Present-day hunting and distribution of dugongs in the Wellesley Islands (Queensland): Implications for conservation

Dugongs were traditionally hunted by netting or spearing from a raft in the Wellesley Islands (Queensland). They are now hunted using a harpoon with a detachable head (wap) from a dinghy with an outboard motor. Despite the increased efficiency in hunting techniques, other factors (usually sociological) today mitigate against hunting on a large scale and the estimated number of animals killed each year (40) is about a tenth of the maximum numbers counted on aerial survey (374). The peak period of hunting activity takes advantage of the seasonal movements of dugongs within the island group. However, at all times of the year, a significant proportion of dugongs seen from the air occurred in areas where hunting now rarely takes place. Some hunters also avoid the capture of pregnant females as a conservation measure.The importance of initiating a programme to educate the Wellesley Island Aboriginals to take further responsibilities for their wildlife, including dugongs, is discussed.     

Adequacy of approximations to distributions of test statistics in complex mixed linear models

A recent study of lady beetle antennae was a small sample repeated measures design involving a complex covariance structure. Distributions of test statistics based on mixed models fitted to such data are unknown, but two recently developed methods for approximating the distributions of test statistics in mixed linear models have been included as options in the latest release of the MIXED procedure of SAS®. One method (FC, from Fai and Cornelius) computes degrees of freedom of an approximating F distribution for the test statistic using spectral decomposition of the hypothesis matrix together with repeated application of a method for single-degree-of-freedom tests. The other method (KR, from Kenward and Roger) adjusts the estimated covariance matrix of the parameter estimates, computes a scale adjustment to the test statistic, and computes the degrees of freedom of an approximating F distribution. Using the two methods, p values for a hypothesis of interest in the lady beetle study were quite different. Simulation studies on the Proc MIXED implementation of these methods showed that Type I error rates of both methods are affected by covariance structure complexity, sample size, and imbalance. Nonetheless, the KR method performs well in situations with fairly complicated covariance structures when sample sizes are moderate to small and the design is reasonably balanced. The KR method should be used in preference to the FC method, although it had inflated Type I error rates for complex covariance structures combined with small sample sizes.     

Hunting vulnerability, ecological characteristics and harvest rates of bushmeat species in afrotropical forests

We reviewed and analysed patterns and between-site variation in hunter-kill profiles of mammals in tropical moist forests in west and central Africa. Published data from 36 sites in seven countries were used. A total of 71 mammals from 8 orders and 22 families were hunted. Significant variation was found in number of species exploited, but ungulates constituted 73.2% of all hunted animals. Rodents and primates contributed 12.2% and 12.0%, respectively. Large-bodied species (15.0-99.9 kg) represented over half (54.4%) of the total hunted biomass. Most species were snared, and were generally medium-sized, fast, terrestrial, frugivore-herbivores. By species, we showed using meta-analysis, that abundance was the main predictor of harvest level. But, the relationship between hunter presence (calculated as average hunter numbers operating per 100 days) and hunter ease (an index of catchability of the hunted fauna) and site harvest levels was shown to be significant only for hunter ease. Site harvest rates were higher in areas with more hunter presence. Overall, an average of 2000 carcasses (16,000 kg in weight) per site was extracted in a year. Per hunter, this amounted to 200 carcasses (1000 kg) extracted. Such amounts fall within reported unsustainable harvest levels of the mammalian fauna in African forests.     

Assessing the sustainability of brocket deer hunting in the Tamshiyacu-Tahuayo Communal Reserve, northeastern Peru

Since the 1800s, brocket deer have been an important source of meat and income for subsistence and professional hunters in the Peruvian Amazon. Today, local people continue to hunt brocket deer for subsistence meat and for sale in local meat markets. Although brocket deer are not hunted as frequently as peccaries, they make a significant contribution to rural household economies. This study assessed the sustainability of hunting of brocket deer by local communities in the Tamshiyacu Tahuayo Communal Reserve (TTCR), northeastern Peru. We analyzed data from 1991 to 1999 using density comparisons, hunting pressures, an age structure model, and a harvest model comparing results between heavily hunted, slightly hunted, and non-hunted sites. The four approaches agreed that brocket deer are harvested sustainably. The sustainability of brocket deer hunting will depend on the continued presence of other valuable wildlife species (e.g. peccaries and large rodents), which are more preferred due to their ease of hunting and higher rates of encounters. Gross productivity indicates that brocket deer are showing resilience in the form of density dependent reproductive adjustments in the TTCR, but they may still be vulnerable to overhunting. Consequently, current levels of harvesting may be continued until further ecological and biological information on the species' population trends assist in defining more reliable sustainable offtake levels.     

