Modeling Collective Animal Movement Through Interactions in Behavioral States

Animal movement often exhibits changing behavior because animals often alternate between exploring, resting, feeding, or other potential states. Changes in these behavioral states are often driven by environmental conditions or the behavior of nearby individuals. We propose a model for dependence among individuals’ behavioral states. We couple this state switching with complex discrete-time animal movement models to analyze a large variety of animal movement types. To demonstrate this method of capturing dependence, we study the movements of ants in a nest. The behavioral interaction structure is combined with a spatially varying stochastic differential equation model to allow for spatially and temporally heterogeneous collective movement of all ants within the nest. Our results reveal behavioral tendencies that are related to nearby individuals, particularly the queen, and to different locations in the nest.     

Bayesian Methods for Hierarchical Distance Sampling Models

The few distance sampling studies that use Bayesian methods typically consider only line transect sampling with a half-normal detection function. We present a Bayesian approach to analyse distance sampling data applicable to line and point transects, exact and interval distance data and any detection function possibly including covariates affecting detection probabilities. We use an integrated likelihood which combines the detection and density models. For the latter, densities are related to covariates in a log-linear mixed effect Poisson model which accommodates correlated counts. We use a Metropolis-Hastings algorithm for updating parameters and a reversible jump algorithm to include model selection for both the detection function and density models. The approach is applied to a large-scale experimental design study of northern bobwhite coveys where the interest was to assess the effect of establishing herbaceous buffers around agricultural fields in several states in the US on bird densities. Results were compared with those from an existing maximum likelihood approach that analyses the detection and density models in two stages. Both methods revealed an increase of covey densities on buffered fields. Our approach gave estimates with higher precision even though it does not condition on a known detection function for the density model.     

Mark-recapture with occasion and individual effects: Abundance estimation through Bayesian model selection in a fixed dimensional parameter space

We present a Bayesian mark-recapture method for explicitly communicating uncertainty about the size of a closed population where capture probabilities vary across both individuals and sampling occasions. Heterogeneity is modeled hierarchically using a continuous logistic-Normal model to specify the capture probabilities for both individuals that are captured on at least one occasion and individuals that are never captured and so remain undetected. Inference about how many undetected individuals to include in the model is accomplished through a Bayesian model selection procedure using MCMC, applied to a product space of possible models for different numbers of undetected individuals. Setting the estimation problem in a fixed dimensional parameter space enables the model selection procedure to be performed using the freely available WinBUGS software. The outcome of inference is a full “posterior” probability distribution for the population size parameter. We demonstrate this method through an example involving real mark-recapture data.     

面向控制的温室系统小气候环境模型要求与现状

以往的温室作物生长和小气候环境模型,主要是从面向研究而不是面向实际生产的温室获得的,这二者的最大不同是:面向研究的模型主要考虑的是得到作物生长高产所需的"最优"的温室内部气候环境参数设定值,而较少考虑温室内控制设备的能力（控制动态过程）、生产过程中温室外气候变化情况和达到"最优"所需付出的能量等代价;而后者在面向实际生产的自动化控制的温室系统模型中是必不可少的。当前温室系统自动化控制面临的一个最大困难,就是缺乏一个这样的可靠的温室系统模型,而只能采用面向研究的温室系统模型去进行实际生产的温室系统控制,这种忽视实际生产条件下的温室系统模型与理想条件下的模型之间差异的"纸上谈兵"的做法,必然导致温室控制技术水平低、达不到预期效果。该文介绍了温室系统的整个控制过程,对一个实际生产的温室系统中各种变量和参数作了简要描述,并概括了面向实际的温室生产控制要求的温室系统模型的基本结构,对温室环境模型、作物生长模型和能耗及CO₂消耗模型的研究现状作了详细的回顾。从满足控制需求出发对现有的温室系统模型所存在的问题进行了分析,并指出了其中的不足和局限性。探讨了未来温室系统的建模方法和需要解决的关键问题,提出了面向控制需求的温室系统建模要满足的要求,为温室系统的建模研究提供了一种新的思路和方向。     

