Fine-scale habitat modeling of a top marine predator: do prey data improve predictive capacity?

Predators and prey assort themselves relative to each other, the availability of resources and refuges, and the temporal and spatial scale of their interaction. Predictive models of predator distributions often rely on these relationships by incorporating data on environmental variability and prey availability to determine predator habitat selection patterns. This approach to predictive modeling holds true in marine systems where observations of predators are logistically difficult, emphasizing the need for accurate models. In this paper, we ask whether including prey distribution data in fine-scale predictive models of bottlenose dolphin (Tursiops truncatus) habitat selection in Florida Bay, Florida, U.S.A., improves predictive capacity. Environmental characteristics are often used as predictor variables in habitat models of top marine predators with the assumption that they act as proxies of prey distribution. We examine the validity of this assumption by comparing the response of dolphin distribution and fish catch rates to the same environmental variables. Next, the predictive capacities of four models, with and without prey distribution data, are tested to determine whether dolphin habitat selection can be predicted without recourse to describing the distribution of their prey. The final analysis determines the accuracy of predictive maps of dolphin distribution produced by modeling areas of high fish catch based on significant environmental characteristics. We use spatial analysis and independent data sets to train and test the models. Our results indicate that, due to high habitat heterogeneity and the spatial variability of prey patches, fine-scale models of dolphin habitat selection in coastal habitats will be more successful if environmental variables are used as predictor variables of predator distributions rather than relying on prey data as explanatory variables. However, predictive modeling of prey distribution as the response variable based on environmental variability did produce high predictive performance of dolphin habitat selection, particularly foraging habitat.     

Can entropy maximization use functional traits to explain species abundances? A comprehensive evaluation

Entropy maximization (EM) is a method that can link functional traits and community composition by predicting relative abundances of each species in a community using limited trait information. We developed a complementary suite of tests to examine the strengths and limitations of EM and the community-aggregated traits (CATs; i.e., weighted averages) on which it depends that can be applied to virtually any plant community data set. We show that suites of CATs can be used to differentiate communities and that EM can address the classic problem of characterizing ecological niches by quantifying constraints (CATs) on complex trait relationships in local communities. EM outperformed null models and comparable regression models in communities with different levels of dominance, diversity, and trait similarity. EM predicted well the abundance of the dominant species that drive community-level traits; it typically identified rarer species as such, although it struggled to predict the abundances of the rarest species in some cases. Predictions were sensitive to choice of traits, were substantially improved by using informative priors based on null models, and were robust to variation in trait measurement due to intraspecific variability or measurement error. We demonstrate how similarity in species' traits confounds predictions and provide guidelines for applying EM.     

Inferential permutation tests for maximum entropy models in ecology

Maximum entropy (maxent) models assign probabilities to states that (1) agree with measured macroscopic constraints on attributes of the states and (2) are otherwise maximally uninformative and are thus as close as possible to a specified prior distribution. Such models have recently become popular in ecology, but classical inferential statistical tests require assumptions of independence during the allocation of entities to states that are rarely fulfilled in ecology. This paper describes a new permutation test for such maxent models that is appropriate for very general prior distributions and for cases in which many states have zero abundance and that can be used to test for conditional relevance of subsets of constraints. Simulations show that the test gives correct probability estimates under the null hypothesis. Power under the alternative hypothesis depends primarily on the number and strength of the constraints and on the number of states in the model; the number of empty states has only a small effect on power. The test is illustrated using two empirical data sets to test the community assembly model of B. Shipley, D. Vile, and E. Garnier and the species abundance distribution models of S. Pueyo, F. He, and T. Zillio.     

