Hierarchical spatial modeling for estimation of population size

Estimation of population size has traditionally been viewed from a finite population sampling perspective. Typically, the objective is to obtain an estimate of the total population count of individuals within some region. Often, some stratification scheme is used to estimate counts on subregions, whereby the total count is obtained by aggregation with weights, say, proportional to the areas of the subregions. We offer an alternative to the finite population sampling approach for estimating population size. The method does not require that the subregions on which counts are available form a complete partition of the region of interest. In fact, we envision counts coming from areal units that are small relative to the entire study region and that the total area sampled is a very small proportion of the total study area. In extrapolating to the entire region, we might benefit from assuming that there is spatial structure to the counts. We implement this by modeling the intensity surface as a realization from a spatially correlated random process. In the case of multiple population or species counts, we use the linear model of coregionalization to specify a multivariate process which provides associated intensity surfaces hence association between counts within and across areal units. We illustrate the method of population size estimation with simulated data and with tree counts from a Southwestern pinyon-juniper woodland data set.     

Soil microbial population and enzyme activity related to grazing pressure in alpine meadows of Nanda Devi Biosphere Reserve

The present study aims to analyze the interaction of prevailing biotic pressure on soil environment with emphasis on its physicochemical and microbiological characteristics determining soil fertility status and thus supporting plant and animal biodiversity in Nanda Devi Biosphere Reserve (NDBR) which is located in northern part of Uttaranchal hills between 79 degrees 40'E to 80 degrees 05'E longitude and 30 degrees 17'N to 30 degrees 41'E latitude. The experimental results revealed that the physico-chemical characteristics (viz., moisture, pH, EC, C, N, P, K, CEC) of soil were maximum in moderately grazed meadow and minimum in intensively grazed meadow. Soil microbial analysis measured in terms of total viable count (TVC) exhibited grazing sensitivity trend being maximum population of bacteria > fungi > actinomycetes. The soil microbial population was positively correlated with soil respiration, dehydrogenase activity, acid phosphatase and microbial biomass, which exhibited uneven trend with grazing pressure. Soil from moderately grazed meadow showed highest microbial count and enzyme activities, whilst intensively grazed meadow showed lowest microbial count and enzyme activities. This depicts the beneficial role of prescribed grazing up to limited extent in management of soil fertility, which might have supported luxuriant growth of a variety of grasses.     

Sensitivity analysis of North American bird population estimates

The Partners in Flight North American Landbird Conservation Plan provided estimates of population sizes for 448 landbird species using a multiplicative model. Input parameters in this calculation included the area of state × Bird Conservation Region polygons, area-specific mean Breeding Bird Survey counts circa 1995, and adjustment factors for the distance over which species may presumably be correctly counted, the assumed pairing of singing males with non-singing females, and variability in the propensity of birds to sing over the course of the survey day. I assessed the sensitivity of this population calculation to changes in the input parameters. I assessed both local and global sensitivity of the model to changes in the parameters with Monte Carlo one-at-a-time simulations and the Fourier amplitude sensitivity test (FAST). Monte Carlo simulations were an estimate of local model sensitivity whereas FAST estimated global model sensitivity, accommodating the potential shared variance between model parameters. Monte Carlo simulations suggested population estimates were 39% more sensitive to changes in the detection distance adjustment than to the other parameters; the other parameters were nearly equal in their contribution to model sensitivity. Conversely, FAST analysis determined that each of the input variables aside from the pair adjustment provided roughly equal contributions to variability in population estimates. The most efficient means for improving continental population estimates for birds surveyed by the Breeding Bird Survey will be through increased scrutiny of the species-specific distance detection and time-of-day adjustments and improved understanding in the spatial and temporal variability in the mean Breeding Bird Survey count.     

Estimation of population density by spatially explicit capture-recapture analysis of data from area searches

The recent development of capture-recapture methods for estimating animal population density has focused on passive detection using devices such as traps or automatic cameras. Some species lend themselves more to active searching: a polygonal plot may be searched repeatedly and the locations of detected individuals recorded, or a plot may be searched just once and multiple cues (feces or other sign) identified as belonging to particular individuals. This report presents new likelihood-based spatially explicit capture-recapture (SECR) methods for such data. The methods are shown to be at least as robust in simulations as an equivalent Bayesian analysis, and to have negligible bias and near-nominal confidence interval coverage with parameter values from a lizard data set. It is recommended on the basis of simulation that plots for SECR should be at least as large as the home range of the target species. The R package "secr" may be used to fit the models. The likelihood-based implementation extends the spatially explicit analyses available for search data to include binary data (animal detected or not detected on each occasion) or count data (multiple detections per occasion) from multiple irregular polygons, with or without dependence among polygons. It is also shown how the method may be adapted for detections along a linear transect.     