Estimating Uncertainty in the Revised Universal Soil Loss Equation via Bayesian Melding

Deterministic simulation models are used to understand environmental processes and guide policy development by decision makers. In order to make informed decisions, uncertainty about input and output of these models needs to be incorporated into the modeling. We use a method known as Bayesian melding to quantify the uncertainty in the Revised Universal Soil Loss Equation (RUSLE), an important component of water quality models. This technique allows for this uncertainty through prior distributions on both the input parameters and the outcomes of interest. There have been relatively few applications of this methodology to complex problems and none to date in soil loss modelling. Moreover, land based spatial data, which are now commonly available in environmental research as well as many other disciplines, have not previously been used to inform Bayesian melding. The results demonstrate that the slope steepness factor of the RUSLE is the main contributor to total uncertainty. We conclude that Bayesian melding provides a good method for exploring the sources of uncertainty in a deterministic model.     

Bayesian capture-recapture analysis and model selection allowing for heterogeneity and behavioral effects

In this article, we present Bayesian analysis of capture-recapture models for a closed population which allows for heterogeneity of capture probabilities between animals and bait/trap effects. We use a flexible discrete mixture model to account for the heterogeneity and behavioral effects. In addition we present a solid model selection criterion. Through illustrations with a motivating dataset, we demonstrate how Bayesian analysis can be applied in this setting and discuss some major benefits which result, including consideration of informative priors based on historical data.     

Ecological Prediction With Nonlinear Multivariate Time-Frequency Functional Data Models

Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.     

Soil and climate are better than biotic land cover for predicting home-range habitat selection by endangered burrowing owls across the Canadian Prairies

Statistical models that describe species-environmental relationships are important components within many wildlife conservation strategies. These models are typically developed from studies conducted on small geographic scales (hundreds of square kilometres), representing a relatively small range in environmental conditions. Such local models from local studies are often then extrapolated to predict the suitability of other unsampled regions. The value of many models would be increased by considering larger-scale processes that might be structuring spatial patterns across species distributions. We examined home-range habitat selection by burrowing owls throughout the mixed prairie grassland region of western Canada (180,000 km²) to determine whether owl selection for biotic factors changes along abiotic gradients. Specifically, we classified 37 explanatory variables into five categories (geography, grassland fragmentation, land-use, soil, and climate), created models for each set of variables, and evaluated the predictive ability of each model. We then examined interaction effects to determine if the relationship between land cover variables and the probability of owl home-range selection varied within large-scale abiotic criteria. Our results showed that soil and climate produce the most predictive models of burrowing owl home-range selection and create unique environmental conditions for owls which are independent of land cover at this scale. This study provides new insight into burrowing owl habitat requirements, and strengthens the case for considering large-scale abiotic gradients when prioritizing areas for species conservation.     

One- and two-objective approaches to an area-constrained habitat reserve site selection problem

We compare several ways to model a habitat reserve site selection problem in which an upper bound on the total area of the selected sites is included. The models are cast as optimization coverage models drawn from the location science literature. Classic covering problems typically include a constraint on the number of sites that can be selected. If potential reserve sites vary in terms of area, acquisition cost or land value, then sites need to be differentiated by these characteristics in the selection process. To address this within the optimization model, the constraint on the number of selected sites can either be replaced by one limiting the total area of the selected sites or area minimization can be incorporated as a second objective. We show that for our dataset and choice of optimization solver average solution time improves considerably when an area-constrained reserve site selection problem is modeled as a two objective rather than a single objective problem with a constraint limiting the total area of the selected sites. Computational experience is reported using a large dataset from Australia.     