主成份分析在城市生态经济动态评价中的应用

城市生态经济的动态发展过程,可以看作多种生态关系要素相互作用的集合沿时序轴波动的轨迹.在这种多变量分析中,完全采用人工筛选变量的方法难以避免确定权重等过程所带来的主观干扰.PCA(主成份分析)采用一种特殊的转换降维方法,根据原始数据自身的特点综合成几个主导因子,并最大程度反应原始数据的信息量.本文根据天津市四十年18项生态经济指标,采用相关矩阵和PCA双重筛选过程,得到载荷量大于90%的前三个主分量,将其作为天津市生态经济动态评价的基础.结果表明:天津市解放后四十年每十年一次的经济周期上迭加了自然社会等非经济因素的影响,表现出城市生态经济动态发展的三大阶段,这种发展主要受到宏观经济管理模式及其相应的投资机制的制约.     

Testing the robustness of primary production models in shallow coastal areas: a case study

In this paper we investigate the robustness of a dynamic model, which describes the dynamic of the seagrass Zostera marina, with respect to the inter-annual variability of the two main forcing functions of primary production models in eutrophicated environments. The model was previously applied to simulate the seasonal evolution of this species in the Lagoon of Venice during a specific year and calibrated against time series of field data. In the this paper, we present and discuss the results which were obtained by forcing the model using time series of site-specific daily values concerning the solar radiation intensity and water temperature. The latter was estimated by means of a regression model, whose input variable was a site-specific time series of the air temperature. The regression model was calibrated using a year-long time series of hourly observations. The Z. marina model was first partially recalibrated against the same data set that was used in the original paper. Subsequently, the model was forced using a 7-year-long time series of the driving functions, in order to check the reliability of its long-term predictions. Even though the calibration gave satisfactory results, the multi-annual trends of the output variables were found to be in contrast with the observed evolution of the seagrass biomasses. Since detailed information about the air temperature and solar radiation are often available, these findings suggest that the testing of the ecological consistency of the evolution of primary production models in the long term would provide additional confidence in their results, particularly in those cases in which the scarcity of field data does not allow one to perform a formal corroboration/validation of these models.     

A single-chain-based multidimensional Markov chain model for subsurface characterization

Multidimensional Markov chain models in geosciences were often built on multiple chains, one in each direction, and assumed these 1-D chains to be independent of each other. Thus, unwanted transitions (i.e., transitions of multiple chains to the same location with unequal states) inevitably occur and have to be excluded in estimating the states at unobserved locations. This consequently may result in unreliable estimates, such as underestimation of small classes (i.e., classes with smaller than average areas) in simulated realizations. This paper presents a single-chain-based multidimensional Markov chain model for estimation (i.e., prediction and conditional stochastic simulation) of spatial distribution of subsurface formations with borehole data. The model assumes that a single Markov chain moves in a lattice space, interacting with its nearest known neighbors through different transition probability rules in different cardinal directions. The conditional probability distribution of the Markov chain at the location to be estimated is formulated in an explicit form by following the Bayes’ Theorem and the conditional independence of sparse data in cardinal directions. Since no unwanted transitions are involved, the model can estimate all classes fairly. Transiogram models (i.e., 1-D continuous Markov transition probability diagrams) are used to provide transition probability input with needed lags to generalize the model. Therefore, conditional simulation can be conducted directly and efficiently. The model provides an alternative for heterogeneity characterization of subsurface formations.

Evaluating the relative importance of intrinsic and extrinsic factors on the foraging activity of top predators: a case study on female little penguins

A complex interaction of biotic and abiotic factors influences animal foraging activity. It is often difficult to understand which factors may affect animals’ foraging and how it is affected. For instance, whereas the effect of sexual dimorphism on foraging activity has been reported in several species, little is known of the complex interactions between variables acting at a finer scale, e.g. the variability of body mass within a sex. Evaluating the importance of these finer scale factors is also essential to the understanding of foraging behaviour. We propose here a simple approach by applying principal component analysis (PCA) in a novel way to examine relationships between biotic and abiotic factors affecting foraging behaviour of top predators. We studied female little penguins (Eudyptula minor) of known age, carrying miniature accelerometers during the guard stage of breeding. Surprisingly, the body mass of the females did not influence any of the foraging parameters, but females foraging later in the breeding season dived shallower and more often, showing a strong correlation with laying date. Similarly, the diving effort of females was greater with increasing chick age within the same breeding stage. These results indicate that for female little penguin, the relationship between changes in prey availability and hunting effort can change at a fine scale, within a breeding stage. Therefore, any analysis of little penguin foraging behaviour during breeding should consider the timing in relation to the breeding season. We encourage researchers to develop the use of this PCA approach as it could help clarify the complexity of the underlying mechanisms determining foraging activity and we propose that it should be used as a first step of foraging behaviour analysis, before examining a particular relationship.     

