Modelling species distributions with penalised logistic regressions: A comparison with maximum entropy models

An important aspect of species distribution modelling is the choice of the modelling method because a suboptimal method may have poor predictive performance. Previous comparisons have found that novel methods, such as Maxent models, outperform well-established modelling methods, such as the standard logistic regression. These comparisons used training samples with small numbers of occurrences per estimated model parameter, and this limited sample size may have caused poorer predictive performance due to overfitting. Our hypothesis is that Maxent models would outperform a standard logistic regression because Maxent models avoid overfitting by using regularisation techniques and a standard logistic regression does not. Regularisation can be applied to logistic regression models using penalised maximum likelihood estimation. This estimation procedure shrinks the regression coefficients towards zero, causing biased predictions if applied to the training sample but improving the accuracy of new predictions. We used Maxent and logistic regression (standard and penalised) to analyse presence/pseudo-absence data for 13 tree species and evaluated the predictive performance (discrimination) using presence-absence data. The penalised logistic regression outperformed standard logistic regression and equalled the performance of Maxent. The penalised logistic regression may be considered one of the best methods to develop species distribution models trained with presence/pseudo-absence data, as it is comparable to Maxent. Our results encourage further use of the penalised logistic regression for species distribution modelling, especially in those cases in which a complex model must be fitted to a sample with a limited size.     

Estimation methods for nonlinear state-space models in ecology

The use of nonlinear state-space models for analyzing ecological systems is increasing. A wide range of estimation methods for such models are available to ecologists, however it is not always clear, which is the appropriate method to choose. To this end, three approaches to estimation in the theta logistic model for population dynamics were benchmarked by Wang (2007). Similarly, we examine and compare the estimation performance of three alternative methods using simulated data. The first approach is to partition the state-space into a finite number of states and formulate the problem as a hidden Markov model (HMM). The second method uses the mixed effects modeling and fast numerical integration framework of the AD Model Builder (ADMB) open-source software. The third alternative is to use the popular Bayesian framework of BUGS. The study showed that state and parameter estimation performance for all three methods was largely identical, however with BUGS providing overall wider credible intervals for parameters than HMM and ADMB confidence intervals.     

Using the maximum principle of impulse control for ecology-economical models

In this work we present mathematical models for population of single cohort and homogeneous animals. Investigating these mathematical models, we determine structure of optimal impulsive control which used maximum principle for optimal processes with impulse control.     

Analysis of composite sampling data using the principle of maximum entropy

A new tool is presented based on the principle of maximum entropy for enhanced hot spot detection based on composite sampling with no (or minimal) requirement for composite breakdown. The methodology presented here is easy to implement and facilitates the use of multiple criteria for evaluating attainment of site remediation objectives. The new methodology provides very simple decision rules that can easily be used by non-statisticians and complements the use of composites for control of residual mean concentrations.     

Reconciling empirical ecology with neutral community models

Neutral community models embody the idea that individuals are ecologically equivalent, having equal fitness over all environmental conditions, and describe how the spatial dynamics and speciation of such individuals can produce a wide range of patterns of distribution, diversity, and abundance. Neutral models have been controversial, provoking a rush of tests and comments. The debate has been spurred by the suggestion that we should test mechanisms. However, the mechanisms and the spatial scales of interest have never clearly been described, and consequently, the tests have often been only peripherally relevant. At least two mechanisms are present in spatially structured neutral models. Dispersal limitation causes clumping of a species, which increases the strength of intraspecific competition and reduces the strength of interspecific competition. This may prolong coexistence and enhance local and regional diversity. Speciation is present in some neutral models and gives a donor-controlled input of new species, many of which remain rare or are short lived, but which directly add to species diversity. Spatial scale is an important consideration in neutral models. Ecological equivalence and equal fitness have implicit spatial scales because dispersal limitation and its emergent effects operate at population levels, and populations and communities are defined at a chosen spatial scale in recent neutral models; equality is measured relative to a metacommunity, and this necessitates defining the spatial scale of that metacommunity. Furthermore, dispersal has its own scales. Thorough empirical tests of neutral models will require both tests of mechanisms and pattern-producing ability, and will involve coupling theoretical models and experiments.     

