Testing for a change in diet using fatty acid signatures

Determining whether the diet of predators has changed is an important ecological problem and appropriate methodology is needed in order to test for differences or changes in diet. It is known that the fatty acid (FA) signature in a predator’s adipose tissue predictably reflects the prey consumed and that, consequently, a change in the FA signatures can be largely attributed to changes in the predator’s diet composition. The use of FA signatures as a means of detecting change in diet presents some statistical challenges however, since the FA signatures are compositional and sample sizes relative to the dimension of a signature are often small due to biological constraints. Furthermore, the FA signatures often contain zeros precluding the direct use of traditional compositional data analysis methods. In this paper, we provide the methodology to carry out valid statistical tests for detecting changes in FA signatures and we illustrate both independent and paired cases using simulation studies and real life seabird and seal data. We conclude that the statistical challenges using FA data are overcome through the use of nonparametric tests applied to the multivariate setting with suitable test statistics capable of handling the zeros that are present in the data.     

Crustacean habitat potential mapping in a tidal flat using remote sensing and GIS

To make a macrofaunal (crustacean) habitat potential map, the spatial distribution of ecological variables in the Hwangdo tidal flat, Korea, was explored. Spatial variables were mapped using remote sensing and a geographic information system (GIS) combined with field observations. A frequency ratio (FR) and logistic regression (LR) model were employed to map the macrofauna potential area for the Ilyoplax dentimerosa, a crustacean species. Spatial variables affecting the tidal macrofauna distribution were selected based on abundance and biomass and used within a spatial database derived from remotely sensed data of various types of sensors. The spatial variables included the intertidal digital elevation model (DEM), slope, distance from a tidal channel, tidal channel density, surface sediment facies, spectral reflectance of the near infrared (NIR) bands and the tidal exposure duration. The relation between the I. dentimerosa and each spatial variable was calculated using the FR and LR. The species was randomly divided into a training set (70%) to analyse habitat potential using FR and LR and a test set (30%) to validate the predicted habitat potential map. The relations were overlaid to produce a habitat potential map with the species potential index (SPI) value for each pixel. The potential habitat maps were compared with the surveyed habitat locations such as validation data set. The comparison results showed that the LR model (accuracy is 85.28%) is better in prediction than the FR (accuracy is 78.96%) model. The performance of models gave satisfactory accuracies. The LR provides the quantitative influence of variables on a potential habitat of species; otherwise, the FR shows the quantitative influence of a class in each variable. The combination of a GIS-based frequency ratio and logistic regression models and remote sensing with field observations is an effective method to determine locations favorable for macrofaunal species occurrences in a tidal flat.     

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

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

用扫描电镜分析表征大气气溶胶单颗粒

1　IntroductionAtmosphericaerosolsarecomplexmixture,originatedfromavarietyofsources.Mostoftheanalyticaltechniquesusedforitscharacterizationarebulkmethodsthatgenerateonesetofdatapersample,describingtheoverallcompositionofthesample .Toworkoutusefulinformationonpollu tionsources,itrequiresdataofagreatnumberofsamplesandmultivariatedataanalysismethodsusuallyareneeded .Ontheotherhand ,scanningelectronmicroscope energydispersiveX rayanaly sissystem (SEM EDX)isabletoprovideanalyticaldataforeachsing…     

Habitat use and foraging behavior of tiger sharks (<Emphasis Type="Italic">Galeocerdo cuvier</Emphasis>) in a seagrass ecosystem

Understanding the foraging behavior and spatial distribution of top predators is crucial to gaining a complete understanding of communities. However, studies of top predators are often logistically difficult and it is important to develop appropriate methods for identifying factors influencing their spatial distribution. Sharks are top predators in many marine communities, yet no studies have quantified the habitat use of large predatory sharks or determined the factors that might influence shark spatial distributions. We used acoustic telemetry and animal-borne video cameras ("Crittercam") to test the hypothesis that tiger shark (Galeocerdo cuvier) habitat use is determined by the availability of their prey. We also used Crittercam to conduct the first investigation of foraging behavior of tiger sharks. To test for habitat preferences of sharks, the observed proportion of time in each habitat for each individual was compared to the predicted values for that individual based on correlated random walk and track randomization methods. Although there was individual variation in habitat use, tiger sharks preferred shallow seagrass habitats, where their prey is most abundant. Despite multiple encounters with potential prey, sharks rarely engaged in prolonged high-speed chases, and did not attack prey that were vigilant. We propose that the tiger sharks' foraging tactic is one of stealth, and sharks rely upon close approaches to prey in order to be successful. This study shows that using appropriate analysis techniques and a variety of field methods it is possible to elucidate the factors influencing habitat use and gain insights into the foraging behavior of elusive top predators.     