The application of generalized linear mixed models to multi-level sampling for insect population monitoring

Iwao's quadratic regression or Taylor's Power Law (TPL) are commonly used to model the variance as a function of the mean for sample counts of insect populations which exhibit spatial aggregation. The modeled variance and distribution of the mean are typically used in pest management programs to decide if the population is above the action threshold in any management unit (MU) (e.g., orchard, forest compartment). For nested or multi-level sampling the usual two-stage modeling procedure first obtains the sample variance for each MU and sampling level using ANOVA and then fits a regression of variance on the mean for each level using either Iwao or TPL variance models. Here this approach is compared to the single-stage procedure of fitting a generalized linear mixed model (GLMM) directly to the count data with both approaches demonstrated using 2-level sampling. GLMMs and additive GLMMs (AGLMMs) with conditional Poisson variance function as well as the extension to the negative binomial are described. Generalization to more than two sampling levels is outlined. Formulae for calculating optimal relative sample sizes (ORSS) and the operating characteristic curve for the control decision are given for each model. The ORSS are independent of the mean in the case of the AGLMMs. The application described is estimation of the variance of the mean number of leaves per shoot occupied by immature stages of a defoliator of eucalypts, the Tasmanian Eucalyptus leaf beetle, based on a sample of trees within plots from each forest compartment. Historical population monitoring data were fitted using the above approaches.     

On the use of growth rate parameters for projecting population sizes: Application to aphids

This paper extends the application of the cumulative size based mechanistic model, which has previously been shown to describe diverse aphid population size data well. The mechanistic model is reviewed with a focus on the explanatory role of the birth and death rate formulation. An analysis of two data sets, one on the mustard aphid and the other on the pecan aphid, indicates that multiple linear regression equations based on the estimated birth and death rate parameters alone account for nearly all (R² > 0.95) of the variability in two key population attributes, namely the peak count and the cumulative density. This indicates that population size variables may be projected directly from the growth rate parameters using linear equations. Such linear relationships based on the birth and death rate parameters are shown to hold also for certain generalized mechanistic models for which the analytical solution is not available. The birth and death rate coefficients, therefore, constitute a new succinct set of variables that could be included in the predictive modeling of aphid populations, as well as other insect and animal populations with local collapse which follow similar growth dynamics.     

Linking noninvasive genetic sampling and traditional monitoring to aid management of a trans-border carnivore population

Noninvasive genetic sampling has been embraced by wildlife managers and ecologists, especially those charged with monitoring rare and elusive species over large areas. Challenges arise when desired population measures are not directly attainable from genetic data and when monitoring targets trans-border populations. Norwegian management authorities count individual brown bears (Ursus arctos) using noninvasive genetic sampling but express management goals in the annual number of bear reproductions (females that produce cubs), a measure that is not directly available from genetic data. We combine noninvasive genetic sampling data with information obtained from a long-term intensive monitoring study in neighboring Sweden to estimate the number of annual reproductions by females detected within Norway. Most female brown bears in Norway occur near the border with neighboring countries (Sweden, Finland, and Russia) and their potential reproduction can therefore only partially be credited to Norway. Our model includes a simulation-based method that corrects census data to account for this. We estimated that 4.3 and 5.7 reproductions can be credited to females detected with noninvasive genetic sampling in Norway in 2008 and 2009, respectively. These numbers fall substantially short of the national target (15 annual reproductions). Ignoring the potential for home ranges to extend beyond Norway's borders leads to an increase in the estimate of the number of reproductions by -30%. Our study shows that combining noninvasive genetic sampling with information obtained from traditional intensive/invasive monitoring can help answer contemporary management questions in the currency desired by managers and policy makers. Furthermore, combining methodologies and thereby accounting for space use increases the accuracy of the information on which decisions are based. It is important that the information derived from multiple approaches is applicable to the same focal population and that predictions are cross-validated. When monitoring and management are constrained to administrative units, census data should be adjusted by discounting portions of individual space utilization that extend beyond the focal jurisdiction. Our simulation-based approach for making such an adjustment may be useful in other situations where management authorities target portions of trans-border populations.     