镉污染胁迫下水稻生理生态表征高光谱识别模型

利用水稻生长季节多个时相的ASD实测高光谱数据和同步获取的土壤-作物特征参数,系统分析了高光谱遥感指数与镉污染胁迫下水稻生理生态参数变化特征之间的响应关系.选择叶绿素含量、水分含量、细胞结构和叶面积指数4个重要生理生态参数作为水稻镉污染胁迫响应因子,通过理论分析和实验验证,得到对上述因子变化敏感的高光谱遥感指数及其响应规律.在此基础上建立MCARI-NDWI、MCARI-RVSI、MCARI-RVI、NDWI-RVSI、NDWI-RVI和RVSI-RVI模型,揭示了不同污染程度条件下响应因子在各光谱指数空间中的表征规律.     

A strong test of a maximum entropy model of trait-based community assembly

We evaluate the predictive power and generality of Shipley's maximum entropy (maxent) model of community assembly in the context of 96 quadrats over a 120-km2 area having a large (79) species pool and strong gradients. Quadrats were sampled in the herbaceous understory of ponderosa pine forests in the Coconino National Forest, Arizona, U.S.A. The maxent model accurately predicted species relative abundances when observed community-weighted mean trait values were used as model constraints. Although only 53% of the variation in observed relative abundances was associated with a combination of 12 environmental variables, the maxent model based only on the environmental variables provided highly significant predictive ability, accounting for 72% of the variation that was possible given these environmental variables. This predictive ability largely surpassed that of nonmetric multidimensional scaling (NMDS) or detrended correspondence analysis (DCA) ordinations. Using cross-validation with 1000 independent runs, the median correlation between observed and predicted relative abundances was 0.560 (the 2.5% and 97.5% quantiles were 0.045 and 0.825). The qualitative predictions of the model were also noteworthy: dominant species were correctly identified in 53% of the quadrats, 83% of rare species were correctly predicted to have a relative abundance of < 0.05, and the median predicted relative abundance of species actually absent from a quadrat was 5 x 10(-5).     

The role of agent-based models in wildlife ecology and management

Conservation planning of critical habitats for wildlife species at risk is a priority topic that requires the knowledge of how animals select and use their habitat, and how they respond to future developmental changes in their environment. This paper explores the role of a habitat-modeling methodological approach, agent-based modeling, which we advocate as a promising approach for ecological research. Agent-based models (ABMs) are capable of simultaneously distinguishing animal densities from habitat quality, can explicitly represent the environment and its dynamism, can accommodate spatial patterns of inter- and intra-species mechanisms, and can explore feedbacks and adaptations inherent in these systems. ABMs comprise autonomous, individual entities; each with dynamic, adaptive behaviors and heterogeneous characteristics that interact with each other and with their environment. These interactions result in emergent outcomes that can be used to quantitatively examine critical habitats from the individual- to population-level. ABMs can also explore how wildlife will respond to potential changes in environmental conditions, since they can readily incorporate adaptive animal-movement ecology in a changing landscape. This paper describes the necessary elements of an ABM developed specifically for understanding wildlife habitat selection, reviews the current empirical literature on ABMs in wildlife ecology and management, and evaluates the current and future roles these ABMs can play, specifically with regards to scenario planning of designated critical habitats.     

