Estimating the per‐capita contribution of habitats and pathways in a migratory network: a modelling approach

Every year, migratory species undertake seasonal movements along different pathways between discrete regions and habitats. The ability to assess the relative demographic contributions of these different habitats and pathways to the species’ overall population dynamics is critical for understanding the ecology of migratory species, and also has practical applications for management and conservation. Metrics for assessing habitat contributions have been well‐developed for metapopulations, but an equivalent metric is not currently available for migratory populations. Here, we develop a framework for estimating the demographic contributions of the discrete habitats and pathways used by migratory species throughout the annual cycle by estimating the per capita contribution of cohorts using these locations. Our framework accounts for seasonal movements between multiple breeding and non‐breeding habitats and for both resident and migratory cohorts. We illustrate our framework using a hypothetical migratory network of four habitats, which allows us to better understand how variations in habitat quality affect per capita contributions. Results indicate that per capita contributions for any habitat or pathway are dependent on habitat‐specific survival probabilities in all other areas used as part of the migratory circuit, and that contribution metrics are spatially linked (e.g. reduced survival in one habitat also decreases the contribution metric for other habitats). Our framework expands existing theory on the dynamics of spatiotemporally structured populations by developing a generalized approach to estimate the habitat‐ and pathway‐specific contributions of species migrating between multiple breeding and multiple non‐breeding habitats for a range of life histories or migratory strategies. Most importantly, it provides a means of prioritizing conservation efforts towards those migratory pathways and habitats that are most critical for the population viability of migratory species.     

Assessing quality of hybridized RNA in Affymetrix GeneChip experiments using mixed-effects models

The technology for hybridizing archived tissue specimens and the use of laser-capture microdissection for selecting cell populations for RNA extraction have increased over the past few years. Both these methods contribute to RNA degradation. Therefore, quality assessments of RNA hybridized to microarrays are becoming increasingly more important. Existing methods for estimating the quality of RNA hybridized to a GeneChip, from resulting microarray data, suffer from subjectivity and lack of estimates of variability. In this article, a method for assessing RNA quality for a hybridized array which overcomes these drawbacks is proposed. The effectiveness of the proposed method is demonstrated by the application of the method to two microarray data sets for which external verification of RNA quality is known.     

Energy cost of ontogenesis as a marker of the quality of the habitat (Tests on <Emphasis Type="Italic">Daphnia magna</Emphasis> and <Emphasis Type="Italic">Carassius auratus</Emphasis>)

The application of the method of energy cost of ontogenesis for characterizing the quality of the habitat is considered. A quantitative method for assessing the adverse effect of the environment on the body is proposed. Examples of practical use of the method in estimating the ecological consequences of environmental pollution are given.     

Identification of suitable unoccupied habitats in metapopulation studies using co-occurrence of species

This study proposed a new quantitative technique to identify suitable but unoccupied habitats for metapopulation studies in plants. It is based on species composition at the habitat and knowledge of species co-occurrence patterns. It uses data from a large phytosociological database as a background for estimating species co-occurrence patterns. If such a database is not available, the technique can still be applied using the same data for which the prediction is done to estimate the species co-occurrence pattern. Using the technique we were able to indicate suitable unoccupied habitats and differentiate them from the unoccupied unsuitable ones. We also identified habitats with low probability of being suitable that were occupied. Compared to a direct approach of identification of suitable habitats, which involves introduction of a species to the habitat and studying its performance, the approach presented here is much easier to apply and can provide extensive information on habitat suitability for a range of species with much less effort and time needed.     

Generalized Linear Mixed-Effects Models with a Finite-Support Random-Effects Distribution: A Maximum-Penalized-Likelihood Approach

We discuss a method for simultaneously estimating the fixed parameters of a generalized linear mixed-effects model and the random-effects distribution of which no parametric assumption is made. In addition, classifying subjects into clusters according to the random regression coefficients is a natural by-product of the proposed method. An alternative approach to maximum-likelihood method, maximum-penalized-likelihood method, is used to avoid estimating “too many” clusters. Consistency and asymptotic normality properties of the estimators are presented. We also provide robust variance estimators of the fixed parameters estimators which remain consistent even in presence of misspecification. The methodology is illustrated by an application to a weight loss study.     

Molecular and spatial analyses reveal links between colony‐specific foraging distance and landscape‐level resource availability in two bumblebee species

Foraging distance is a key determinant of colony survival and pollination potential in bumblebees Bombus spp. However this aspect of bumblebee ecology is poorly understood because of the difficulty in locating colonies of these central place foragers. Here, we used a combination of molecular microsatellite analyses, remote sensing and spatial analyses using kernel density estimates to estimate nest location and foraging distances for a large number of wild colonies of two species, and related these to the distribution of foraging habitats across an experimentally manipulated landscape. Mean foraging distances were 755 m for Bombus lapidarius and 775 m for B. pascuorum (using our most conservative estimation method). Colony‐specific foraging distances of both species varied with landscape structure, decreasing as the proportion of foraging habitats increased. This is the first time that foraging distance in wild bumblebees has been shown to vary with resource availability. Our method offers a means of estimating foraging distances in social insects, and informs the scale of management required to conserve bumblebee populations and enhance their pollination services across different landscapes.     