Saiga antelope calving site selection is increasingly driven by human disturbance

Many terrestrial mammalian species aggregate to give birth. Such aggregations are likely to be a response to changing resource and water availability, for predator swamping and avoidance of disturbance. The critically endangered saiga antelope (Saiga tatarica) is one such species. We analysed spatio-temporal locations of saiga calving aggregations in Kazakhstan over the last four decades obtained from aerial and ground surveys, to identify the factors determining the selection of calving sites within the species’ range as well as any changes in these locations over time. Generalized mixed models were employed in a use - availability framework to assess the factors distinguishing calving from random sites and predict suitable areas for calving. Saigas selected sites, with lower than average productivity and low year to year variability in productivity, at an intermediate distance from water sources, and away from human settlements. A significant change in calving locations was observed during the last decade, with calving areas occurring further north and further away from settlements than previously. The results demonstrate that the choice of calving areas is largely driven by environmental factors. However, disturbance also has a significant impact on calving site selection and in recent decades, its influence overrides that of environmental factors. This increase in the influence of disturbance coincides with a precipitous decline in saiga numbers due to poaching, as well as substantial reductions in the intensity of land use for livestock grazing following the breakup of the Soviet Union. Predictive models based on such studies can improve species conservation by guiding the stratification of sampling for effective monitoring and deployment of rangers to protect the females at this critical time.     

A rapid approach to modeling species-habitat relationships

A basic element in the success of managing species of conservation concern is knowledge of the species’ habitat occupancy. Often, predictive species-habitat models are developed from GIS data sources that were intended for purposes other than predicting species habitat occupancy and are of inappropriate scale. In addition, the techniques used to quantify predictor variables from such data sources are often time consuming and cannot be repeated efficiently to reflect changing conditions. We used digital orthophotos and a grid cell classification scheme to develop an efficient technique to quantify predictor variables to model Florida scrub-jay habitat occupancy. We combined our classification scheme with a priori hypothesis development using expert knowledge and a previously published habitat suitability index and used an objective model selection procedure to choose candidate models. We classified a large area (43,000 ha) in a fraction of the time that would be required to map vegetation classes and were able to test models at varying scales using a grid-cell windowing process. Interpretation of the selected models confirmed existing knowledge of factors important to Florida scrub-jay habitat occupancy. The potential uses and advantages of using a grid cell classification scheme in conjunction with expert knowledge and an objective model selection procedure are discussed.     

Computationally Efficient Statistical Differential Equation Modeling Using Homogenization

Statistical models using partial differential equations (PDEs) to describe dynamically evolving natural systems are appearing in the scientific literature with some regularity in recent years. Often such studies seek to characterize the dynamics of temporal or spatio-temporal phenomena such as invasive species, consumer-resource interactions, community evolution, and resource selection. Specifically, in the spatial setting, data are often available at varying spatial and temporal scales. Additionally, the necessary numerical integration of a PDE may be computationally infeasible over the spatial support of interest. We present an approach to impose computationally advantageous changes of support in statistical implementations of PDE models and demonstrate its utility through simulation using a form of PDE known as “ecological diffusion.” We also apply a statistical ecological diffusion model to a data set involving the spread of mountain pine beetle (Dendroctonus ponderosae) in Idaho, USA. This article has supplementary material online.     

The practical use of semiparametric models in field trials

This article examines the practical use of semiparametric models in the analysis of field trials—that is, models with parameterized treatment effects and additive terms derived by a data-driven approach using a locally weighted running line smoother (loess). We discuss graphical methods to identify spatial structure in the data and model selection procedures to choose the degree of smoothing. Once the spatial part of the model has been chosen, hypotheses about the treatment effects may be tested. Semiparametric models are used to analyze two barley field trials exhibiting spatial trends. The first has a single experimental treatment and a row-column design. The second has a split-plot design, and we use a semiparametric model which accounts for the randomization at the different strata of this design. We compare the semiparametric analyses with classical analyses of variance and with alternative spatial models. We find that semiparametric models give a good insight into spatial variation in the field and can improve the precision of parameter estimates.     

