Estimating population abundance using counts from an auxiliary population

Environmental and Ecological Statistics - We develop a new method for estimating population abundance for notoriously difficult to count populations. This is made possible using an easy to count...     

Estimating total abundance of a large temperate-reef fish using visual strip-transects

Total abundance estimates for the large, common, reef fish Cheilodactylus spectabilis (Hutton) were obtained for a marine reserve and adjacent section of coast in north-eastern New Zealand during 1985. Visual strip-transects were used to estimate abundance and size structure in both areas. The accuracy, precision and cost efficiency of five transect sizes (500, 375, 250, 100, 75 m²) were examined over three times per day (dawn, midday and dusk), by simulating transects over mapped C. spectabilis populations. Two transect sizes showed similarly high efficiency. The smaller of the two (20x5 m) was chosen for the survey because of the general advantages attributable to small sampling units. Biases related to strip-transect size are discussed. Preliminary sampling indicated that C. spectabilis was distributed heterogeneously, and that density was habitat-related. An optimal stratified-random design was employed in both locations, to obtain total abundance and size-structure estimates. This reduced the between-habitat source of variability in density. The total number of sampling units used was governed by the time available. The resulting total abundance estimates obtained were 18 338±2 886 (95% confidence limit) for the 5 km marine reserve, compared to 3 987±1 117 for an adjacent, heavily fished 4 km section of coast. When corrected for total area and habitat area sampled, this represented a 2.3-fold difference in abundance. If sampling had been designed to detect an arbitrary 10% difference in abundance within each habitat, an infeasible 440 h of sampling would have been required. Size-frequency distributions of C. spectabilis at the reserve had a larger model size class than distributions from the adjacent area. The data suggest that reserve status is causal in these differing abundance and size structure estimates.     

Estimating wild boar density in hunting areas by a probabilistic sampling of drive counts

Environmental and Ecological Statistics - The evaluation of wild boar density in a hunting district can be performed by accurate drive counts of boars within the drive areas assigned to each...     

Estimating species occurrence, abundance, and detection probability using zero-inflated distributions

Researchers have developed methods to account for imperfect detection of species with either occupancy (presence absence) or count data using replicated sampling. We show how these approaches can be combined to simultaneously estimate occurrence, abundance, and detection probability by specifying a zero-inflated distribution for abundance. This approach may be particularly appropriate when patterns of occurrence and abundance arise from distinct processes operating at differing spatial or temporal scales. We apply the model to two data sets: (1) previously published data for a species of duck, Anas platyrhynchos, and (2) data for a stream fish species, Etheostoma scotti. We show that in these cases, an incomplete-detection zero-inflated modeling approach yields a superior fit to the data than other models. We propose that zero-inflated abundance models accounting for incomplete detection be considered when replicate count data are available.     

Updating coarse-scale species distribution models using small fine-scale samples

Large, fine-grained samples are ideal for predictive species distribution models used for management purposes, but such datasets are not available for most species and conducting such surveys is costly. We attempted to overcome this obstacle by updating previously available coarse-grained logistic regression models with small fine-grained samples using a recalibration approach. Recalibration involves re-estimation of the intercept or slope of the linear predictor and may improve calibration (level of agreement between predicted and actual probabilities). If reliable estimates of occurrence likelihood are required (e.g., for species selection in ecological restoration) calibration should be preferred to other model performance measures. This updating approach is not expected to improve discrimination (the ability of the model to rank sites according to species suitability), because the rank order of predictions is not altered. We tested different updating methods and sample sizes with tree distribution data from Spain. Updated models were compared to models fitted using only fine-grained data (refitted models). Updated models performed reasonably well at fine scales and outperformed refitted models with small samples (10-100 occurrences). If a coarse-grained model is available (or could be easily developed) and fine-grained predictions are to be generated from a limited sample size, updating previous models may be a more accurate option than fitting a new model. Our results encourage further studies on model updating in other situations where species distribution models are used under different conditions from their training (e.g., different time periods, different regions).     

Modelling the distribution of plant species using the autologistic regression model

For modeling the distribution of plant species in terms of climate covariates, we consider an autologistic regression model for spatial binary data on a regularly spaced lattice. This model belongs to the class of autologistic models introduced by Besag (1974). Three estimation methods, the coding method, maximum pseudolikelihood method and Markov chain Monte Carlo method are studied and comparedvia simulation and real data examples. As examples, we use the proposed methodology to model the distributions of two plant species in the state of Florida.     