The validity of burrow counting in estimating the densities of Chiromantes dehaani and Ilyoplax deschampsi in tidal flat at Yangtze Estuary

It has been confirmed that the crabs play significant roles in the structure and function of coastal wetland ecosystems, such as mangroves and salt marshes. However it is not easy to estimate the abundance and density of burrowing crabs effectively, thus a further understanding of roles of crabs in these ecosystems has been lagged. Some studies have discussed the suitability of several census techniques, such as burrow counting method in estimating crab density in mangroves. The validities of burrow counting method and other census techniques in estimating crab density, however, has not been tested in salt marshes, especially where vegetation are dense. In this study, we tested the validity of burrow counting method in estimating the densities of Chiromantes dehaani and Ilyoplax deschampsi in tidal flat with dense vegetation of Phragmites australis and Zizania aquatica at Yangtze Estuary through comparing densities estimated by the burrow counting method and the excavation. Burrow counting averagely underestimated the density of C. dehaani by 15% and the degree of underestimation varied among vegetations and habitats (from an overestimate by 23% to underestimate by 41%). Burrow counting averagely overestimated the density of I. deschampsi by 43% and the degree of overestimate varied from 0% to 133% depending on the vegetations and habitats. The percentage of occupied burrows and the number of crabs sharing one burrow were important factors influencing the validity of estimating crab density through burrow counting method.     

The validity of burrow counting in estimating the densities of Chiromantes dehaani and Ilyoplax deschampsi in tidal flat at Yangtze Estuary

It has been confirmed that the crabs play significant roles in the structure and function of coastal wetland ecosystems, such as mangroves and salt marshes. However it is not easy to estimate the abundance and density of burrowing crabs effectively, thus a further understanding of roles of crabs in these ecosystems has been lagged. Some studies have discussed the suitability of several census techniques, such as burrow counting method in estimating crab density in mangroves. The validities of burrow counting method and other census techniques in estimating crab density, however, has not been tested in salt marshes, especially where vegetation are dense. In this study, we tested the validity of burrow counting method in estimating the densities of Chiromantes dehaani and Ilyoplax deschampsi in tidal flat with dense vegetation of Phragmites australis and Zizania aquatica at Yangtze Estuary through comparing densities estimated by the burrow counting method and the excavation. Burrow counting averagely underestimated the density of C. dehaani by 15% and the degree of underestimation varied among vegetations and habitats (from an overestimate by 23% to underestimate by 41%). Burrow counting averagely overestimated the density of I. deschampsi by 43% and the degree of overestimate varied from 0% to 133% depending on the vegetations and habitats. The percentage of occupied burrows and the number of crabs sharing one burrow were important factors influencing the validity of estimating crab density through burrow counting method.     

Ecological traps and species distribution models: a challenge for prioritizing areas of conservation importance

Species distribution models analyse how species use different types of habitats. Their spatial predictions are often used to prioritize areas for conservation. Individuals may, however, prefer settling in habitat types of low quality compared to other available habitats. This ecological trap phenomenon is usually studied in a small number of habitat patches and consequences at the landscape level are largely unknown. It is therefore often unclear whether the spatial pattern of habitat use is aligned with the behavioural decisions made by the individuals during habitat selection or reflects actual variation in the quality of different habitat types. As species distribution models analyse the pattern of occurrence in different habitats, there is a conservation interest in examining what their predictions mean in terms of habitat quality when ecological traps are operating. Previous work in Belgium showed that red-backed shrikes Lanius collurio are more attracted to newly available clear-cut habitat in plantation forests than to the traditionally used farmland habitat. We developed models with shrike distribution data and compared their predictions with spatial variation in shrike reproductive performance used as a proxy for habitat quality. Models accurately predicted shrike distribution and identified the preferred clear-cut patches as the most frequently used habitat, but reproductive performance was lower in clear-cut areas than in farmland. With human-induced rapid environmental changes, organisms may indeed be attracted to low-quality habitats and occupy them at high densities. Consequently, the predictions of statistical models based on occurrence records may not align with variation in significant population parameters for the maintenance of the species. When species expand their range to novel habitats, such models are useful to document the spatial distribution of the organisms, but data on population growth rates are worth collecting before using model predictions to guide the spatial prioritization of conservation actions.     