A generalized estimating equations approach for capture–recapture closed population models

The estimation of population density animal population parameters, such as capture probability, population size, or population density, is an important issue in many ecological applications. Capture–recapture data may be considered as repeated observations that are often correlated over time. If these correlations are not taken into account then parameter estimates may be biased, possibly producing misleading results. We propose a generalized estimating equations (GEE) approach to account for correlation over time instead of assuming independence as in the traditional closed population capture–recapture studies. We also account for heterogeneity among observed individuals and over-dispersion, modelling capture probabilities as a function of covariates. The GEE versions of all closed population capture–recapture models and their corresponding estimating equations are proposed. We evaluate the effect of accounting for correlation structures on capture–recapture model selection based on the quasi-likelihood information criterion (QIC). An example is used for an illustrative application and for comparison to currently used methodology. A Horvitz–Thompson-like estimator is used to obtain estimates of population size based on conditional arguments. A simulation study is conducted to evaluate the performance of the GEE approach in capture-recapture studies. The GEE approach performs well for estimating population parameters, particularly when capture probabilities are high. The simulation results also reveal that estimated population size varies on the nature of the existing correlation among capture occasions.     

A Bayesian hierarchical framework for calibrating aquatic biogeochemical models

Model practitioners increasingly place emphasis on rigorous quantitative error analysis in aquatic biogeochemical models and the existing initiatives range from the development of alternative metrics for goodness of fit, to data assimilation into operational models, to parameter estimation techniques. However, the treatment of error in many of these efforts is arguably selective and/or ad hoc. A Bayesian hierarchical framework enables the development of robust probabilistic analysis of error and uncertainty in model predictions by explicitly accommodating measurement error, parameter uncertainty, and model structure imperfection. This paper presents a Bayesian hierarchical formulation for simultaneously calibrating aquatic biogeochemical models at multiple systems (or sites of the same system) with differences in their trophic conditions, prior precisions of model parameters, available information, measurement error or inter-annual variability. Our statistical formulation also explicitly considers the uncertainty in model inputs (model parameters, initial conditions), the analytical/sampling error associated with the field data, and the discrepancy between model structure and the natural system dynamics (e.g., missing key ecological processes, erroneous formulations, misspecified forcing functions). The comparison between observations and posterior predictive monthly distributions indicates that the plankton models calibrated under the Bayesian hierarchical scheme provided accurate system representations for all the scenarios examined. Our results also suggest that the Bayesian hierarchical approach allows overcoming problems of insufficient local data by “borrowing strength” from well-studied sites and this feature will be highly relevant to conservation practices of regions with a high number of freshwater resources for which complete data could never be practically collected. Finally, we discuss the prospect of extending this framework to spatially explicit biogeochemical models (e.g., more effectively connect inshore with offshore areas) along with the benefits for environmental management, such as the optimization of the sampling design of monitoring programs and the alignment with the policy practice of adaptive management.     

