The neutral model posits that random variation in extinction and speciation events, coupled with limited dispersal, can account for many community properties, including the relative abundance ...distribution. There are important analogies between this model in ecology and a three-tiered hierarchy of models in evolution (Hardy Weinburg, drift, drift and selection). Because it invokes random processes and is used in statistical tests of empirical data, the neutral model can be interpreted as a specialized form of a null model. However, the application and interpretation of neutral models differs from that of standard null models in three important ways: 1) whereas most null models incorporate specieslevel constraints that are often associated with niche differences, the neutral model assumes that all species are functionally equivalent. 2) Null models are usually fit with constraints that are measured directly from the data set itself. In contrast, the neutral model requires parameters for speciation, extinction, and migration rates that are almost never measured directly, so their values must be guessed at or fitted. 3) Most important, null models are viewed as simple statistical descriptors: unspecified "random" forces generate variation in a simple model that excludes particular biological mechanisms (usually species interactions). Although the neutral model was originally framed as a null model, recent proponents of the neutral model have begun to treat it as a literal process-based description of community assembly. These differences lie at the heart of much of the recent controversy over the neutral model. If the neutral model is truly a process-based model, then its assumptions should be directly tested, and its predictions should be compared to those of an appropriate null model. Such tests are rarely informative, and most empirical data sets can be fit more parsimoniously to a simple log-normal distribution. Because unknown parameters in the neutral model must usually be guessed at or fit in ad-hoc ways, classical frequentist tests are compromised, and may be biased towards finding a good fit with the model. There has been little analysis of the potential for type I and type II errors in statistical tests of the neutral model. The neutral model has recently been proposed as a specific form of more general null models in biogeography (the mid-domain effect) and community ecology (species co-occurrence). In both cases, the neutral model is qualitatively, but not quantitatively, similar to the predictions of classic null models. However, because the important parameters in the neutral model can rarely be measured directly, it may be of limited value as a null hypothesis for empirical tests. Future progress may come from moving beyond dichotomous tests of neutral versus null models. Instead, the neutral model might be viewed as a mechanism that contributes to pattern along with other processes. Alternatively, the fit of data to the neutral model can be compared to the fit to other process-based models that are not based on neutrality assumptions. Finally, the neutral model can also be tested directly if its parameters can be estimated independently of the test data. However, these approaches may require more data than are often available. For these reasons, simple null model tests will continue to be important in the evaluation of the neutral model.
Accurate knowledge of the vertical and horizontal extent of clouds and aerosols in the earth's atmosphere is critical in assessing the planet's radiation budget and for advancing human understanding ...of climate change issues. To retrieve this fundamental information from the elastic backscatter lidar data acquired during the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, a selective, iterated boundary location (SIBYL) algorithm has been developed and deployed. SEBYL accomplishes its goals by integrating an adaptive context-sensitive profile scanner into an iterated multiresolution spatial averaging scheme. This paper provides an in-depth overview of the architecture and performance of the SIBYL algorithm. It begins with a brief review of the theory of target detection in noise-contaminated signals, and an enumeration of the practical constraints levied on the retrieval scheme by the design of the lidar hardware, the geometry of a space-based remote sensing platform, and the spatial variability of the measurement targets. Detailed descriptions are then provided for both the adaptive threshold algorithm used to detect features of interest within individual lidar profiles and the fully automated multiresolution averaging engine within which this profile scanner functions. The resulting fusion of profile scanner and averaging engine is specifically designed to optimize the trade-offs between the widely varying signal-to-noise ratio of the measurements and the disparate spatial resolutions of the detection targets. Throughout the paper, specific algorithm performance details are illustrated using examples drawn from the existing CALIPSO dataset. Overall performance is established by comparisons to existing layer height distributions obtained by other airborne and space-based lidars. PUBLICATION ABSTRACT
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Aim: Distribution modelling relates sparse data on species occurrence or abundance to environmental information to predict the population of a species at any point in space. Recently, the importance ...of spatial autocorrelation in distributions has been recognized. Spatial autocorrelation can be categorized as exogenous (stemming from autocorrelation in the underlying variables) or endogenous (stemming from activities of the organism itself, such as dispersal). Typically, one asks whether spatial models explain additional variability (endogenous) in comparison to a fully specified habitat model. We turned this question around and asked: can habitat models explain additional variation when spatial structure is accounted for in a fully specified spatially explicit model? The aim was to find out to what degree habitat models may be inadvertently capturing spatial structure rather than true explanatory mechanisms. Location: We used data from 190 species of the North American Breeding Bird Survey covering the conterminous United States and southern Canada. Methods: We built 13 different models on 190 bird species using regression trees. Our habitat-based models used climate and landcover variables as independent variables. We also used random variables and simulated ranges to validate our results. The two spatially explicit models included only geographical coordinates or a contagion term as independent variables. As another angle on the question of mechanism vs. spatial structure we pitted a model using related bird species as predictors against a model using randomly selected bird species. Results: The spatially explicit models outperformed the traditional habitat models and the random predictor species outperformed the related predictor species. In addition, environmental variables produced a substantial R² in predicting artificial ranges. Main conclusions: We conclude that many explanatory variables with suitable spatial structure can work well in species distribution models. The predictive power of environmental variables is not necessarily mechanistic, and spatial interpolation can outperform environmental explanatory variables.