Characterizing species abundance distributions across taxa and ecosystems using a simple maximum entropy model

The species abundance distribution (SAD) is one of themost studied patterns in ecology due to its potential insights into commonness and rarity, community assembly, and patterns of biodiversity. It is well established that communities are composed of a few common and many rare species, and numerous theoretical models have been proposed to explain this pattern. However, no attempt has been made to determine how well these theoretical characterizations capture observed taxonomic and global-scale spatial variation in the general form of the distribution. Here, using data of a scope unprecedented in community ecology, we show that a simple maximum entropy model produces a truncated log-series distribution that can predict between 83% and 93% of the observed variation in the rank abundance of species across 15 848 globally distributed communities including birds, mammals, plants, and butterflies. This model requires knowledge of only the species richness and total abundance of the community to predict the full abundance distribution, which suggests that these factors are sufficient to understand the distribution for most purposes. Since geographic patterns in richness and abundance can often be successfully modeled, this approach should allow the distribution of commonness and rarity to be characterized, even in locations where empirical data are unavailable.     

Comparisons between air-transport models via bootstrapped significance tests

The bootstrap resampling method is used to help with the intercomparison of air transport models. Time-by-time analysis of variance and follow up simultaneous comparisons are proposed which do not rely on distributional assumptions for the predictions of the models and which allow errors to be postulated in the observed values of the phenomena under investigation. The techniques are demonstrated using some data from the APSIS experiment.     

Testing environmental effects on taxonomic composition with canonical correspondence analysis: alternative permutation tests are not equal

Environmental and Ecological Statistics - After applying canonical correspondence analysis to metagenomics data with hugely different library sizes (site totals) it became evident that Canoco and...     

Random forests for classification in ecology

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.     

The application of predicted habitat models to investigate the spatial ecology of demersal fish assemblages

Benthic habitats are known to influence the abundance and richness of demersal fish assemblages; however, little is known about how habitat structure and composition influences these distributions at very fine scales. We examined how the benthic environment structures marine fish assemblages using high-resolution bathymetry and accurate predicted benthic habitat maps. Areas characterised by a mosaic of habitat patches supported the highest richness of demersal fishes. A total of 37.4% of the variation in the distribution of the fish assemblage was attributed to 6 significant variables. Depth explained 23.0% of the variation, with the boulders explaining 12.6% and relief 1.4%. The remaining measures (seawhips, light/exposure and solid reef) provided a small (<1.0%) but significant contribution. Identifying components of the benthic environment important in structuring fish assemblages and understanding how they influence the spatial distribution of marine fishes is imperative for better management of demersal fish populations.     

The foraging ecology of coastal bottlenose dolphins based on stable isotope mixing models and behavioural sampling

Understanding trophic interactions is critical for elucidating ecological roles of marine predators. We used behavioural observations and stable isotope mixing models to investigate the feeding ecology of Indo-Pacific bottlenose dolphins (Tursiops aduncus) in the lagoon of Mayotte (East Africa). We identified prey during 77 % of 54 observed feeding events, observed in both rainy (61 % of events) and dry (39 %) seasons. Caranx melampygus and Gnathanodon speciosus were involved in 67 % of these events, with Tylosurus crocodilus (20 %) and Mugil cephalus (13 %) also consumed. Mixing models, based on δ¹³C and δ¹⁵N values of skin and blubber (n = 30 samples for both tissues), suggest that behavioural observations are representative of general feeding patterns. Indeed, C. melampygus and T. crocodilus (G. speciosus could not be included in models) were estimated to contribute most to dolphin diets, with mean estimated contributions of 44.6 % (±18.9) and 48.1 % (±19.1) for skin and 73.7 % (±14.9) and 16.9 % (±12.4) for blubber, respectively. Our results highlight the value of two independent methods (stable isotopes and behavioural observations) to assess prey preferences of free-ranging dolphins.     

Social network analysis and valid Markov chain Monte Carlo tests of null models

Analyses of animal social networks derived from group-based associations often rely on randomisation methods developed in ecology (Manly, Ecology 76:1109–1115, 1995) and made available to the animal behaviour community through implementation of a pair-wise swapping algorithm by Bejder et al. (Anim Behav 56:719–725, 1998). We report a correctable flaw in this method and point the reader to a wider literature on the subject of null models in the ecology literature. We illustrate the importance of correcting the method using a toy network and use it to make a preliminary analysis of a network of associations among eagle rays.