Hunting reduces recruitment of primate-dispersed trees in Amazonian Peru

Hunting with firearms decimates primates of large and medium body size (>2 kg) that disperse the seeds of large-seeded trees. In continuous, un-fragmented forests of southeastern Peru regularly hunted with firearms for 30-40 years, large primates are extirpated and medium-sized (medium) primates are reduced 61% compared with protected forests. At hunted sites seedlings and small juveniles (<1 m height) of trees dispersed by primates heavier than 2 kg are reduced 46%, a loss of one species m⁻², and abiotically-dispersed plants are 284% more common, adding eight individuals m⁻², compared with protected forests. Here we provide evidence consistent with the long-held prediction that commercial hunting changes plant communities. We show that the composition of seedling and small juvenile tree communities that ultimately regenerate future forests differs markedly in forests hunted with firearms compared with protected forests. This opens the possibility of shifts in tree species composition, even in hunted forests that are not logged or fragmented, towards forests dominated by trees dispersed by wind or non-game animals.     

Composite Likelihood Inference for Multivariate Gaussian Random Fields

In the recent years, there has been a growing interest in proposing covariance models for multivariate Gaussian random fields. Some of these covariance models are very flexible and can capture both the marginal and the cross-spatial dependence of the components of the associated multivariate Gaussian random field. However, effective estimation methods for these models are somehow unexplored. Maximum likelihood is certainly a useful tool, but it is impractical in all the circumstances where the number of observations is very large. In this work, we consider two possible approaches based on composite likelihood for multivariate covariance model estimation. We illustrate, through simulation experiments, that our methods offer a good balance between statistical efficiency and computational complexity. Asymptotic properties of the proposed estimators are assessed under increasing domain asymptotics. Finally, we apply the method for the analysis of a bivariate dataset on chlorophyll concentration and sea surface temperature in the Chilean coast.     

Allocation of hunting effort by Amazonian smallholders: Implications for conserving wildlife in mixed-use landscapes

Most tropical forest landscapes are modified by humans, but the effects of these changes on rural hunting patterns and hunted vertebrate populations remain poorly understood. We investigated subsistence hunting patterns across a highly heterogeneous landscape mosaic in the Brazilian Amazon, where hunters from three villages had access to primary forest, active and fallow agricultural fields, and active and fallow Eucalyptus plantations. Landscape composition and the areas used by hunters were defined using a remote-sensing approach combined with mapping. We quantified hunting effort accounting for the availability and spatial distribution of each habitat. Overall, 71% of the kills were sourced in primary forest, but hunting in primary forest, which was often combined with other extractive activities (such as Brazil nut harvesting), yielded the lowest catch-per-unit-effort of all habitats. Hunting effort per unit area was highest in fallow fields, followed by primary forest, and both of these habitats were over-represented within village hunting catchments when compared to the composition of the available landscape. Active and fallow fields sourced a limited number of species known to be resilient to hunting, but hunting had additional benefits through crop-raider control. In contrast, hunting pressure in active and fallow plantations was low, despite a high catch-per-unit-effort, presumably because there were limited additional benefits from visiting these habitats. These results indicate that large-scale tree plantation and forest regeneration schemes have limited conservation potential for large vertebrates, as they support few forest specialists and fail to attract hunters away from primary forest.     

Change-Point Estimation in the Multivariate Model Taking into Account the Dependence: Application to the Vegetative Development of Oilseed Rape

In this paper, we address the change-point estimation issue in multivariate observations which consist in functions having piecewise constant first derivatives corrupted by some additional noise. We propose to solve this problem by rewriting it as a variable selection issue in a sparse multivariate linear model. Moreover, the methodology that we propose takes into account the dependence that may exist within the multivariate observations. Then, the performance of our approach is assessed through some numerical experiments and compared to other alternative and classical methods. Finally, we apply our methodology to experimental data in order to study the vegetative development of oilseed rape. The evolution of the number of leaves of oilseed rape can be modeled as a function having piecewise constant first derivatives corrupted by some additional noise where the change-points correspond to key times in the plant phenology. Our novel estimation method increases the accuracy of the change-point estimation in comparison with classical approaches. Moreover, we show that the parameters of the covariance matrix depend on the level of competition between plants. Supplementary materials accompanying this paper appear online.     