Bayesian estimation of species relative abundances and habitat preferences using opportunistic data

We develop a new statistical procedure to monitor relative species abundances and their respective preferences for different habitat types, using opportunistic data. Following Giraud et al. (Biometrics 72(2):649–658, 2015), we combine the opportunistic data with some standardized data in order to correct the bias inherent to the opportunistic data collection. Species observations are modeled by Poisson distributions whose parameters quantify species abundances and habitat preferences, and are estimated using Bayesian computations. Our main contributions are (i) to tackle the bias induced by habitat selection behaviors, (ii) to handle data where the habitat type associated to each observation is unknown, (iii) to estimate probabilities of selection of habitat for the species. As an illustration, we estimate common bird species habitat preferences and abundances in the region of Aquitaine (France).     

Refining logistic regression models for wildlife habitat suitability modeling—A case study with muntjak and goral in the Central Himalayas, India

High quality habitat suitability maps are indispensable for the management and planning of wildlife reserves. This is particularly important for megadiverse developing countries where shortages in skilled manpower and funding may preclude the use of mathematically complex modeling techniques and resource-intensive field surveys. In this study, we propose a simulation based k-fold partitioning and re-substitution approach to refine and update logistic regression models that are widely used for habitat suitability assessment and modeling. We test the modeling strategy using data from a rapid field survey conducted for habitat suitability assessment for muntjak (Muntiacus muntjak) and goral (Naemorrhaedus goral) in the central Himalayas, India. Results obtained from simulations match expectations in terms of model behavior and in terms of published habitat associations of the investigated species. Qualitative comparisons with predictions from the GARP, MaxEnt and Bioclimatic Envelopes modeling systems also show broad agreement with predictions obtained from the proposed technique. The proposed technique is suggested as a rapid-assessment precursor to detailed habitat studies such as patch occupancy modeling in situations where funds or trained manpower are not available.     

The importance of defining focal assemblages when evaluating amphibian and reptile responses to land use

Habitat loss and degradation are primary threats to amphibians and reptiles, but the relative effects of common land uses on assemblages and the mechanisms that underlie faunal responses are poorly studied. We reviewed the effects of four prevalent types of habitat alteration (urbanization, agriculture, livestock grazing, and silviculture) on amphibian and reptile species richness and abundance by summarizing reported responses in the literature and by estimating effect sizes across studies for species richness in each land‐use type. We then used a multinomial model to classify species as natural habitat specialists, generalists, and disturbed habitat specialists and examined variation in effect sizes for each land‐use type according to habitat specialization categories. There were mixed conclusions from individual studies, some reporting negative, neutral, or positive effects of land use on species richness and total abundance. A large proportion of studies reported species‐specific effects of individual species abundance. However, in our analysis of effect sizes, we found a general trend of negative effects of land use on species richness. We also demonstrate that habitat associations of common species and species turnover can explain variation in the effect of land use on herpetofauna. Our review highlights the pervasive negative effects of common land uses on amphibians and reptiles, the importance of identifying groups vulnerable to land‐use change (e.g., forest‐associated species) in conservation studies, and the potential influence of disturbance‐associated species on whole assemblage analyses.     

Eigenanalysis of selection ratios from animal radio-tracking data

The development of methods to analyze habitat selection when resources are defined by several categories (e.g., vegetation types) is a topical issue in radio-tracking studies. The White and Garrott statistic, an extension of the widely used test of Neu et al., can be used to determine whether habitat selection is significant. As well, Manly's selection ratio, a particularly useful measure of resource selectivity by resource users, allows detection of the most strongly selected habitat types. However, when both the number of animals and types of habitat are large, the biologist often has to deal with an excessively large number of measures. In this paper we present a new method, the eigenanalysis of selection ratios, that generalizes these two common methods within the framework of eigenanalyses. This method undertakes an additive linear partitioning of the White and Garrott statistic, so that the difference between habitat use and availability is maximized on the first factorial axes. The eigenanalysis of selection ratios is therefore optimal in habitat selection studies. Although we primarily consider the case where the habitat availability is the same for all animals (design II), we also extend this analysis to the case where the habitat availability varies from one animal to another (design III). An application of this method is provided using radio-tracking data collected on 17 squirrels in five habitat types. The results indicate variability in habitat selection, with two groups of animals displaying two patterns of preference. This difference between the two groups is explained by the patch structure of the study area. Because this method is mainly exploratory, and therefore does not rely on any distributional assumption, we recommend its use in studies of habitat selection.     