Estimating population dynamics without population data

We develop a biologically correct cost system for production systems facing invasive pests that allows the estimation of population dynamics without a priori knowledge of their true values. We apply that model to a data set for olive producers in Crete and derive from it predictions about the underlying population dynamics. Those dynamics are compared to information on population dynamics obtained from pest sampling with extremely favorable results.     

Estimating population dynamics without population data

We develop a biologically correct cost system for production systems facing invasive pests that allows the estimation of population dynamics without a priori knowledge of their true values. We apply that model to a data set for olive producers in Crete and derive from it predictions about the underlying population dynamics. Those dynamics are compared to information on population dynamics obtained from pest sampling with extremely favorable results.     

丁草胺污染对高产水稻土微生物区系的影响

土壤微生物多样性是表征土壤质量最有潜力的指标,与农田生态系统的稳定性和生产力密切相关。云南永胜涛源乡是保持我国水稻小面积超高产纪录的特殊生态区,常年施用丁草胺作为选择性芽前除草剂,因此,了解丁草胺对其土壤微生物物种多样性的影响意义重大。采用平板菌落计数法,研究了模拟条件下不同丁草胺剂量（有效成分质量分数0.15、0.30和1.5 mg·kg^-1）对高产水稻土中好氧细菌（aerobic bacteria）、放线菌（actinobacteria）和真菌（fungi）,以及功能微生物自生固氮菌（nitrogen fixing bacteria）、磷酸盐溶解菌（phosphate solubilizing bacteria）和硅酸盐细菌（silicate dissolving bacteria）数量的影响。结果表明：施药7 d,中、高质量分数（0.30和1.50 mg·kg^-1）丁草胺处理好氧细菌数量比CK分别高出78.6%和153.8%,而后数量逐渐下降,表现出先刺激生长、后抑制活性的作用,低质量分数（0.15 mg·kg^-1）丁草胺对好氧细菌的生长和增殖影响不明显;施药7 d,高质量分数处理放线菌数量超过CK 75.1%,表现出明显的刺激作用;施药15 d,中等质量分数处理放线菌数量比 CK 高出125.0%,丁草胺浓度越高,刺激作用越迅速,低浓度丁草胺对放线菌则主要表现为抑制作用。低浓度丁草胺对真菌的生长和增殖基本没有影响,中等浓度有先抑制后刺激的作用,施药30 d后其真菌数量超过CK 56.9%,高浓度丁草胺则表现为抑制作用,施药7、30和45 d其真菌数量始终显著低于CK;不同浓度处理丁草胺均能刺激自生固氮菌的数量显著增加,施药7 d,低、中、高质量分数处理自生固氮菌数量分别高出CK 237.1%,179.9%和138.1%,刺激作用显著,但随培养时间延长,高浓度开始表现出抑制作用;不同浓度丁草胺均能抑制磷酸盐溶解菌的生长和增殖,低浓度处理抑制作用微弱,中、高浓度处     

A comparative study on the physicochemical and bacterial analysis of drinking, borewell and sewage water in the three different places of Sivakasi

The drinking, borewell and sewage water in the Sanmugasikamani Nadar (S.N) street, Naivatti Nadar (N.N) street and Thiruthangal area of Sivakasi has been studied. The various constituents monitored include the physicochemical characters like pH, total solids, total dissolved solids, total suspended solids; chemical parameters like total alkalinity acidity free CO2, dissolved oxygen, total hardness, calcium, magnesium, chloride, salinity and bacterial parameters like standard plate count (SPC), total coliform count (TCC), faecal coliform count (FCC), faecal streptococcal count (FSC). Most of the physicochemical characters of drinking and borewell water were within the ISI permissible level. However in water samples from all the sites, bacterial count exceeded the recommended permissible level of WHO. Introduction of sewage into the drinking and borewell water was the main reason for the bacterial contamination. The boiling of water is therefore advisable before consumption. The physicochemical and bacterial characters of the sewage water were unworthy. The sewage water recycling was necessary to minimize the water born diseases.     