Estimating the normal background rate of species extinction

A key measure of humanity's global impact is by how much it has increased species extinction rates. Familiar statements are that these are 100–1000 times pre‐human or background extinction levels. Estimating recent rates is straightforward, but establishing a background rate for comparison is not. Previous researchers chose an approximate benchmark of 1 extinction per million species per year (E/MSY). We explored disparate lines of evidence that suggest a substantially lower estimate. Fossil data yield direct estimates of extinction rates, but they are temporally coarse, mostly limited to marine hard‐bodied taxa, and generally involve genera not species. Based on these data, typical background loss is 0.01 genera per million genera per year. Molecular phylogenies are available for more taxa and ecosystems, but it is debated whether they can be used to estimate separately speciation and extinction rates. We selected data to address known concerns and used them to determine median extinction estimates from statistical distributions of probable values for terrestrial plants and animals. We then created simulations to explore effects of violating model assumptions. Finally, we compiled estimates of diversification—the difference between speciation and extinction rates for different taxa. Median estimates of extinction rates ranged from 0.023 to 0.135 E/MSY. Simulation results suggested over‐ and under‐estimation of extinction from individual phylogenies partially canceled each other out when large sets of phylogenies were analyzed. There was no evidence for recent and widespread pre‐human overall declines in diversity. This implies that average extinction rates are less than average diversification rates. Median diversification rates were 0.05–0.2 new species per million species per year. On the basis of these results, we concluded that typical rates of background extinction may be closer to 0.1 E/MSY. Thus, current extinction rates are 1,000 times higher than natural background rates of extinction and future rates are likely to be 10,000 times higher. Estimación de la Tasa Normal de Extinción de Especies     

Estimating a distance dependent contagion function using point sample data

Natural events and human activities cause changes in landscape structure. Landscape metrics are used as a useful tool to study landscape trends and ecological processes related to the landscape structure. These metrics are commonly calculated on wall-to-wall raster data from remote sensing. A recent trend is to use sample data to estimate landscape metrics. In this study, point sampling was used to estimate a vector-based and distance dependent contagion metric. The metric is an extension of the established contagion. The statistical properties, for both unconditional and conditional contagions, were assessed by a point (point pairs) sampling experiment in maps from the National Inventory of landscapes in Sweden. Random and systematic sampling designs were tested for nine point distances and five sample sizes and for two classification systems. The systematic design showed slightly smaller root mean square error (RMSE) and bias than the random design. Both true and estimated values were calculated using computer programs in FORTRAN, which was specifically written for the purpose of the study. For a given sample size, RMSE and bias increased with increasing point distance. The estimator of unconditional contagion had acceptable RMSE and bias for moderate sample sizes, but in the conditional case the bias (and thus the RMSE) was unacceptably large. The main reason for this is that small classes (by area) affect both the true value of the contagion and are often missing in the sample. The method proposed can be adopted in gradient-based model of landscape structure where no distinct border is assumed between polygons. The method can also be applied in field-based inventories.     

Compositional analysis of overdispersed counts using generalized estimating equations

Multivariate abundance data are commonly collected in ecology, and used to explore questions of “community composition”—how relative abundance of different taxa changes with environmental conditions. In this paper, we propose a log-linear marginal modeling approach for analyzing such compositional count data, via generalized estimating equations. This method exploits the multiplicative nature of log-linear models for counts, by reparameterizing models that describe marginal effects on mean abundance. This allows partitioning into “main effects” and compositional effects, which is appealing for interpretation. We apply the proposed approach to reanalyze compositional counts of benthic invertebrates from Delaware Bay, and data of invertebrate communities inhabiting Acacia plants in eastern Australia. In both cases we resort to a resampling approach to make inferences about regression parameters, because the number of clusters was not large compared to cluster size.     

Estimating the spatio-temporal risk of disease epidemics using a bioclimatic niche model

There are presently few tools available for estimating epidemic risks from forest pathogens, and hence informing pro-active disease management. In this study we demonstrated that a bioclimatic niche model can be used to examine questions of epidemic risk in temperate eucalypt plantations. The bioclimatic niche model, CLIMEX, was used to identify regional variation in climate suitability for Mycosphaerella leaf disease (MLD), a major cause of foliage damage in temperate eucalypt plantations around the world. Using historical observations of MLD damage, we were able to convert the relative score of climatic suitability generated by CLIMEX into a severity ranking ranging from low to high, providing for the first time a direct link between risk and impact, and allowing us to explore disease severity in a way meaningful to forest managers. We determined that the ‘Compare Years’ function in CLIMEX could be used for site-specific risk assessment to identify severity, frequency and seasonality of MLD epidemics. We explored appropriate scales of risk assessment for forest managers. Applying the CLIMEX model of MLD using a 0.25° or coarser grid size to areas of sharp topographic relief frequently misrepresented the risk posed by MLD, because considerable variation occurred between individual forest sites encompassed within a single grid cell. This highlighted the need for site-specific risk assessment to address many questions pertinent to managing risk in plantations.     