Model-based maximum covariance analysis for irregularly observed climatological data

In climatology, maximum covariance analysis (MCA) is one of the most popular tools for investigating association between two multivariate variables across time and space. These association studies are important because many climate phenomena such as the El Niño-Southern Oscillation (ENSO) and the North Atlantic Oscillation are results of interaction between these variables. Despite its popularity, maximum covariance analysis does not provide straightforward statistical inference on its estimates and furthermore it does not provide an objective way to handle irregularly observed data, frequently encountered in climatology. The aim of this article is to describe a modelbased maximum covariance analysis that can accommodate irregularly observed data. The methodology combines maximum covariance analysis’s relationship with Tucker inter-battery factor analysis and the state-space methodology for missing data. The methodology is illustrated with an application to investigate association between Irish winter precipitation and global sea surface temperature (SST) anomalies.     

Threats from overhunting to the flying fox, Pteropus tonganus, (Chiroptera: Pteropodidae) on Niue Island, South Pacific Ocean

Flying fox fruit bats are hunted in an annual 2-month season in Niue, a small and isolated South Pacific island nation. The sustainability of this hunt has been questioned because of an obvious recent decline. We estimated the island-wide flying fox population to be between 2040 and 4080 bats, 2 months prior to the 1998-1999 hunt. Sixty hunters interviewed after the hunt had shot 1555 bats, an unsustainable number. Many Niueans believe that an infinite quantity of flying foxes live in two small taboo or forbidden areas that originally acted as wildlife sanctuaries to safeguard animal resources for times of famine. However, our surveys suggest only a small colony roosts in one taboo area. Niueans' belief that taboo areas shelter an unlimited number of bats cannot be refuted as the areas may not be visited. Consequently, few people believe that the population is being overharvested.     

Modeling yield sustainability for different rotations in long-term barley trials

In crop rotation trials, the errors arising over time on the same plot are correlated. Often-used analyses assume zero or constant correlation between the errors while the actual covariance structure for the plot errors might be very different. The objective of this study was to identify the most suitable covariance structure and incorporate the error structure in assessment of the crop rotations in terms of yield productivity and yield sustainability. A set of five covariance structures were examined for barley yield data from a 14-year, two-course barley rotation trial conducted at two locations in northern Syria. Selection of the covariance structures was based on the Akaike information criterion (AIC, Akaike 1974) (a function of penalized log-likelihood) obtained from fitting the structure. Covariance structure with heterogeneous variances and with constant correlation between errors over cycles within the same plot was found to account for most variability in grain and straw yields at both locations. Modeling data with this covariance structure, the legume rotations gave higher productivity as well as higher annual increases compared with the continuous barley system. This implies that an agricultural production system based on a legume following a cereal is likely to be more sustainable for cereal production compared to cereal followed by cereals.     

Making sequential analysis of environmental monitoring data feasible by simplifying the covariance matrix structure

As environmental monitoring data are collected successively in time, the data are suitable for sequential analysis. An earlier article proposed a refined sequential probability ratio test (SPRT) to test against a minimal relevanttrend, assuming no serial correlations and without modeling the spatial covariance matrix. As the model parameters are unknown in advance, a minimal number of observations(n _min) is required for estimation prior to analysis. Leaving the spatial covariance matrix unstructured, n _min increases if the number of sampling locations increases. Therefore, assumptions on the spatial covariance matrix are proposed, thereby reducing the number of nuisance parameters, thus reducingn _min. This article studies. three simple types of spatial covariance matrix structures and derives an adjusted SPRT for each of these types. Furthermore, we examine the robustness against deviations from the assumed spatial covariance matrix structure. Simulation studies show that adjusted SPRTs can be derived rather easily and that they are in general robust against deviations from the assumed type of spatial covariance matrix. Sequential analysis of simulated data, which are based on monitoring data of bats in the Netherlands, illustrates the use of one of the derived SPRTs.     

