land use may influence dryland carbon cycle dynamics at the global scale, particularly in tropical regions.

Understanding patterns of habitat use across trophic levels and the physical drivers of multispecies aggregations is essential to inform ecosystem-based management. To achieve this, we quantified the spatial distribution and co-occurrence of hotspots (defined using the Getis-Ord statistic) for euphausiids and nine of their commercially important fish and whale predators on the west coast of Canada during summer. We first developed fine-scale spatiotemporal distribution models of euphausiids and Pacific hake using high-resolution acoustic data from coast-wide surveys conducted between 2007 and 2018. We found that the spatiotemporal distribution of hotspots of euphausiids and hake was variable between years with low direct overlap (apart from 2017). The summer of 2015, during the 2014–2016 marine heatwave event, was a particularly anomalous year, as euphausiids and hake showed spatial mismatch in their biomass hotspot distributions. For the other eight predator species, predictions from published species distribution models were used to identify spatial hotspots as an average across years. Co-occurrence patterns were associated with the depth gradient across the shelf and slope and along the canyon and sea valley systems that characterize the Pacific coast of Canada. One assemblage was associated with the deeper parts (200–1000 m+) of the continental slope (euphausiids, hake, redbanded rockfish, sablefish, Pacific ocean perch, and humpback and fin whales) and a different assemblage (redstripe and yellowtail rockfish, and dogfish) was associated with the shallower shelf regions. Important ecological areas with co-occurring multispecies hotspots occurred along the west coast of Vancouver Island, the sea valleys of Queen Charlotte Sound, and the northwest coast of Haida Gwaii. Our results identify areas where multiple species aggregate, which can inform better management and hopefully protection of these regions that support complex food webs, commercial species, and large predators, and are therefore essential for overall ecosystem health.

Environmental DNA (eDNA) metabarcoding is increasingly applied to a variety of questions and challenges across basic and applied ecology. Although streams and rivers (i.e., lotic ecosystems) can serve as conveyor belts of both aquatic and terrestrial eDNA from upstream or riparian areas, precipitation can dilute eDNA due to increasing discharge and/or mobilize eDNA into rivers from adjacent terrestrial ecosystems. Previous research has examined eDNA detectability of single species after high flow events, but no studies have compared aquatic and terrestrial communities recovered by eDNA metabarcoding together in response to rainfall. For this study, we used eDNA metabarcoding to sample three rivers before and after precipitation over six sampling events to evaluate if terrestrial eDNA exhibits a mobilization effect and aquatic eDNA exhibits a dilution effect after rainfall. We found that as rainfall increased, terrestrial taxa richness significantly increased and aquatic taxa richness decreased but not significantly. As such, researchers using eDNA metabarcoding from lotic ecosystems to characterize terrestrial communities might not need to avoid, and could even seek out, precipitation events in their sampling design. However, our study should be replicated over more lotic ecosystems and ecoregions and larger gradients of precipitation events.