Estimating Plecoglossus altivelis altivelis migration using a mass balance model expressed by hydrological distribution parameters in a major limpid river basin in Japan

We examined the influence of several hydrological and meteorological parameters on the migratory movements of ayu Plecoglossus altivelis altivelis in central Japan. When comprehensively evaluating rivers and ayu behaviour on a catchment scale, the subjects of analysis typically include human activities and hydrological and meteorological phenomena. However, limiting analyses to such factors may be too restrictive when human activities are being conducted. Accordingly, we incorporated a biological viewpoint into the evaluation method, analysing hydrological data (river discharge, river water temperature, sea water temperature) to determine watershed characteristics and examining the relationship between these characteristics and the habitat conditions of ayu. Then we constructed a numerical model for ayu migratory runs that incorporated ayu ecology and watershed characteristics. Analyses of ayu movements from a lower estuarine dam demonstrated that downstream displacements were associated with high water flows of more than 200 m³ s⁻¹ at the beginning of summer. We conclude that it is important to consider the effects of environmental parameters on the movements of different fish species to understand the causes of spatial variation in fish distribution in lowland rivers.     

Analysis of environmental correlates of sexual segregation in northern elephant seals using species distribution models

Elephant seals are among the most sexually dimorphic and polygynous species of all mammals. Their foraging grounds occupy a wide area of the world oceans, where they show spatial segregation between males and females. The objective of this paper was to correlate female and male foraging distributions of Mirounga angustirostris with main climatic variables at a biogeographical scale. We used website and bibliographical sources to obtain information on adult elephant seal distribution and environmental predictors (surface and bottom sea temperatures, productivity and bathymetry) and three species distribution models [maximum entropy model, environmental niche factor analysis and based on climatic envelopes (BIOCLIM)] to predict the habitat suitability of ocean regions. BIOCLIM provided the best fit. Sea surface and bottom temperatures were the variables with the highest explanatory power for females, while bathymetry was for males. Predictive maps suggest that low temperatures constrain female, but not male, distribution at high latitudes. We suggest that large size increases foraging efficiency of males because, among other benefits, it augments thermal insulation, improving the use of cold, rich sectors of the ocean. Different thermoregulatory abilities between sexes due to size dimorphism should be a complementary explanation of sexual segregation in elephant seals.     

Importance of species traits for species distribution in fragmented landscapes

Knowledge of the relationship between species traits and species distribution in fragmented landscapes is important for understanding current distribution patterns and as background information for predictive models of the effect of future landscape changes. The existing studies on the topic suffer from several drawbacks. First, they usually consider only traits related to dispersal ability and not growth. Furthermore, they do not apply phylogenetic corrections, and we thus do not know how considerations of phylogenetic relationships can alter the conclusions. Finally, they usually apply only one technique to calculate habitat isolation, and we do not know how other isolation measures would change the results. We studied the issues using 30 species forming congeneric pairs occurring in fragmented dry grasslands. We measured traits related to dispersal, survival, and growth in the species and recorded distribution of the species in 215 grassland fragments. We show many strong relationships between species traits related to both dispersal and growth and species distribution in the landscape, such as the positive relationship between habitat occupancy and anemochory and negative relationships between habitat occupancy and seed dormancy. The directions of these relationships, however, often change after application of phylogenetic correction. For example, more isolated habitats host species with smaller seeds. After phylogenetic correction, however, they turn out to host species with larger seeds. The conclusions also partly change depending on how we calculate habitat isolation. Specifically, habitat isolation calculated from occupied habitats only has the highest predictive power. This indicates slow dynamics of the species. All the results support the expectation that species traits have a high potential to explain patterns of species distribution in the landscape and that they can be used to build predictive models of species distribution. The specific conclusions are, however, dependent on the technique used, and we should carefully consider this when comparing among different studies. Since different techniques answer slightly different questions, we should attempt to use analyses both with and without phylogenetic correction and explore different isolation measures whenever possible and compare the results.     

Estimating behavioral parameters in animal movement models using a state-augmented particle filter

Data on fine-scale animal movement are being collected worldwide, with the number of species being tagged and the resolution of data rapidly increasing. In this study, a general methodology is proposed to understand the patterns in these high-resolution movement time series that relate to marine animal behavior. The approach is illustrated with dive data from a northern fur seal (Callorhinus ursinus) tagged on the Pribilof Islands, Alaska, USA. We apply a state-space model composed of a movement model and corresponding high-resolution vertical movement data. The central goal is to estimate parameters of this movement model, particularly their variation on appropriate time scales, thereby providing a direct link to behavior. A particle filter with state augmentation is used to jointly estimate the movement parameters and the state. A multiple iterated filter using overlapping data segments is implemented to match the parameter time scale with the behavioral inference. The time variation in the auto-covariance function facilitates identification of a movement model, allows separation of observation and process noise, and provides for validation of results. The analysis yields fitted parameters that show distinct time-evolving changes in fur seal behavior over time, matching well what is observed in the original data set.     