武汉月湖水体主要元素与浮游植物的PCA与CCA分析

通过2007年1—12月对武汉月湖不同的取样点进行监测,并选择汉江琴台段水域作为研究对照,调查了月湖水体硅藻、蓝藻与水体主要元素的种类,采用PCA、CCA分析法探讨了月湖水体硅藻、蓝藻与水体元素种类的关系。结果表明：月湖水体中所含元素全年监测出28种;汉江琴台段水域中所含元素全年监测出27种。经PCA分析表明,月湖水体中累计贡献率较大为磷（P）、砷（As）、铁（Fe）、铜（Cu）、锰（Mn）;汉江琴台段水域累计贡献率较大为硅（Si）、锑（Sb）、镉（cd）、钒（V）、钡（Ba）、Ag（银）、钼（Mo）。月湖水体中蓝藻密度大于硅藻,汉江琴台段水域硅藻密度大于蓝藻。经CCA分析表明,月湖硅藻密度与硒（Se）、锶（sr）、银（Ag）、Ba、铝（A1）呈正相关关系;月湖蓝藻密度与P、Cu、铬（Cr）呈正相关关系,月湖蓝藻密度与镁（Mg）、镍（Ni）、钙（ca）、锌（zn）、硫（S）呈负相关关系。CCA分析中,汉江琴台段硅藻密度与铅（Pb）、硼（B）、As、Cr、Zn、A1、Cu、Mn、Fe、P呈负相关关系,汉江琴台段硅藻密度与Mo、ca、钴（Co）、V、Sr、Ag呈正相关关系;汉江琴台段蓝藻密度与钠（Na）、S、Mg、Ni、钾（K）呈负相关关系。月湖水体缺乏可溶硅（dissolvedsilicon,DSi）,硅藻会提升对其他元素（Se、Sr、Ag、Ba、A1）的吸收能力,这些元素会起到缺乏元素（si）近似的作用,替代性可能出现。水中各种元素的组态是导致月湖浮游植物群落发生演变的重要原因之一。     

The Bayesian conditional independence model for measurement error: applications in ecology

The measurement error model is a well established statistical method for regression problems in medical sciences, although rarely used in ecological studies. While the situations in which it is appropriate may be less common in ecology, there are instances in which there may be benefits in its use for prediction and estimation of parameters of interest. We have chosen to explore this topic using a conditional independence model in a Bayesian framework using a Gibbs sampler, as this gives a great deal of flexibility, allowing us to analyse a number of different models without losing generality. Using simulations and two examples, we show how the conditional independence model can be used in ecology, and when it is appropriate.     

Estimating equations for separable spatial-temporal binary data

For binary data with correlation across space and over time, the literature concerning the estimation of fixed effects in marginal models is limited. In this paper, we model the marginal probability of binary responses in terms of parameters of interest by a logistic function. An estimating equation based on the quasi-likelihood concept is developed to estimate parameters. Under separable correlation models, we show that the quasi-likelihood estimate is asymptotically optimal. A series of simulations is conducted to evaluate how the efficiency varies with the regression coefficients. We also compare the relative efficiency with another estimating equation by simulations. The proposed method is applied to an ecological study of forest decline to test independence of two spatial-temporal binary outcomes.     

Space-time modeling of environmental monitoring data

This study describes and applies statistical methods for space-time modeling of data from environmental monitoring programs, e.g., within areas such as climate change, air pollution and aquatic environment. Such data are often characterized by sparse sampling in both the temporal and spatial dimensions. In order to improve the amount of information on the physical system in question we suggest using statistical modeling methods for monitoring data. Model predictions combined with observations could be analyzed directly to assess the environmental state or as forcing functions for time series models and deterministic, hydrodynamic models. To illustrate the approach we applied the proposed modeling methods to data from the Danish and Swedish marine monitoring programs. Time series with a weekly resolution were predicted from observations of dissolved inorganic nitrogen (DIN) from the Kattegat basin (1993–1997). DIN observations were sparse, irregularly distributed and comprised approximately 10% of the generated time series.     

