Ecology最新文献

The Swiss Common Breeding Bird Monitoring (“Monitoring Häufige Brutvögel” MHB) is a long-term study organized by the Swiss Ornithological Institute. Its main goal is to collect data for estimating breeding population trends of relatively abundant and widespread species. Since 1999, 267 one-km squares laid out in a mostly systematic grid across all of Switzerland have been surveyed annually by skilled, mostly volunteer ornithologists. The sampling sites thus cover a wide range of typical Western European habitats, and an altitudinal range from 250 up to 2750 m above sea level. Bird populations are recorded using a simplified territory mapping protocol with two visits per square above the timberline and three elsewhere. Surveys are conducted during the breeding season (mid-April to early July) along a square-specific transect route that does not change over the years. A typical transect route is between 4 and 6 km long, and each visit usually lasts 3 to 4 h. The location of all visually or acoustically detected birds is recorded on topographical maps or using a smartphone app. Records that meet predefined criteria in terms of species-specific breeding period and observed behavior are retained for the subsequent step of territory delimitation. This is done automatically for most species by the program Autoterri since 2022 and was done manually before, with subsequent checks by an expert. This process finally results in an estimate of the total number of detected territories per species, square and year. The design also explicitly generates detection histories, consisting of two to three numbers that represent the number of territories found to be occupied during each respective visit, enabling the analysis with binomial N-mixture and site-occupancy models. The dataset currently covers the breeding seasons from 1999 to 2024 and includes 6852 site-by-year combinations with estimates of detected territory numbers. It covers 162 of the 166 bird species recorded at least once as potential breeders, excluding four species to prevent potential disturbance at nesting sites. Besides informing about population trends, data from the Swiss Common Breeding Bird Monitoring were used to illustrate several methodological developments in N-mixture, occupancy and related models and to answer scientific and applied questions. With its clearly defined survey method, the largely systematic distribution of its survey sites, and the long timespan covered, it is likely that this dataset will continue to make important contributions in biological and biostatistical research. Herewith, we make the annually updated data set available with a CC BY 4.0 license, allowing researchers and conservationists to use and analyze the data for their own research and conservation efforts.

Understanding the stable coexistence of species despite resource competition has been a central topic in ecology. Ant communities are particularly enigmatic as various species coexist despite resource overlap. Community ecology theory predicts stable species coexistence when intraspecific competition is stronger than interspecific competition, but due to their perennial and underground life, competition coefficients of ants have never been rigorously measured in the field. We tackled this problem by studying Diacamma cf. indicum, which allows for noninvasive mark–recapture of whole colonies. Several ant species coexisted at the study site where Diacamma was most abundant, and baiting experiments and stable isotope analyses suggested overlapping food niches. Consistently, per worker brood production of Diacamma colonies was significantly negatively correlated with con- and heterospecific worker densities within the foraging area, suggesting exploitative competition among the ants. In terms of net population growth, however, the estimated intraspecific competition coefficient was about five times larger than the interspecific competition coefficient. This is possibly because exploitative competition for food occurs both intra- and interspecifically, whereas interference competition occurs mostly among conspecifics. Indeed, for Diacamma worker survival, there was a significant (nonlinear) negative correlation only with the density of conspecific colonies within the foraging area. This is consistent with the observation that Diacamma rarely fought with other species, although it violently attacked conspecific aliens encountered in their nest vicinity. We interpreted these results in light of the recent theory of intraspecific adaptation load. This theory predicts that density-dependent adaptation to intraspecific conflict can intensify intraspecific competition and act to suppress per capita population growth in dominant species, thereby leading to species coexistence with overlapping resources. Our inclusive fitness model suggests that the intraspecific territorial aggression in Diacamma may be a counter-adaptation to intraspecific conflict, that is, brood abduction between conspecific colonies. This aggression pattern can cause the observed density-dependent worker mortality. Our population dynamic model indicates that such density-dependent excess mortality acting on dominant competitors can promote stable coexistence with subordinate competitors. Overall, our results support the intraspecific adaptation load theory that aims at integrating behavioral and community ecology to understand how adaptation interacts with population and community dynamics.

Meta-analysis (MA), a powerful tool for synthesizing reported results, is influential in ecology. While ecologists have long been well-informed on the potential problems associated with nonindependence in experimental work (e.g., pseudoreplication), they have, until recently, largely neglected this issue in MA. However, results used in MAs are likely much more similar when they come from the same locality, system, or laboratory. A simple and common form of nonindependence in MA arises when multiple data points, that is, observed effect sizes, come from the same paper. We obtained original data from 20 published MAs, reconstructed the published analyses, and then, for 14 that had not accounted for a paper effect, used three approaches to evaluate whether within-paper nonindependence was a problem. First, we found that “nonsense” explanatory variables added to the original analyses were statistically significant (p < 0.05) far more often than the expected 5% (25%–50% for four nonsense variables). For example, the number of vowels in the first author's name had a significant effect 50% of the time. Second, we found that an added dummy variable, which was randomly assigned at one of two levels, was statistically significant an average of 38% of the time, far exceeding the expected 5%. Even after including a random paper effect in the analyses, there was still an excess of significant results, suggesting that the within-paper nonindependence was more complex than modeled with the random paper effect. Third, we repeated the original MAs that did not include random paper effects (n = 14 MAs) but added a random paper effect to each revised analysis. In 12 out of the 14 MAs, an added random effect was statistically significant (indicating group nonindependence that was not accounted for in the original analyses), and often the original inferences were substantially altered. Further, incorporating random paper effects was not a sufficient solution to nonindependence. Thus, problems resulting from nonindependence are often substantial, and accounting for the problem will likely require careful consideration of the details of the potential dependence among observed effect sizes. MAs that do not properly account for this problem may reach unwarranted conclusions.