Size structure of illegally harvested and surviving caiman Caiman crocodilus yacare in Pantanal,Brazil

The Yacare caiman Caiman crocodilus yacare is protected by law in Brazil but is hunted illegally. Until now, few authorities have had access to data that might allow analysis of the effect of hunting. We used measurements from 2978 confiscated hides and 1358 skulls found in hunters' camps between October 1987 and September 1990 to reconstruct the sizes of harvested caiman. Hunters preferentially took large caiman and probably harvested mostly males. Some reproductive-sized animals remained in residual populations in heavily hunted areas, and often successful reproduction was known to have occurred in these areas.     

A Nonlinear Mixed-Effects Model for Multivariate Longitudinal Data with Dropout with Application to HIV Disease Dynamics

The main challenge in biomedical and clinical studies which involve collection of longitudinal data is the premature withdrawal of the subjects from the study resulting in incomplete data. Standard statistical analysis approaches usually give biased estimates of the model parameters if the mechanisms that led to dropout are ignored. In this discussion, we consider nonlinear mixed-effects models for multivariate longitudinal data in the presence of subject dropout. We present techniques for estimation of model parameters. These procedures are applied to estimate the parameters in the HIV dynamic system using routine observational data from an HIV clinic.     

基于多元地统计的土壤有机质含量空间格局反演

为了提高土壤有机质含量的空间预测精度,该文采用了一种多元地统计方法来构建遥感定量反演模型。考虑到回归误差在空间上具有一定程度的聚类,该文提出了基于局部变化均值的普通克里金方法,然后用其构建土壤有机质含量遥感定量反演模型。对四川省西南部土壤有机质含量进行空间预测试验,并与普通克里金、普通遥感定量反演、基于回归克里金的遥感定量反演等方法相比较。结果表明:该文提出方法的空间预测结果最优,其原因为该方法通过空间统计来建立采样数据与地表反射率间的联系,充分考虑了数据间的空间相关性,因此可以更精确地获得土壤有机质含量的遥感反演模型;相比基于回归克里金的遥感定量反演方法,基于局部变化均值的普通克里金假设回归误差在局部邻域内的均值也不一定为零,更符合实际情况。该方法为农田养分管理及区域农业的可持续发展提供科学依据。     

Resource availability, matrix quality, microclimate, and spatial pattern as predictors of patch use by the Karner blue butterfly

Determination of which aspects of habitat quality and habitat spatial arrangement best account for variation in a species’ distribution can guide management for organisms such as the Karner blue butterfly (Lycaeides melissa samuelis), a federally endangered subspecies inhabiting savannas of Midwest and Eastern United States. We examined the extent to which three sets of predictors, (1) larval host plant (Lupinus perennis, wild lupine) availability, (2) characteristics of the matrix surrounding host plant patches, and (3) factors affecting a patch’s thermal environment, accounted for variation in lupine patch use by Karner blues at Indiana Dunes National Lakeshore, Indiana and Fort McCoy, Wisconsin, USA. Each predictor set accounted for 7-13% of variation in patch occupancy by Karner blues at both sites and in larval feeding activity among patches at Indiana Dunes. Patch area, an indicator of host plant availability, was an exception, accounting for 30% of variation in patch occupancy at Indiana Dunes. Spatially structured patterns of patch use across the landscape accounted for 9-16% of variation in patch use and explained more variation in larval feeding activity than did spatial autocorrelation between neighboring patches. Because of this broader spatial trend across sites, a given management action may be more effective in promoting patch use in some portions of the landscape than in others. Spatial trend, resource availability, matrix quality, and microclimate, in general, accounted for similar amounts of variation in patch use and each should be incorporated into habitat management planning for the Karner blue butterfly.     

Explaining movement decisions of forest rodents in fragmented landscapes

Functional connectivity is a measure of the interaction of landscape structure and a species’ dispersal ability to determine the degree to which a landscape facilitates movement among patches. Dispersal through an inhospitable matrix requires that a species is willing to enter the matrix and can successfully colonize another habitat patch. Many connectivity indices have been developed which incorporate various attributes of the landscape, but little empirical evidence of the accuracy of these indices is available. We studied the ability of white-footed mice (Peromyscus leucopus), eastern chipmunks (Tamias striatus), and southern flying squirrels (Glaucomys volans) to move through agricultural fields in west-central Indiana. Radio-collared animals were translocated into corridors that either were connected to or unconnected from an associated forest patch to determine their willingness to enter the matrix. Animals also were translocated into fields to determine how motivation to find resources influences movement through the matrix. All species demonstrated a strong motivation to find the forest. Animals were capable of moving through the matrix successfully, however, we were unable to determine whether they would do so willingly. Initial bearing was an important predictor for successfully reaching a forest patch, which has implications for modeling dispersal. Additionally, abiotic variables, such as temperature and precipitation, had a strong effect on latency to move from the release site. Although weather often correlates with seasonal migration, our study emphasizes the importance of weather in influencing short-term decisions on timing of movement.     