A water-quality model for the Lagoon of Venice,Italy

A water-quality model for the Lagoon of Venice is proposed. The model is based on the results of an existing, deterministic, hydraulic-dispersive model of the Lagoon to provide the distribution of salinity and residence time in the Lagoon of Venice. This model has been implemented by Magistrato alle Acque di Venezia and Consorzio Venezia Nuova to evaluate the environmental impact of the MOSE Project, that has the aim to defend the city of Venice from extraordinary high tides [CVN, 1997. Allegato allo studio di impatto ambientale del progetto di massima delle opere mobili per la difesa dei centri abitati lagunari dagli allagamenti, vol. 2., CVN, 2002. Studio di nuove configurazioni dei canali di bocca e del relativo adeguamento progettuale delle opere mobili alle bocche di porto].The water-quality is simulated by statistic analysis on water-quality data, monthly collected in 30 stations. The data-set covers a period of 2 years, and has been collected in the framework of MELa1, the institutional water-quality monitoring program (Magistrato alle Acque di Venezia, Consorzio Venezia Nuova). The Spearman correlation index of salinity and residence time versus the water-quality variables (nitrogen, phosphorus, chlorophyll-a and the trophic index TRIX) has been studied on a yearly average basis and for the spring–summer periods. The spatial distribution of the water-quality variables, based on the yearly average of nutrients, is mostly driven by the dispersive processes and is well correlated to salinity [Bianchi, F., Acri, F., Alberghi, M., Bastianini, M., Boldrin, A., Cavalloni, B., Cioce, F., Comaschi, A., Rabitti, S., Socal, G., Turchetto M.M., 1999. Biologocal variability in the Venice Lagoon. In: Lasserre, P., Marzollo, A. (Eds.), The Venice Lagoon Ecosystem. Input Interaction between Land and Sea, UNESCO, Man and Biosphere Series, vol. 25, pp. 97–125].The model has been applied to simulate the variation of nutrients and trophic index distribution in the Lagoon as a consequence of an increase of hydraulic dissipation at the Lagoon outlets.The work presented in this paper shows that, coupling a deterministic, distributed-parameters, dynamic, hydraulic-dispersive model to a statistic one that accounts for the correlation between hydraulic related forcing functions (salinity, residence time) and water-quality data is a promising and simple way to evaluate the water-quality of the Lagoon of Venice.Of course, this methodology is applicable because a very large data-set is now available.The usual limitations of the statistical model methodology are present in this application too. E.g., it cannot precisely estimate the values of the water-quality variables, but it can indicate how they react when the system hydrological features change. Besides, the outcomes depend strongly on site characteristics and on the actual ecosystem state.The model has not been validated yet, due to the short data time lag, but the aim of this work is to suggest a simple simulation tool whose reliability is at least the same of that obtained by complex, deterministic, dynamic water-quality models. These models, accounting for several processes and hence including a lot of parameters, require for calibration a much more detailed data-set not yet available.The increase of dissipation is altering nutrient concentrations in the Lagoon of an average +3.2%, while the average variation for TRIX is +0.4%, and for chlorophyll-a is +3.0%.These variations are small enough to confirm a posteriori the validity of the adopted statistical approach.     

Predicting tree distributions in an East African biodiversity hotspot: model selection, data bias and envelope uncertainty

The Eastern Arc Mountains (EAMs) of Tanzania and Kenya support some of the most ancient tropical rainforest on Earth. The forests are a global priority for biodiversity conservation and provide vital resources to the Tanzanian population. Here, we make a first attempt to predict the spatial distribution of 40 EAM tree species, using generalised additive models, plot data and environmental predictor maps at sub 1 km resolution. The results of three modelling experiments are presented, investigating predictions obtained by (1) two different procedures for the stepwise selection of predictors, (2) down-weighting absence data, and (3) incorporating an autocovariate term to describe fine-scale spatial aggregation. In response to recent concerns regarding the extrapolation of model predictions beyond the restricted environmental range of training data, we also demonstrate a novel graphical tool for quantifying envelope uncertainty in restricted range niche-based models (envelope uncertainty maps). We find that even for species with very few documented occurrences useful estimates of distribution can be achieved. Initiating selection with a null model is found to be useful for explanatory purposes, while beginning with a full predictor set can over-fit the data. We show that a simple multimodel average of these two best-model predictions yields a superior compromise between generality and precision (parsimony). Down-weighting absences shifts the balance of errors in favour of higher sensitivity, reducing the number of serious mistakes (i.e., falsely predicted absences); however, response functions are more complex, exacerbating uncertainty in larger models. Spatial autocovariates help describe fine-scale patterns of occurrence and significantly improve explained deviance, though if important environmental constraints are omitted then model stability and explanatory power can be compromised. We conclude that the best modelling practice is contingent both on the intentions of the analyst (explanation or prediction) and on the quality of distribution data; generalised additive models have potential to provide valuable information for conservation in the EAMs, but methods must be carefully considered, particularly if occurrence data are scarce. Full results and details of all species models are supplied in an online Appendix.     