Continuous-trait game theory fills the niche of enabling analytically solvable models of the evolution of biologically realistically complex traits. Game theory provides a mathematical language for ...understanding evolution by natural selection. Continuous-trait game theory starts with the notion of an evolutionarily stable strategy (ESS) and adds the concept of convergence stability (that the ESS is an evolutionary attractor). With these basic tools in hand, continuous-trait game theory can be easily extended to model evolution under conditions of disruptive selection and speciation, nonequilibrium population dynamics, stochastic environments, coevolution, and more. Many models applying these tools to evolutionary ecology and coevolution have been developed in the past two decades. Going forward we emphasize the communication of the conceptual simplicity and underlying unity of ideas inherent in continuous-trait game theory and the development of new applications to biological questions.
•We built multimetric indices (MMIs) of wetland condition for vegetation, soil, water and algae.•We used data from the Environmental Protection Agency's National Wetland Condition ...Assessment.•Vegetation and soil were the best performing MMIs, and included many commonly cited indicators.•Adjacent land use and vegetation were strong predictors of soil, water, and algae MMIs.•The wetland MMIs we constructed are applicable to a range of wetland types covering 11 eastern US states.
Using data collected for the Environmental Protection Agency's (EPA) 2011 National Wetland Condition Assessment (NWCA), we developed separate multimetric indices (MMIs) for vegetation, soil, algae taxa, and water to assess condition of freshwater wetlands in the northeastern US. This study represents the first attempt at developing multiple biotic and abiotic MMIs of wetland condition over this large of an area, and is only possible because of the high quality data collected by the NWCA. We chose metrics that distinguished between reference and most disturbed sites, had a signal:noise ratio>2, and were not strongly correlated with other metrics, latitude, or longitude. The vegetation and soil MMIs were the best performing indices, with good separation between reference and most disturbed sites, and included commonly used condition metrics (e.g., pH and P concentration for soil, and percent cover of exotic species for vegetation). The algae MMI was the weakest index, with considerable overlap between reference and most disturbed sites. For areas smaller than our study, algae taxa may be suitable for wetland MMIs. However, in our study area, many algae taxa followed strong latitudinal or longitudinal gradients, and could not be considered for the algae MMI. Small sample size and several metrics with a high signal:noise ratio were the major limitations of the water MMI. We also examined how well landscape (level 1) and rapid assessment (level 2) metrics predicted MMIs using random forest regression. Agricultural land use surrounding wetlands was an important predictor for all four MMIs, although the soil, algae and water MMI models performed best when intensive (level 3) vegetation metrics were also included in the random forest regression models. Based on these results, we recommend wetland assessment programs employ a combination of landscape and rapid assessment monitoring at many sites, along with level 3 monitoring at a subset of sites. We developed these MMIs to evaluate freshwater wetland condition for a long-term monitoring program in Acadia National Park. These MMIs are also applicable to a range of wetland types covering 11 states in the northeastern United States and can be calculated using a downloadable spreadsheet that calculates and rates each MMI using raw metric values.
Behaviour is often the first response of organisms to rapid environmental change and can be a key mediator of ecological responses at higher organisational levels.Macroecology seeks to understand ...patterns and processes that emerge from the interaction of many smaller components, and behaviour is an important but understudied category of component.We propose the new field of macrobehaviour, which aims to unify behavioural ecology and macroecology. Researchers from both disciplines can take advantage of new tools, approaches, concepts, and data, and ultimately ask new interdisciplinary questions.
We explore how integrating behavioural ecology and macroecology can provide fundamental new insight into both fields, with particular relevance for understanding ecological responses to rapid environmental change. We outline the field of macrobehaviour, which aims to unite these disciplines explicitly, and highlight examples of research in this space. Macrobehaviour can be envisaged as a spectrum, where behavioural ecologists and macroecologists use new data and borrow tools and approaches from one another. At the heart of this spectrum, interdisciplinary research considers how selection in the context of large-scale factors can lead to systematic patterns in behavioural variation across space, time, and taxa, and in turn, influence macroecological patterns and processes. Macrobehaviour has the potential to enhance forecasts of future biodiversity change.