Complementarity-based conservation prioritization using a community classification, and its application to riverine ecosystems

We demonstrate a novel method for spatial conservation prioritization at a community-level that takes account of: (i) an environmentally-based classification of the landscape into community classes; (ii) similarities between community classes to allow for community complementarity-based selection; (iii) variation in species richness; (iv) variation in human impacts on ecological integrity; and (v) requirements for maintenance of upstream-downstream connectivity in riverine systems. While this technique has generic application, we demonstrate its application using a biologically-trained environmental classification of New Zealand’s river network. Our analysis produces a priority ranking of planning units (here 4th order catchments or sub-catchments) and performance estimates in terms of expected biodiversity returns given varying degrees of geographic protection. Accounting for community similarity ensures high protection for distinct habitat classes with low similarity to other classes; our results indicate a 28% loss in conservation efficiency of the highest-ranked 10% of the landscape if it is ignored. Accounting for human pressures and connectivity also had clear influences on spatial priority rankings, indicating the need to consider these factors in the conservation planning process.     

Habitat selection by the common wombat (Vombatus ursinus) in disturbed environments: Implications for the conservation of a ‘common’ species

The construction of habitat models is a repeatable technique for describing and mapping species distributions, the utility of which lies in enabling management to predict where a species is likely to occur within a landscape. Typically, habitat models have been used to establish habitat requirements for threatened species; however they have equal applicability for modelling local populations of common species. Often, few data exist on local populations of common species, and issues of abundance and habitat selection at varying scales are rarely addressed. We provide a habitat suitability model for the common wombat (Vombatus ursinus) in southern New South Wales. This species is currently perceived as abundant throughout its extensive range across temperate regions of eastern Australia, yet little factual survey data exist and populations appear under threat. We use wombat burrows to reflect habitat selection and as our basis for ecological modelling. We found that environmental variables representing proximity to cover, measures of vegetation and proximity to watercourses are important predictors of burrow presence. Extrapolation of habitat models identified an abundance of habitat suitable for burrows. However, burrows in many suitable areas were abandoned. Our estimate of the population size was similar to the total annual mortality associated with road-kill. Theoretically, given the availability of suitable habitat, common wombat populations in the region should be thriving. It seems likely that this area once supported a much higher number of wombats; however limiting factors such as road mortality and disease have reduced the populations. The persistence of wombats in the study region must be supported by migration from other populations. Our findings challenge the perception that wombats are currently common and not in need of monitoring, suggesting that perceptions of abundance are often clouded by socio-political motives rather than informed by biological and ecological factors.     

Dynamic multistate site occupancy models to evaluate hypotheses relevant to conservation of Golden Eagles in Denali National Park, Alaska

The recent development of multistate site occupancy models offers great opportunities to frame and solve decision problems for conservation that can be viewed in terms of site occupancy. These models have several characteristics (e.g., they account for detectability) that make them particularly well suited for addressing management and conservation problems. We applied multistate site occupancy models to evaluate hypotheses related to the conservation and management of Golden Eagles (Aquila chrysaetos) in Denali National Park, Alaska, and provided estimates of transition probabilities among three occupancy states for nesting areas (occupied with successful reproduction, occupied with unsuccessful reproduction, and unoccupied). Our estimation models included the effect of potential recreational activities (hikers) and environmental covariates such as a snowshoe hare (Lepus americanus) index on transition probabilities among the three occupancy states. Based on the most parsimonious model, support for the hypothesis of an effect of potential human disturbance on site occupancy dynamics was equivocal. There was some evidence that potential human disturbance negatively affected local colonization of territories, but there was no evidence of an effect on reproductive performance parameters. In addition, models that assume a positive relationship between the hare index and successful reproduction were well supported by the data. The statistical approach that we used is particularly useful to parameterize management models that can then be used to make optimal decisions related to the management of Golden Eagles in Denali. Although in our case we were particularly interested in managing recreational activities, we believe that such models should be useful to for a broad class of management and conservation problems.     