Bayesian entropy for spatial sampling design of environmental data

We develop a spatial statistical methodology to design national air pollution monitoring networks with good predictive capabilities while minimizing the cost of monitoring. The underlying complexity of atmospheric processes and the urgent need to give credible assessments of environmental risk create problems requiring new statistical methodologies to meet these challenges. In this work, we present a new method of ranking various subnetworks taking both the environmental cost and the statistical information into account. A Bayesian algorithm is introduced to obtain an optimal subnetwork using an entropy framework. The final network and accuracy of the spatial predictions is heavily dependent on the underlying model of spatial correlation. Usually the simplifying assumption of stationarity, in the sense that the spatial dependency structure does not change location, is made for spatial prediction. However, it is not uncommon to find spatial data that show strong signs of nonstationary behavior. We build upon an existing approach that creates a nonstationary covariance by a mixture of a family of stationary processes, and we propose a Bayesian method of estimating the associated parameters using the technique of Reversible Jump Markov Chain Monte Carlo. We apply these methods for spatial prediction and network design to ambient ozone data from a monitoring network in the eastern US.     

A frontier model for landscape ecology: the tapir in Honduras

We borrow a frontier specification from the econometrics literature to make inferences about the tolerance of the tapir to human settlements. We estimate the width of an invisible band surrounding human settlements which would act as a frontier or exclusion zone to the tapir to be around 290 metres.     

A new technique for ordering asymmetrical three-dimensional data sets in ecology

The aim of this paper is to tackle the problem that arises from asymmetrical data cubes formed by two crossed factors fixed by the experimenter (factor A and factor B, e.g., sites and dates) and a factor which is not controlled for (the species). The entries of this cube are densities in species. We approach this kind of data by the comparison of patterns, that is to say by analyzing first the effect of factor B on the species-factor A pattern, and second the effect of factor A on the species-factor B pattern. The analysis of patterns instead of individual responses requires a correspondence analysis. We use a method we call Foucart's correspondence analysis to coordinate the correspondence analyses of several independent matrices of species x factor A (respectively B) type, corresponding to each modality of factor B (respectively A). Such coordination makes it possible to evaluate the effect of factor B (respectively A) on the species-factor A (respectively B) pattern. The results obtained by such a procedure are much more insightful than those resulting from a classical single correspondence analysis applied to the global matrix that is obtained by simply unrolling the data cube, juxtaposing for example the individual species x factor A matrices through modalities of factor B. This is because a single global correspondence analysis combines three effects of factors in a way that cannot be determined from factorial maps (factor A, factor B, and factor A x factor B interaction) whereas the applications of Foucart's correspondence analysis clearly discriminate two different issues. Using two data sets, we illustrate that this technique proves to be particularly powerful in the analyses of ecological convergence which include several distinct data sets and in the analyses of spatiotemporal variations of species distributions.     

The value of virtual conferencing for ecology and conservation

The objectives of conservation science and dissemination of its research create a paradox: Conservation is about preserving the environment, yet scientists spread this message at conferences with heavy carbon footprints. Ecology and conservation science depend on global knowledge exchange—getting the best science to the places it is most needed. However, conference attendance from developed countries typically outweighs that from developing countries that are biodiversity and conservation hotspots. If any branch of science should be trying to maximize participation while minimizing carbon emissions, it is conservation. Virtual conferencing is common in other disciplines, such as education and humanities, but it is surprisingly underused in ecology and conservation. Adopting virtual conferencing entails a number of challenges, including logistics and unified acceptance, which we argue can be overcome through planning and technology. We examined 4 conference models: a pure‐virtual model and 3 hybrid hub‐and‐node models, where hubs stream content to local nodes. These models collectively aim to mitigate the logistical and administrative challenges of global knowledge transfer. Embracing virtual conferencing addresses 2 essential prerequisites of modern conferences: lowering carbon emissions and increasing accessibility for remote, time‐ and resource‐poor researchers, particularly those from developing countries.