质量—数量性状的遗传参数估计Ⅲ：利用杂种F2或回交世代

采用混合分布模型和极大似然法,提出了杂种早期世代Ｆ_２与回交群体中两个位点之 基因控制的质量－数量性状的遗传分析方法,据此可以进行主基因及其作用方式的测验、主基因 和微基因效应的估计等．探讨了利用回交群体进行质量－数量性状遗传分析与检测的适用范围和 有效范围等．     

Sparse Sampling and Maximum Likelihood Estimation for Boolean Models

A condition for practical independence of contact distribution functions in Boolean models is obtained. This result allows the authors to use maximum likelihcod methods, via sparse sampling, for estimating unknown parameters of an isotropic Boolean model. The second part of this paper is devoted to a simulation study of the proposed method. AMS classification: 60D05     

Importance of estimating matrix quality for modeling species distribution in complex tropical landscapes: a test with Atlantic forest small mammals

Information to guide decision making is especially urgent in human dominated landscapes in the tropics, where urban and agricultural frontiers are still expanding in an unplanned manner. Nevertheless, most studies that have investigated the influence of landscape structure on species distribution have not considered the heterogeneity of altered habitats of the matrix, which is usually high in human dominated landscapes. Using the distribution of small mammals in forest remnants and in the four main altered habitats in an Atlantic forest landscape, we investigated 1) how explanatory power of models describing species distribution in forest remnants varies between landscape structure variables that do or do not incorporate matrix quality and 2) the importance of spatial scale for analyzing the influence of landscape structure. We used standardized sampling in remnants and altered habitats to generate two indices of habitat quality, corresponding to the abundance and to the occurrence of small mammals. For each remnant, we calculated habitat quantity and connectivity in different spatial scales, considering or not the quality of surrounding habitats. The incorporation of matrix quality increased model explanatory power across all spatial scales for half the species that occurred in the matrix, but only when taking into account the distance between habitat patches (connectivity). These connectivity models were also less affected by spatial scale than habitat quantity models. The few consistent responses to the variation in spatial scales indicate that despite their small size, small mammals perceive landscape features at large spatial scales. Matrix quality index corresponding to species occurrence presented a better or similar performance compared to that of species abundance. Results indicate the importance of the matrix for the dynamics of fragmented landscapes and suggest that relatively simple indices can improve our understanding of species distribution, and could be applied in modeling, monitoring and managing complex tropical landscapes.     

Linking Resource Matching and Dispersal

We study theoretically the effect of inter-habitat migration on the distribution of population sizes between two habitats, and compare this distribution with the expected ideal free distribution (IFD). Whenever emigration from the two habitats is asymmetric, or when there is a survival cost during migration, the resulting equilibrium distribution of population sizes deviates from the IFD. This result holds irrespective of emigration rule, even though a density-dependent fraction of emigrants generally produces a distribution closer to the IFD than a constant fraction of emigrants. Environmental stochasticity causes a linear relation between population sizes in the two habitats, with slope and intercept only identical to the IFD when net inter-habitat exchange is zero. The type and asymmetry of inter-habitat migration will influence how we should interpret data on population distribution in different habitats. The resulting resource matching is also critically contingent on the relative time-scales of population renewal and dispersal, and when population size is measured in relation to reproduction and dispersal. Therefore, data on population sizes cannot be used uncritically to assess habitat quality.     

Someplace like home: Experience,habitat selection and conservation biology

Recent insights from habitat selection theory may help conservation managers encourage released animals to settle in appropriate habitats. By all measures, success rates for captive–release and translocation programs are low, and have shown few signs of improvement in recent years. We consider situations in which free-living dispersers prefer new habitats that contain stimuli comparable to those in their natal habitat, a phenomenon called natal habitat preference induction (NHPI). Theory predicts NHPI when dispersers experienced favorable conditions in their natal habitat, and have difficulty estimating the quality of unfamiliar habitats. NHPI is especially likely to occur when performance in a given habitat is enhanced if an animal developed in that same habitat type. Animals exhibiting NHPI are expected to rely on conspicuous cues that can be quickly and easily detected during search, and to prefer new habitats possessing cues that match those encountered in their natal habitat.A major obstacle to successful relocations is that newly released animals often reject the habitat near the release site and rapidly travel long distances away before settling. An NHPI perspective argues that long-distance movements away from release sites occur because releasees prefer to settle in familiar types of habitat, and reject novel areas lacking cues similar to those in their habitat of origin. Similarly, a preference by releasees for familiar cues may encourage them to seek out inappropriate, low quality habitats following release at a new location. We review evidence from a number of studies indicating that problems with habitat selection behavior compromise conservation efforts, and provide recommendations that may encourage animals to “feel more at home” in post-release habitats.     