A study on abundance and distribution of mangrove species in Indian Sundarban using remote sensing technique

Conservation and management of Sundarban mangrove forest is difficult chiefly due to inaccessibility and hostile condition. Remote sensing serves as an important tool to provide up-to date baseline information which is the primary requirement for the conservation planning of mangroves. In this study, supervised classification by maximum likelihood classifier (MLC) has been used to classify LANDSAT TM and LANDSAT ETM satellite data. This algorithm is used for computing likelihood of unknown measurement vector belonging to unknown classes based on Bayesian equation. Image spectra for various mangrove species were also generated from hyperspectral image. During field visits, GPS locations of five dominant mangrove species with appreciable distribution were taken and image spectra were generated for the same points from hyperion image. The result of this classification shows that, in 1999 total mangrove forest accounted for 55.01 % of the study area which has been reduced to 50.63 % in the year 2010. Avicennia sp. is found as most dominating species followed by Excoecaria sp. and Phoenix sp. but the aerial distribution of Avicennia sp., Bruguiera sp. and Ceriops sp. has reduced. In this classification technique the overall accuracy and Kappa value for 1999 and 2010 are 80 % and 0.77, 85.71 % and 0.81 respectively.     

应用物种敏感性分布法分析太湖及天目湖水体的生态风险

采用物种敏感性分布法(SSDs法),应用水生生物急性和慢性毒性数据计算了31种污染物在水中和其中疏水物质在沉积物中的预测无效应浓度(PNECwater和PNECsed),通过收集以往报道的16种污染物在太湖和天目湖的环境浓度,用商值法求得物质的风险商并进行排序.结果发现:蒽、菲、萘、荧蒽、阿特拉津和马拉硫磷在太湖的梅梁湾和五里湖具有很大的生态风险;毒死蜱、三丁基锡氧化物和环己锡虽无环境浓度报道但很有可能具有风险.     

Insights on plant interaction between dominating species from patterns of plant association: expected covariance of pin-point cover measurements of two species

It has been suggested that in order to infer ecological processes from observed patterns of species abundance we need to investigate the covariance in species abundance. Consequently, an expression for the expected covariance of pin-point cover measurements of two species is developed. By comparing the observed covariance with the expected covariance we get a new type of information on the spatial arrangement of two species. Here the discrepancy between the observed and expected covariance may be thought of as a measure of the spatial configuration of the two species that may indicate underling ecological processes. The method is applied in a case study of Calluna vulgaris and Deschampsia flexuosa on dry heathland sites. The observed covariance of Calluna and Deschampsia at the level of the sites was positively and significantly correlated with the expected covariance. Negative covariance was observed on sites where both Calluna and Deschampsia had a high cover, which is in agreement with the notion that both species form distinct patches. Oppositely, at sites where both species have a low cover, we found that both the expected and observed covariance were positive. The proposed measure for the expected covariance of two species does capture information on the combined spatial configuration of the two species if the species are common. We show how this may be relevant for understanding the underlying ecological processes leading to the observed covariance.     

Estimating travel time of recharge water through a deep vadose zone using a transfer function model

We estimate the travel time of percolating water through a deep vadose zone at the regional scale using a transfer function model and a physical based conceptual flow model (Hydrus-1D), thereby exploiting the time series of precipitation, actual evapotranspiration and groundwater piezometry and generic vadose zone data. With the transfer function model we observe a high variability of estimated travel time varying from 0.9 to 3.1 years, corresponding to estimated vertical water flux velocities varying from 6.6 to 28.0 m/year. These results were compared with the travel time estimated from the physical based conceptual model. With the flow model, estimated travel time varies between 4.7 and 15.5 years, corresponding to water flux velocities varying between 1.7 and 4.1 m/year. The estimated travel time calculated with the flow model were therefore about five times larger than those estimated with the transfer function model. This could be explained by the fact that the transfer function model considers heterogeneous recharge from the vadose zone as well as from the vicinity of the piezometer through the so called “pushing effect”. In addition, the flow model requires various hydrogeological and hydrodynamic parameters which were estimated using generic parametrisation approaches, that are largely affected by uncertainty and may not reflect the local conditions. In contrast, the transfer function model only exploits available measurable time series and has the advantage of being site-specific.