Kernel-based home range method for data with irregular sampling intervals

Studies of habitat selection and movements often use radio-tracking data for defining animal home ranges. Home ranges (HR) can be approximated by a utilization density distribution (UD) that instead of assuming uniform use of areas within HR boundary provides a probabilistic measure of animal space use. In reality, radio-tracking data contain periods of frequent autocorrelated observations interspersed with temporally more independent observations. Using such temporally irregular data directly may result in biased UD estimates, because areas that have been sampled intensively receive too much weight. The problem of autocorrelation has been tackled by resampling data with an appropriate time interval. However, resampling may cause a large reduction in the data set size along with a loss of information. Evidently, biased UD estimates or reduction in data may prejudice the results on animal habitat selection and movement. We introduce a new method for estimating UDs with temporally irregular data. The proposed method, called the time kernel, accounts for temporal aggregation of observations and gives less weight to temporally autocorrelated observations. A further extension of the method accounts also for spatially aggregated observations with relatively low weights given to observations that are both temporally and spatially aggregated. We test the behaviour of the time kernel method and its spatiotemporal version using simulated data. In addition, the method is applied to a data set of brown bear locations.     

K-select analysis: a new method to analyse habitat selection in radio-tracking studies

Two kinds of wildlife habitat studies can be distinguished in the literature: hindcasting and forecasting studies. Hindcasting studies aim to emphasize among a large set of habitat variables those that are of interest for the focus species, whereas forecasting studies are intended to predict habitat selection according to a small number of habitat variables for a given area. We provide here a new analytical tool which relies on the concept of ecological niche, the K-select analysis, for hindcasting studies of habitat selection by animals using radio-tracking data. Each habitat variable defines one dimension in the ecological space. For each animal, the difference between the vector of average available habitat conditions and the vector of average used conditions defines the marginality vector. Its size is proportional to the importance of habitat selection, and its direction indicates which variables are selected. By performing a non-centered principal component analysis of the table containing the coordinates of the marginality vectors of each animal (row) on the habitat variables (column), the K-select analysis returns a linear combination of habitat variables for which the average marginality is greatest. It is a synthesis of variables which contributes the most to the habitat selection. As with principal component analysis, the biological significance of the factorial axes is deduced from the loading of variables. An example is provided: habitat selection by wild boar is studied in a Mediterranean habitat using the K-select analysis. The numerous advantages of the analysis (a large number of variables that can be included, individual variability in habitat selection taken into account, a lack of too strict underlying hypotheses) make it a powerful approach in radio-tracking studies designed to identify habitat variables that are selected by animals.     

Patch use and vigilance by sympatric lemmings in predator and competitor-driven landscapes of fear

Prey living in risky environments can adopt a variety of behavioral tactics to reduce predation risk. In systems where predators regulate prey abundance, it is reasonable to assume that differential patterns of habitat use by prey species represent adaptive responses to spatial variation in predation. However, patterns of habitat use also reflect interspecific competition over habitat. Collared (Dicrostonyx groenlandicus) and brown (Lemmus trimucronatus) lemmings represent such a system and possess distinct upland tundra versus mesic meadow habitat preferences consistent with interspecific competition. Yet, we do not know whether this habitat preference might also reflect differences in predation risk or whether the two species differ in their behavioral tactics used to avoid predation. We performed experiments where we manipulated putative predation risk perceived by lemmings by increasing protective cover in upland and meadow habitats while we recorded lemming activity and behavior. Both lemming species preferentially used cover more than open patches, but Dicrostonyx was more vigilant than Lemmus. Both species also constrained their activity to protective patches in upland and meadow habitats, but during different periods of the day. Use of cover and vigilance were independent of habitat, suggesting that both species live in a fearsome but flattened landscape of fear at Walker Bay (Nunavut, Canada), and that their habitat preference is a consequence of competition rather than predation risk. Future studies aiming to map the contours of fear in multi-prey–predator systems should consider how predation and competition interact to modify prey species’ habitat preference, patch use, and vigilance.     