Efficient statistical mapping of avian count data

We develop a spatial modeling framework for count data that is efficient to implement in high-dimensional prediction problems. We consider spectral parameterizations for the spatially varying mean of a Poisson model. The spectral parameterization of the spatial process is very computationally efficient, enabling effective estimation and prediction in large problems using Markov chain Monte Carlo techniques. We apply this model to creating avian relative abundance maps from North American Breeding Bird Survey (BBS) data. Variation in the ability of observers to count birds is modeled as spatially independent noise, resulting in over-dispersion relative to the Poisson assumption. This approach represents an improvement over existing approaches used for spatial modeling of BBS data which are either inefficient for continental scale modeling and prediction or fail to accommodate important distributional features of count data thus leading to inaccurate accounting of prediction uncertainty.     

Detecting Population Declines over Large Areas with Presence-Absence, Time-to-Encounter, and Count Survey Methods

Abstract:  Ecologists often discount presence-absence surveys as a poor way to gain insight into population dynamics, in part because these surveys are not amenable to many standard statistical tests. Still, presence-absence surveys are sometimes the only feasible alternative for monitoring large areas when funds are limited, especially for sparse or difficult-to-detect species. I undertook a detailed simulation study to compare the power of presence-absence, count, and time-to-encounter surveys to detect regional declines in a population. I used a modeling approach that simulates both population numbers and the monitoring process, accounting for observation and other measurement errors. In gauging the efficacy of presence-absence surveys versus other approaches, I varied the number of survey sites, the spatial variation in encounter rate, the mean encounter rate, and the type of population loss. My results showed that presence-absence data can be as or more powerful than count data in many cases. Quantitative guidelines for choosing between presence-absence surveys and count surveys depend on the biological and logistical constraints governing a conservation monitoring situation. Generally, presence-absence surveys work best when there is little variability in abundance among the survey sites, the organism is rare, and the species is difficult to detect so that the time spent getting to each survey site is less than or equal to the time spent surveying each site. Count surveys work best otherwise. I present a case study with count data on the Northern Flicker ( Colaptes auratus ) from the North American Breeding Bird Survey to illustrate how the method might be used with field-survey data. The case study demonstrates that a count survey would be the most cost-effective design but would entail reduction in the number of sites. If this site reduction is not desirable, a presence-absence survey would be the most cost-effective survey.     

Factors affecting aural detections of songbirds.

Many factors affect the number of birds detected on point count surveys of breeding songbirds. The magnitude and importance of these factors are not well understood. We used a bird song simulation system to quantify the effects of detection distance, singing rate, species differences, and observer differences on detection probabilities of birds detected by ear. We simulated 40 point counts consisting of 10 birds per count for five primary species (Black-and-white Warbler Mniotilta varia, Black-throated Blue Warbler Dendroica caerulescens, Black-throated Green Warbler Dendroica virens, Hooded Warbler Wilsonia citrina, and Ovenbird Seiurus aurocapillus) over a range of 15 distances (34-143 m). Songs were played at low (two songs per count) and high (13-21 songs per count) singing rates. Detection probabilities averaged across observers ranged from 0.60 (Black-and-white Warbler) to 0.83 (Hooded Warbler) at the high singing rate and 0.41 (Black-and-white Warbler) to 0.67 (Hooded Warbler) at the low singing rate. Logistic regression analyses indicated that species, singing rate, distance, and observer were all significant factors affecting detection probabilities. Singing rate x species and singing rate X distance interactions were also significant. Simulations of expected counts, based on the best logistic model, indicated that observers detected between 19% (for the worst observer, lowest singing rate, and least detectable species) and 65% (for the best observer, highest singing rate, and most detectable species) of the true population. Detection probabilities on actual point count surveys are likely to vary even more because many sources of variability were controlled in our experiments. These findings strongly support the importance of adjusting measures of avian diversity or abundance from auditory point counts with direct estimates of detection probability.     

Robust population management under uncertainty for structured population models.