On the Estimation of the Nearest Neighbour Distribution,G(t), for Point Processes

A new method is proposed for estimating G(t), the distribution function of the distance from an object to its nearest neighbour in a spatial point process. The new method makes more complete use of the information available and has a smaller mean squared error than that of the existing alternatives. The method appears equally effective with random, clustered and regular patterns.     

Greater sage-grouse habitat function relative to 230-kV transmission lines

Greater sage-grouse (Centrocercus urophasianus) is a landscape-level species that requires large tracts of intact sagebrush (Artemisia spp.). Loss of functional habitat resulting from increased demand for energy generation, transmission, and distribution within greater sage-grouse habitats in the western United States has the potential to negatively affect this species. We monitored 346 radio-marked female greater sage-grouse from 2009 to 2014 to evaluate the potential effects of 27-m-tall, 230-kilovolt (kV) wood-pole, H-frame transmission lines on greater sage-grouse habitat selection and demography. We modeled the effect of the transmission lines in 2 different study areas simultaneously using consistent habitat data. Previous research in our study areas suggested that the effect of transmission lines was potentially confounded by other habitat features. We accounted for these potential confounding effects by estimating habitat suitability before estimating the effect of transmission lines. We combined habitat selection and demography results to estimate habitat function relative to transmission lines and inform management recommendations. Overall, we found evidence that transmission lines had a negative effect on greater sage-grouse habitat selection and survival within our study areas over 6 years, but the magnitude of this effect varied by habitat suitability and proximity to occupied leks. The effect of transmission lines on habitat function extended 1.0 km from a transmission line in habitats within 3.1 km of an occupied lek compared to 0.50 km from a transmission line in habitats beyond 3.1 km from occupied leks. Based on these results, we suggest future power line placement relative to sage-grouse nesting, brood-rearing, and summer habitats consider potential effects to sage-grouse habitat selection and demography. Effects can be minimized by incorporating design features that discourage avian predator perching and siting power lines in habitats with lower suitability and, in our study area, habitats beyond 3.1 km from occupied leks. © 2019 The Wildlife Society.     

Conditional-cumulant-of-exposure method in logistic missing covariate regression

We consider estimation in logistic regression where some covariate variables may be missing at random. Satten and Kupper (1993, Journal of the American Statistical Association 88, 200-208) proposed estimating odds ratio parameters using methods based on the probability of exposure. By approximating a partial likelihood, we extend their idea and propose a method that estimates the cumulant-generating function of the missing covariate given observed covariates and surrogates in the controls. Our proposed method first estimates some lower order cumulants of the conditional distribution of the unobserved data and then solves a resulting estimating equation for the logistic regression parameter. A simple version of the proposed method is to replace a missing covariate by the summation of its conditional mean and conditional variance given observed data in the controls. We note that one important property of the proposed method is that, when the validation is only on controls, a class of inverse selection probability weighted semiparametric estimators cannot be applied because selection probabilities on cases are zeroes. The proposed estimator performs well unless the relative risk parameters are large, even though it is technically inconsistent. Small-sample simulations are conducted. We illustrate the method by an example of real data analysis.     

DETERMINING THE LABELING INDEX IN AUTORADIOGRAPHY

A method is proposed for estimating the labeling index in autoradiographs. It is based on a model for the frequency distribution of grain counts for those cells which have few grains. The method does not require extensive grain counting and provides reasonable estimates even when the background level is high. It is particularly suited for experiments where tumor cells, labeled in vivo, are, autoradiographed after being cultured in vitro.     

Comparison of methods used for recovering the line origin of alleles in a cross between outbred lines

Here, we introduce the idea of probabilities of line origins for alleles in general pedigrees as found in crosses between outbred lines. We also present software for calculating these probabilities. The proposed algorithm is based on the linear regression method of Haley, Knott and Elsen (1994) combined with the Markov chain Monte Carlo (MCMC) method for estimating quantitative trait locus coefficients used as regressors. We compared the relative precision of our method and the original method as proposed by Haley et al. (1994). The scenarios studied varied in the allelic distribution of marker alleles in parental lines and in the frequency of missing marker genotypes. We found that the MCMC method achieves a higher accuracy in all scenarios considered. The benefits of using MCMC approximation are substantial if the frequency of missing marker data is high or the number of marker alleles is low and the allelic frequency distribution is similar in both parental lines.     

Estimation of Clustering Parameters Using Gaussian Process Regression

We propose a method for estimating the clustering parameters in a Neyman-Scott Poisson process using Gaussian process regression. It is assumed that the underlying process has been observed within a number of quadrats, and from this sparse information the distribution is modelled as a Gaussian process. The clustering parameters are then estimated numerically by fitting to the covariance structure of the model. It is shown that the proposed method is resilient to any sampling regime. The method is applied to simulated two-dimensional clustered populations and the results are compared to a related method from the literature.