Assessing the Impacts of Time-to-Detection Distribution Assumptions on Detection Probability Estimation

Abundance estimates from animal point-count surveys require accurate estimates of detection probabilities. The standard model for estimating detection from removal-sampled point-count surveys assumes that organisms at a survey site are detected at a constant rate; however, this assumption can often lead to biased estimates. We consider a class of N-mixture models that allows for detection heterogeneity over time through a flexibly defined time-to-detection distribution (TTDD) and allows for fixed and random effects for both abundance and detection. Our model is thus a combination of survival time-to-event analysis with unknown-N, unknown-p abundance estimation. We specifically explore two-parameter families of TTDDs, e.g., gamma, that can additionally include a mixture component to model increased probability of detection in the initial observation period. Based on simulation analyses, we find that modeling a TTDD by using a two-parameter family is necessary when data have a chance of arising from a distribution of this nature. In addition, models with a mixture component can outperform non-mixture models even when the truth is non-mixture. Finally, we analyze an Ovenbird data set from the Chippewa National Forest using mixed effect models for both abundance and detection. We demonstrate that the effects of explanatory variables on abundance and detection are consistent across mixture TTDDs but that flexible TTDDs result in lower estimated probabilities of detection and therefore higher estimates of abundance.Supplementary materials accompanying this paper appear on-line.     

Selection of Spatial-Temporal Lattice Models: Assessing the Impact of Climate Conditions on a Mountain Pine Beetle Outbreak

Insects are among the most significant indicators of a changing climate. Here we evaluate the impact of temperature, precipitation, and elevation on the tree-killing ability of an eruptive species of bark beetle in pine forests of British Columbia, Canada. We consider a spatial-temporal linear regression model and in particular, a new statistical method that simultaneously performs model selection and parameter estimation. This approach is penalized maximum likelihood estimation under a spatial-temporal adaptive Lasso penalty, paired with a computationally efficient algorithm to obtain approximate penalized maximum likelihood estimates. A simulation study shows that finite-sample properties of these estimates are sound. In a case study, we apply this approach to identify the appropriate components of a general class of landscape models which features the factors that propagate an outbreak. We interpret the results from ecological perspectives and compare our method with alternative model selection procedures.     

拖拉机自动转向最优控制方法的研究

该文讨论了拖拉机自动驾驶(自动转向)控制点的选择，并且在此基础上建立了拖拉机跟踪直线行驶时的运动学与动力学模型，分别对基于这两种模型的拖拉机自动驾驶最优控制方法进行了研究，分析了参数选择对控制方法的影响。由于拖拉机是一个具有大延迟、高度非线性的复杂系统，为了减少试验确定参数时的试凑次数，提高其最优控制参数的整定精度，提出一种融合运动学与动力学模型各自特点的控制参数的确定方法，对拖拉机自动驾驶试验时控制参数的整定具有指导意义。结果表明，所提出的方法用于拖拉机自动驾驶是可行的。     

A habitat-based framework for grizzly bear conservation in Alberta

Grizzly bear (Ursus arctos L.) populations in Alberta are threatened by habitat loss and high rates of human-caused mortality. Spatial depictions of fitness would greatly improve management and conservation action. We are currently challenged, however, in our ability to parameterize demographic rates necessary for describing fitness, especially across gradients of human disturbance and for land cover types. Alternative approaches are therefore needed. We describe here a method of estimating relative habitat states and conditions as surrogates of fitness using models of occupancy and mortality risk. By combining occurrence and risk models into a two-dimensional habitat framework, we identified indices of attractive sinks and safe harbour habitats, as well as five habitat states: non-critical habitats, secondary habitats (low-quality and secure), primary habitats (high-quality and secure), secondary sinks (low-quality, but high risk), and primary sinks (high-quality and high risk). Primary sink or high attractive sink situations were evident in the foothills where bears were using forest edges associated with forestry and oil and gas activities on Crown lands, while primary habitats or safe harbour sites were most common to protected alpine/sub-alpine sites. We suggest that habitat states and indices be used for setting baseline conditions for management and comparison of habitat conditions over time and identification of grizzly bear conservation reserves. A no net loss policy of critical habitats could be used to maintain existing habitat conditions for landscapes threatened by human development. Under such a policy, conversions of primary habitat would require restoration of equivalent amounts of primary sinks through decommissioning of roads.