Structured population models are increasingly used in decision making, but typically have many entries that are unknown or highly uncertain. We present an approach for the systematic analysis of the effect of uncertainties on long-term population growth or decay. Many decisions for threatened and endangered species are made with poor or no information. We can still make decisions under these circumstances in a manner that is highly defensible, even without making assumptions about the distribution of uncertainty, or limiting ourselves to discussions of single, infinitesimally small changes in the parameters. Suppose that the model (determined by the data) for the population in question predicts long-term growth. Our goal is to determine how uncertain the data can be before the model loses this property. Some uncertainties will maintain long-term growth, and some will lead to long-term decay. The uncertainties are typically structured, and can be described by several parameters. We show how to determine which parameters maintain long-term growth. We illustrate the advantages of the method by applying it to a Peregrine Falcon population. The U.S. Fish and Wildlife Service recently decided to allow minimal harvesting of Peregrine Falcons after their recent removal from the Endangered Species List. Based on published demographic rates, we find that an asymptotic growth rate lambda > 1 is guaranteed with 5% harvest rate up to 3% error in adult survival if no two-year-olds breed, and up to 11% error if all two-year-olds breed. If a population growth rate of 3% or greater is desired, the acceptable error in adult survival decreases to between 1% and 6% depending of the proportion of two-year-olds that breed. These results clearly show the interactions between uncertainties in different parameters, and suggest that a harvest decision at this stage may be premature without solid data on adult survival and the frequency of breeding by young adults.     

Extinction-effective population index: incorporating life-history variations in population viability analysis

Viability status of populations is a commonly used measure for decision-making in the management of populations. One of the challenges faced by managers is the need to consistently allocate management effort among populations. This allocation should in part be based on comparison of extinction risks among populations. Unfortunately, common criteria that use minimum viable population size or count-based population viability analysis (PVA) often do not provide results that are comparable among populations, primarily because they lack consistency in determining population size measures and threshold levels of population size (e.g., minimum viable population size and quasi-extinction threshold). Here I introduce a new index called the "extinction-effective population index," which accounts for differential effects of demographic stochasticity among organisms with different life-history strategies and among individuals in different life stages. This index is expected to become a new way of determining minimum viable population size criteria and also complement the count-based PVA. The index accounts for the difference in life-history strategies of organisms, which are modeled using matrix population models. The extinction-effective population index, sensitivity, and elasticity are demonstrated in three species of Pacific salmonids. The interpretation of the index is also provided by comparing them with existing demographic indices. Finally, a measure of life-history-specific effect of demographic stochasticity is derived.     

Traditional occupancy–abundance models are inadequate for zero-inflated ecological count data

Traditional occupancy–abundance and abundance–variance–occupancy models do not take into account zero-inflation, which occurs when sampling rare species or in correlated counts arising from repeated measures. In this paper we propose a novel approach extending occupancy–abundance relationships to zero-inflated count data. This approach involves three steps: (1) selecting distributional assumptions and parsimonious models for the count data, (2) estimating abundance, occupancy and variance parameters as functions of site- and/or time-specific covariates, and (3) modelling the occupancy–abundance relationship using the parameters estimated in step 2. Five count datasets were used for comparing standard Poisson and negative binomial distribution (NBD) occupancy–abundance models. Zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) occupancy–abundance models were introduced for the first time, and these were compared with the Poisson, NBD, He and Gaston's and Wilson and Room's abundance–variance–occupancy models. The percentage of zero counts ranged from 45 to 80% in the datasets analysed. For most of the datasets, the ZINB occupancy–abundance model performed better than the traditional Poisson, NBD and Wilson and Room's model. He and Gaston's model performed better than the ZINB in two out of the five datasets. However, the occupancy predicted by all models increased faster than the observed as density increased resulting in significant mismatch at the highest densities. Limitations of the various models are discussed, and the need for careful choice of count distributions and predictors in estimating abundance and occupancy parameter are indicated.     

Evaluation d'une population planctonique

In order to evaluate quantitative aspects of dynamics in planetonic populations, a method is presented permitting the more accurate estimation of the number of individuals within, each size-group. The samples analysed may have been caught with any type of collecting gear. Correcting coefficients are determined only once for each species studied and gear employed from suitable material, and may then be used for any other sample concerned with the same species and gear. The method is based on the fact that, in monospecific populations with all generations present, the number of individuals is always lowest in the oldest age group. A model calculation is presented for euphausiid crustaceans caught with a 10 ft Isaacs-Kidd midwater trawl.     

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.     

回用水紫外线消毒

在污水的深度处理工艺中,紫外线消毒具有很多优点,如不向水中投加化学剂,停留时间短,运行管理安全等.本文在研究紫外线静态试验中,紫外线投量与细菌存活率之间的关系,可作为紫外线消毒器的设计依据.基于静态实验的结果,在规模为2.5m~3/h的消毒器中进行了动态实验.实验结果表明,消毒后出水中细菌总数<100个/mL,满足回用要求.