Analysis of time series data is fundamental in ecology for understanding community dynamics, and the mechanisms driving such dynamics. However, ecological time series commonly contain missing values, which can arise due to methodological changes in monitoring programs, poor weather conditions, logistical constraints, or human error. State-space models are a useful suite of techniques for analyzing ecological time series with missing data. Such models can estimate the unobserved true abundances as latent variables, even for putative census occasions that lack observations. Nevertheless, the impact of missing data on parameter accuracy and precision in these state-space models remains poorly investigated, particularly in species-rich systems. We evaluated the performance of a multivariate process-based state-space model for time series of varying lengths and sampling frequencies, using simulated data informed by empirical counts of reef fish communities from the Great Barrier Reef in Australia. We found that despite containing missing data, the models fitted to longer time series produced more accurate and precise parameter estimates compared to shorter, complete-case time series. Additionally, higher spatial replication with uneven census intervals enhanced parameter precision more than maintaining census frequency at the expense of reducing the number of sites. Analysis of reef fish community data revealed attenuation in intrinsic population growth parameters and weaker intraspecific density dependence when modeling longer time series whose inter-census intervals change, while estimates of variability in species' population growth parameters due to environmental fluctuations remained consistent regardless of sampling design. Nonetheless, analyses of shorter, complete-case time series still produced reliable parameter estimates. Our findings highlight the robustness of Bayesian state-space models to missing data, demonstrating that flexibility in long-term monitoring programs does not compromise ecological inference from these models.

Consumers play a critical role in mediating plant and ecosystem responses to abiotic stress, yet their influence on belowground processes under changing environmental conditions remains underexplored. Insect consumers are vital components of grassland ecosystems that can shape ecosystem function and stability by mitigating how plant and microbial communities respond to abiotic stress, like drought. This study investigates how small-bodied consumers influence the magnitude and stability of grassland belowground functions across gradients of abiotic stress. We conducted a fully factorial field experiment manipulating consumer presence and induced drought over a growing season. Our results reveal that the presence of consumers stabilizes bacterial biomass and microbial activity across variable soil moisture conditions. Interestingly, this consumer-induced increase in ecosystem stability was driven by a destabilization of microbial communities, as indicated by increased variability in bacterial community composition and abundance. Consumer presence also shifted soil bacterial community composition and richness, while fungal communities were less affected. Combined, our results highlight another important dimension of ecosystem stability: community responsiveness and rapid adaptability. Additionally, our findings underscore the critical role of consumers in maintaining belowground ecosystem stability and highlight the need to consider trophic interactions when predicting the impacts of global change on grassland ecosystems.

Sudden habitat loss associated with environmental disturbance can trigger animals to move from affected to undisturbed areas, where increases in local density may occur. Although pathogen transmission is strongly related to local density, how crowding after habitat loss affects infection dynamics in wild populations remains unclear. Here we conceptualize the Disturbance-Density-Disease hypothesis, which posits that disturbance-induced habitat loss results in increased pathogen prevalence via increases in local density at adjacent, undisturbed patches. We then used data from before, during, and after flooding disturbance to test this hypothesis in boreal toads Anaxyrus boreas boreas co-occurring with the pathogenic fungus Batrachochytrium dendrobatidis (Bd). We collected Bd samples from captured individuals during a 5-year (2015–2019) mark-recapture study of boreal toads (n = 1295) that breed in beaver ponds in western Wyoming, USA. During spring of 2017, an extreme flooding event destroyed several beaver dams, resulting in the loss of breeding habitat. We compared host density and pathogen prevalence pre- and post-disturbance at sites affected versus unaffected by flooding. At affected sites, population density and Bd prevalence increased at adjacent, undisturbed ponds following the sudden loss of habitat. Moreover, neither host density nor Bd prevalence increased at control sites in areas unaffected by flooding. Taken together, our results support hypothesized links between disturbance, adjacent increases in density, and subsequent increases in pathogen prevalence. Our study contributes to a growing body of ecological research leveraging natural experiments to extract insights from extreme disturbance events. By doing so, we demonstrate an important consequence of disturbance beyond proximate habitat loss and introduce a clear conceptual approach (the Disturbance-Density-Disease hypothesis) to understanding how pathogen transmission can be affected by disturbance via alterations to local density.

Biological systems face multiple stressors that impact biodiversity and ecosystem functions, with complex interactions that vary spatially and temporally leading to unpredictable outcomes. In freshwater ecosystems, benthic microbial communities underpin vital functions like decomposition and primary productivity, but are threatened by stressors such as salinization and nutrient enrichment, which are intensifying and increasingly co-occurring due to climate change. We experimentally tested how stressor repetition and different orders of stressor exposure affect freshwater benthic microbial community responses over time. Communities established on tiles in 1000 L open freshwater ponds established more than 10 years ago in the field were exposed to elevated salinity and nutrient enrichment, either once or repeatedly, independently or in combination, and under different orders. Repeated exposure to nutrient enrichment led to stronger functional changes than the single exposure, while repeated exposure to elevated salinity resulted in weaker changes compared to a single exposure. Critically, we found that the sequence in which stressors occurred was a major determinant of microbial responses, driving interaction outcomes in opposing directions. When exposure to nutrient enrichment preceded elevated salinity, gross primary productivity was halved and carbon metabolic rates increased by 50% compared to communities treated in the reverse order. This study is among the first in a complex, outdoor freshwater system to demonstrate that stressor sequence can strongly shape multiple stressor effects, highlighting the order of stressor exposure as a key but often overlooked dimension of global change ecology. These findings suggest that microbial functions, including productivity and carbon cycling, will fluctuate more dramatically as stressors increasingly occur in different sequences under global change.