Impacts of daily weather variability on simulations of the Canadian boreal forest

This study examines the importance of climate variability when simulating forest succession using a process-based model of stand development. The FORSKA-2V forest gap model, originally developed for forcing with monthly mean climate data, was modified to accept daily weather data. The model's performance was compared using different temporal resolutions of forcing along a bioclimatic transect crossing the boreal region of central Canada, including the aspen-parkland and forest-tundra ecotones. Forcing the model with daily weather data improved the simulation of key attributes of present-day forest along the transect, particularly at the ecotones, compared to forcing with monthly data or long term averages. The results support the hypothesis that climatic variability at daily time-scales is an important determinant of present-day boreal forest composition and productivity. To simulate boreal forest response to climatic change it will be necessary to create climatic scenarios that include plausible projections of future daily scale variability.     

The Reynolds transport theorem: Application to ecological compartment modeling and case study of ecosystem energetics

The Reynolds transport theorem (RTT) from mathematics and engineering has a rich history of success in mass transport dynamics and traditional thermodynamics. This paper introduces RTT as a complementary approach to traditional compartmental methods used in ecological modeling and network analysis. A universal system equation for a generic flow quantity is developed into a generic open-system differential expression for conservation of energy. Nonadiabatic systems are defined and incorporated into control volume (CV) and control surface (CS) perspectives of RTT where reductive assumptions in empirical data are then formally introduced, reviewed, and appropriately implemented. Compartment models are abstract, time-dependent systems of simultaneous differential equations describing storage and flow of conservative quantities between interconnected entities (the compartments). As such, they represent a set of flexible and somewhat informal, assumptions, definitions, algebraic manipulations, and graphical depictions subject to influence and selectively parsed expression by the modeler. In comparison, RTT compartment models are more rigorous and formal integro-differential equations and graphics initiated by the RTT universal system equation, forcing an ordered identification of simplifying assumptions, ending with clearly identified depictions of the transfer and transport of conservative substances in physical space and time. They are less abstract in the rigor of their equation development leaving less ambiguity to modeler discretion. They achieve greater consistency with other RTT compartment style models while possibly generating greater conformity with physical reality. Characteristics of the RTT approach are compared with those of a traditional compartment model of energy flow in an intertidal oyster-reef community.     

Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system

A fully non-linear analysis of forcing influences on temperatures is performed in the climate system by means of neural network modelling. Two case studies are investigated, in order to establish the main factors that drove the temperature behaviour at both global and regional scales in the last 140 years. In particular, our neural network model shows the ability to catch non-linear relationships among these variables and to reconstruct temperature records with a high degree of accuracy. In this framework, we clearly show the need of including anthropogenic inputs for explaining the temperature behaviour at global scale and recognise the role of El Niño southern oscillation for catching the inter-annual variability of temperature data. Furthermore, we analyse the relative influence of global forcing and a regional circulation pattern in determining the winter temperatures in Central England, showing that the North Atlantic oscillation represents the driven element in this case study. Our modelling activity and results can be very useful for simple assessments of relationships in the complex climate system and for identifying the fundamental elements leading to a successful downscaling of atmosphere–ocean general circulation models.