Response of predators to loss and fragmentation of prey habitat: a review of theory

Despite extensive empirical research and previous reviews, no clear patterns regarding the effects of habitat loss and fragmentation on predator-prey interactions have emerged. We suggest that this is because empirical researchers do not design their studies to test specific hypotheses arising from the theoretical literature. In fact, theoretical work is almost completely ignored by empirical researchers, perhaps because it may be inaccessible to them. The purpose of this paper is to review theoretical work on the effects of habitat loss and fragmentation on predator-prey interactions. We provide a summary of clear, testable theoretical predictions for empirical researchers. To test one or more of these predictions, an empiricist will need certain information on the predator and prey species of interest. This includes: (1) whether the predator is a specialist on one prey species or feeds on many kinds of prey (omnivore and generalist); (2) whether the predator is restricted to the same habitat type as the focal prey (specialist), can use a variety of habitats but has higher survival in the prey habitat (omnivore), or lives primarily outside of the focal prey's habitat (generalist); (3) whether prey-only patches have lower prey extinction rates than predator-prey patches; and (4) whether the prey emigrate at higher rates from predator-prey patches than from prey-only patches. Empiricists also need to be clear on whether they are testing a prediction about habitat loss or habitat fragmentation and need to conduct empirical studies at spatial scales appropriate for testing the theoretical prediction(s). We suggest that appropriate use of the theoretical predictions in future empirical research will resolve the apparent inconsistencies in the empirical literature on this topic.     

Reliance of mobile species on sensitive habitats: a case study of manta rays (Manta alfredi) and lagoons

Quantifying the ecological importance of individual habitats to highly mobile animals is challenging because patterns of habitat reliance for these taxa are complex and difficult to observe. We investigated the importance of lagoons to the manta ray, Manta alfredi, a wide-ranging and vulnerable species in a less-disturbed atoll ecosystem. Lagoons are highly sensitive to anthropogenic disturbance and are known to be ecologically important to a wide variety of mobile species. We used a novel combination of research tools to examine the reliance of M. alfredi on lagoon habitats. Stable isotope analysis was used to assay the recent energetic importance of lagoons to M. alfredi; high-resolution tracking data provided information about how M. alfredi utilised lagoonal habitats over long and short time periods; acoustic cameras logged patterns of animal entrances and departures from lagoons; and photo identification/laser photogrammetry provided some insight into why they may be using this habitat. M. alfredi showed strong evidence of energetic dependence on lagoon resources during the course of the study and spent long periods of residence within lagoons or frequently transited into them from elsewhere. While within lagoons, they demonstrated affinities for particular structural features within this habitat and showed evidence of temporal patterning in habitat utilization. This work sheds light on how and why M. alfredi uses lagoons and raises questions about how this use may be altered in disturbed settings. More generally, these observations contribute to our knowledge of how to assess the ecological importance of particular habitats situated within the broader home range of mobile consumers.     

The feeding behavior of Beroe ovata

Patches of the neritic ctenophores Beroe ovata and Bolinopsis vitrea were observed on the edge of the Great Bahama Bank in order to determine the interaction between the predator Beroe ovata and its prey Bolinopsis vitrea. Laboratory experiments on Beroe ovata showed that it responds chemokinetically to the presence of its prey; as it swims it collides with other etenophores on which it preys. The unique use of macrocilia as cutting implements aids the predator in removing tissue from its prey, yielding maximum gain from each encounter. By means of direct observations while diving, data on horizontal and vertical patchiness, swimming speeds, abundance, and feeding behavior were collected, and used to assess the impact of populations of the predator on its prey. Together, these two animal populations form an ecological feed-back system which affects other portions of the planktonic community.     

A general approach to the analysis of habitat selection

The investigation of animal habitat selection aims at the detection of selective usage of habitat types and the identification of covariates influencing their selection. The results not only allow for a better understanding of the habitat selection process but are also intended to help improve the conservation of animals. Usually, habitat selection by larger animals is assessed by radio-tracking or visual observation studies, where the chosen habitat is determined for some animals at a set of specific points in time. Hence the resulting data often have the following structure: a categorical variable indicating the habitat type selected by an animal at a specific point in time is repeatedly observed and will be explained by covariates. These may either describe properties of the habitat types currently available and/or properties of the animal. In this paper, we present a general approach to the analysis of such data in a categorical regression setup. The proposed model generalizes and improves upon several of the approaches previously discussed in the literature. In particular, it accounts for changing habitat availability due to the movement of animals within the observation area. It incorporates both habitat- and animal-specific covariates, and includes individual-specific random effects to account for correlations introduced by the repeated measurements on single animals. Furthermore, the assumption that the effects are linear can be dropped by including the effects in nonparametric manner based on a penalized spline approach. The methodology is implemented in a freely available software package. We demonstrate the general applicability and the potential of the proposed approach in two case studies: The analysis of a songbird community in South-America and a study on brown bears in Central Europe.     