We explore how integrating behavioural ecology and macroecology can provide fundamental new insight into both fields, with particular relevance for understanding ecological responses to rapid environmental change. We outline the field of macrobehaviour, which aims to unite these disciplines explicitly, and highlight examples of research in this space. Macrobehaviour can be envisaged as a spectrum, where behavioural ecologists and macroecologists use new data and borrow tools and approaches from one another. At the heart of this spectrum, interdisciplinary research considers how selection in the context of large-scale factors can lead to systematic patterns in behavioural variation across space, time, and taxa, and in turn, influence macroecological patterns and processes. Macrobehaviour has the potential to enhance forecasts of future biodiversity change.
Empirical evaluation of neutral theory McGill, Brian J.; Maurer, Brian A.; Weiser, Michael D.
Ecology (Durham),
June 2006, Letnik:
87, Številka:
6
Journal Article
Recenzirano
We describe a general framework for testing neutral theory. We summarize similarities and differences between ten different versions of neutral theory. Two central predictions of neutral theory are ...that species abundance distributions will follow a zero-sum multinomial distribution and that community composition will change over space due to dispersal limitation. We review all published empirical tests of neutral theory. With the exception of one type of test, all tests fail to support neutral theory. We identify and perform several new tests. Specifically, we develop a set of best practices for testing the fit of the zero-sum multinomial (ZSM) vs. a lognormal null hypothesis and apply this to a data set, concluding that the lognormal outperforms neutral theory on robust tests. We explore whether a priori parameterization of neutral theory is possible, and we conclude that it is not. We show that non-curve-fitting predictions readily derived from neutral theory are easily falsifiable. In toto, there is a current overwhelming weight of evidence against neutral theory. We suggest some next steps for neutral theory.
The species composition of plant and animal assemblages across the globe has changed substantially over the past century. How do the dynamics of individual species cause this change? We classified ...species into seven unique categories of temporal dynamics based on the ordered sequence of presences and absences that each species contributes to an assemblage time series. We applied this framework to 14,434 species trajectories comprising 280 assemblages of temperate marine fishes surveyed annually for 20 or more years. Although 90% of the assemblages diverged in species composition from the baseline year, this compositional change was largely driven by only 8% of the species' trajectories. Quantifying the reorganization of assemblages based on species shared temporal dynamics should facilitate the task of monitoring and restoring biodiversity. We suggest ways in which our framework could provide informative measures of compositional change, as well as leverage future research on pattern and process in ecological systems.
How do individual species contribute to long‐term patterns of compositional change? We devised a canonical classification scheme in which each species in a time series assemblage is assigned uniquely to one of seven different categories (colors) of temporal dynamics. In an analysis of 280 north temperate fish assemblages monitored for 20 or more years, a key result is that only 8% of the species (orange and brown rows) contribute to systematic changes in species composition away from an initial baseline composition (first column).
A key feature of life's diversity is that some species are common but many more are rare. Nonetheless, at global scales, we do not know what fraction of biodiversity consists of rare species. Here, ...we present the largest compilation of global plant diversity to quantify the fraction of Earth's plant biodiversity that are rare. A large fraction, ~36.5% of Earth's ~435,000 plant species, are exceedingly rare. Sampling biases and prominent models, such as neutral theory and the k-niche model, cannot account for the observed prevalence of rarity. Our results indicate that (i) climatically more stable regions have harbored rare species and hence a large fraction of Earth's plant species via reduced extinction risk but that (ii) climate change and human land use are now disproportionately impacting rare species. Estimates of global species abundance distributions have important implications for risk assessments and conservation planning in this era of rapid global change.
We present new data and analyses revealing fundamental flaws in a critique of two recent meta-analyses of local-scale temporal biodiversity change. First, the conclusion that short-term time series ...lead to biased estimates of long-term change was based on two errors in the simulations used to support it. Second, the conclusion of negative relationships between temporal biodiversity change and study duration was entirely dependent on unrealistic model assumptions, the use of a subset of data, and inclusion of one outlier data point in one study. Third, the finding of a decline in local biodiversity, after eliminating post-disturbance studies, is not robust to alternative analyses on the original data set, and is absent in a larger, updated data set. Finally, the undebatable point, noted in both original papers, that studies in the ecological literature are geographically biased, was used to cast doubt on the conclusion that, outside of areas converted to croplands or asphalt, the distribution of biodiversity trends is centered approximately on zero. Future studies may modify conclusions, but at present, alternative conclusions based on the geographic-bias argument rely on speculation. In sum, the critique raises points of uncertainty typical of all ecological studies, but does not provide an evidence-based alternative interpretation.
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