分时段修正双源模型在西北干旱区玉米蒸散量模拟中的应用

蒸散发(ET)是陆地水循环过程的重要组成部分,同时也是区域能量平衡以及水量平衡的关键环节,精确估算ET,对于提高水分利用效率以及优化区域用水结构具有重要意义。本文利用黑河重大计划观测数据,对比了考虑CO_2浓度和不考虑CO_2浓度对玉米冠层影响的冠层阻力模型,分别将其耦合到双源的Shuttleworth-Wallace(S-W)模型中,并利用这两种模型分时段对玉米整个生育期内半小时尺度上的ET进行模拟,利用涡度相关实测数据对模型进行验证,最后分别对影响玉米冠层阻力的气象要素和影响ET的阻力参数进行敏感性分析,探寻大气CO_2浓度改变条件下黑河中游绿洲区玉米不同生长阶段的农田耗水规律。结果表明:本文所修正的考虑CO_2浓度对玉米冠层影响的冠层阻力模型耦合到S-W模型后,能够较精准地模拟玉米整个生育期不同生长阶段半小时尺度上农田耗水过程。敏感性分析表明:各生长阶段冠层阻力(r_s~c)和冠层面高度到参考面高度间的空气动力阻力(r_a~a)对ET的影响最为强烈,其他阻力参数对ET的影响不明显,ET的变化程度随着r_s~c和r_a~a的增大而减小。本文所修正的考虑CO_2浓度影响的分时段双源模型能够精准地模拟玉米整个生育期各生长阶段的ET,可为种植结构调整和土地利用方式改变以及CO_2浓度变化环境下的农田蒸散研究提供参考。     

Multivariate statistical analysis of the color-anthocyanin relationships in different soilless-grown strawberry genotypes

Apart from the need to assess the color of foods due to its preponderant role in their acceptability, there is currently a new trend consisting in the study of the relationships between the color and the pigments accounting for it. The color of five strawberry varieties cultivated in two different soilless systems has been studied, and an array of multivariate statistical methods have been performed to single out the color parameters that best discriminate among the different samples surveyed and to correlate them with the pigment content. It is concluded that there is not a direct relationship between the external and flesh colorations of the berries. Additionally, after discriminant methods were applied, it was noticed that, taking into account the strawberry varieties, >90% of the cases could be correctly classified, a noticeably lower percentage of correct classification (around 60%) being obtained when the type of cultivation system was selected as the criterion for discrimination. The best correlations of pigment-color coordinates were found between pelargonidin-3-rutinoside and the external a* (r= -0.87) followed by pelargonidin-3-glucoside and the internal L* (r= -0.72).     

Residual Variance–Covariance Modelling in Analysis of Multivariate Data from Variety Selection Trials

Field trials for variety selection often exhibit spatial correlation between plots. When multivariate data are analysed from these field trials, there is the added complication in having to simultaneously account for correlation between the traits at both the residual and genetic levels. This may be temporal correlation in the case of multi-harvest data from perennial crop field trials, or between-trait correlation in multi-trait data sets. Use of parsimonious yet plausible models for the variance–covariance structure of the residuals for such data is a key element to achieving an efficient and inferentially sound analysis. In this paper, a model is developed for the residual variance–covariance structure firstly by considering a multivariate autoregressive model in one spatial direction and then extending this to two spatial directions. Conditions for ensuring that the processes are directionally invariant are presented. Using a canonical decomposition, these directionally invariant processes can be transformed into a set of independent separable processes. This simplifies the estimation process. The new model allows for flexible modelling of the spatial and multivariate interaction and allows for different spatial correlation parameters for each harvest or trait. The methods are illustrated using data from lucerne breeding trials at several environments.