Three-way compositional analysis of water quality monitoring data

Water quality monitoring data typically consist of \(J\) parameters and constituents measured at \(I\) number of static locations at \(K\) sets of seasonal occurrences. The resulting \(I \times J \times K\) three-way array can be difficult to interpret. Additionally, the constituent portion of the dataset (e.g., major ion and trace element concentration, pH, etc.) is compositional in that it sums to a constant (e.g., 1 kg/L) and is mathematically confined to the simplex, the sample space for compositional data. Here we apply a Tucker3 model on centered log-ratio data to find low dimensional representation of latent variables as a means to simplify data processing and interpretation of three years of seasonal compositional groundwater chemistry data for 14 wells at a study site in Wyoming, USA. The study site has been amended with treated coalbed methane produced water, using a subsurface drip irrigation system, to allow for irrigation of forage crops. Results from three-way compositional data analysis indicate that primary controls on water quality at the study site include: solutes concentration by evapotranspiration, cation exchange, and dissolution of native salts. These findings agree well with results from more detailed investigations of the site. In addition, the model identified Ba uptake during gypsum precipitation in some portions of the site during the final 6–9 months of investigation, a process for which the timing and extent had not previously been identified. These results suggest that multi-way compositional analyses hold promise as a means to more easily interpret water quality monitoring data.     

Habitat association studies in conjunction with adaptive cluster samples

Habitat association studies investigate the relationships between habitat characteristics and animal usage of study regions. These studies are often conducted in conjunction with surveys designed primarily to estimate population totals. This paper shows that habitat association studies may proceed from surveys using adaptive cluster sampling. The manner in which units appear in the sample turns out not be relevant to the habitat association study, which proceeds as though the units came from a simple random sample. However, it is also shown that the information about the habitat association parameters is greater than one would expect from a simple random sample of the same general size.     

Sonar backscatter differentiation of dominant macrohabitat types in a hydrothermal vent field.

Over the past 20 years, sonar remote sensing has opened ways of acquiring new spatial information on seafloor habitat and ecosystem properties. While some researchers are presently working to improve sonar methods so that broad-scale high-definition surveys can be effectively conducted for management purposes, others are trying to use these surveying techniques in more local areas. Because ecosystem management is scale-dependent, there is a need to acquire spatiotemporal knowledge over various scales to bridge the gap between already-acquired point-source data and information available at broader scales. Using a 675-kHz single-pencil-beam sonar mounted on the remotely operated vehicle ROPOS, 2200 m deep on the Juan de Fuca Ridge, East Pacific Rise, five dominant habitat types located in a hydrothermal vent field were identified and characterized by their sonar signatures. The data, collected at different altitudes from 1 to 10 m above the seafloor, were depth-normalized. We compared three ways of handling the echoes embedded in the backscatters to detect and differentiate the five habitat types; we examined the influence of footprint size on the discrimination capacity of the three methods; and we identified key variables, derived from echoes that characterize each habitat type. The first method used a set of variables describing echo shapes, and the second method used as variables the power intensity values found within the echoes, whereas the last method combined all these variables. Canonical discriminant analysis was used to discriminate among the five habitat types using the three methods. The discriminant models were constructed using 70% of the data while the remaining 30% were used for validation. The results showed that footprints 20-30 cm in diameter included a sufficient amount of spatial variation to make the sonar signatures sensitive to the habitat types, producing on average 82% correct classification. Smaller footprints produced lower percentages of correct classification; instead of the habitat types, the sonar data responded to intrapatch roughness and hardness characteristics. The sonar variables used in this study and the methods for extracting and transforming them are fully described in this paper and available in the public domain.     

Effect of sheep grazing and fire on sage grouse populations in southeastern Idaho

This paper describes the development, evaluation, and use of a model that simulates the effect of grazing and fire on temporal and spatial aspects of sagebrush community vegetation and sage grouse population dynamics. The model is represented mathematically as a discrete-time, stochastic compartment model based on difference equations with a time interval of 1 week. In the model, sheep graze through sage grouse breeding habitat during spring and fall, and different portions of the area can burn at different frequencies, creating a habitat mosaic of burned and unburned areas.The model was evaluated by examining predictions of (1) growth of sagebrush canopy cover after fire, (2) seasonal dynamics of grass and forb biomass under historical environmental conditions, and (3) sage grouse population dynamics associated with selected sagebrush canopy covers. Simulated changes in sagebrush canopy cover following fire correspond well with qualitative reports of long-term trends, simulated seasonal dynamics of herbaceous biomass correspond well with field data, and simulated responses of sage grouse population size and age structure to changing sagebrush canopy cover correspond well to qualitative field observations.Simulation results suggest that large fires occurring at high frequencies may lead to the extinction of sage grouse populations, whereas fires occurring at low frequencies may benefit sage grouse if burned areas are small and sheep grazing is absent. Sheep grazing may contribute to sage grouse population decline, but is unlikely to cause extinction under fire regimes that are favorable to sage grouse.