Metal-contaminated potato crops and potential human health risk in Bolivian mining highlands

This study assessed metals in irrigation water, soil and potato crops impacted by mining discharges, as well as potential human health risk in the high desert near the historic mining center of Potosí, Bolivia. Metal concentrations were compared with international concentration limit guidelines. In addition, an ingested average daily dose and minimum risk level were used to determine the hazard quotient from potato consumption for adults and children. Irrigation water maximum concentrations of Cd, Pb and Zn in mining-impacted sites were elevated 20- to 1100-fold above international concentration limit guidelines. Agricultural soils contained total metal concentrations of As, Cd, Pb and Zn that exceeded concentration limits in agricultural soil guidelines by 22-, 9-, 3- and 12-fold, respectively. Potato tubers in mining-impacted sites had maximum concentrations of As, Cd, Pb and Zn that exceeded concentration limits in commercially sold vegetables by 9-, 10-, 16- and fourfold, respectively. Using conservative assumptions, hazard quotients (HQ) for potatoes alone were elevated for As, Cd and Pb among children (range 1.1–71.8), in nearly all of the mining-impacted areas; and for As and Cd among adults (range 1.2–34.2) in nearly all of the mining-impacted areas. Only one mining-impacted area had a Pb adult HQ for potatoes above 1 for adults. Toxic trace elements in a major regional dietary staple may be a greater concern than previously appreciated. Considering the multitude of other metal exposure routes in this region, it is likely that total HQ values for these metals may be substantially higher than our estimates.     

Handling covariates subject to limits of detection in regression

In the environmental health sciences, measurements of toxic exposures are often constrained by a lower limit called the limit of detection (LOD), with observations below this limit called non-detects. Although valid inference may be obtained by excluding non-detects in the estimation of exposure effects, this practice can lead to substantial reduction in power to detect a significant effect, depending on the proportion of censoring and the closeness of the effect size to the null value. Therefore, a variety of methods have been commonly used in the environmental science literature to substitute values for the non-detects for the purpose of estimating exposure effects, including ad hoc values such as ${LOD/2, LOD/\sqrt{2}}$ and LOD. Another method substitutes the expected value of the non-detects, i.e., E[X|X ≤?LOD] but this requires that the inference be robust to mild miss-specifications in the distribution of the exposure variable. In this paper, we demonstrate that the estimate of the exposure effect is extremely sensitive to ad-hoc substitutions and moderate distribution miss-specifications under the conditions of large sample sizes and moderate effect size, potentially leading to biased estimates. We propose instead the use of the generalized gamma distribution to estimate imputed values for the non-detects, and show that this method avoids the risk of distribution miss-specification among the class of distributions represented by the generalized gamma distribution. A multiple imputation-based procedure is employed to estimate the regression parameters. Compared to the method of excluding non-detects, the proposed method can substantially increase the power to detect a significant effect when the effect size is close to the null value in small samples with moderate levels of censoring (?≤ 50%), without compromising the coverage and relative bias of the estimates.     

中国冶炼行业场地土壤污染特征及分布情况

通过收集大量国内各个地区冶炼场地的最新污染数据,试图从全国尺度范围进行分析,反映中国冶炼行业场地的土壤污染状况.运用污染指数评价法、潜在生态危害指数法和生物毒性评价法,以《土壤环境质量建设用地土壤污染风险管控标准(试行)》(GB 36600—2018)的第一类用地标准值为参比,对土壤中重金属和有机物的污染特征及生态风险...     

Regional ecotoxicological hazards associated with anthropogenic enrichment of heavy metals

Regional geochemical data of heavy metals are commonly used for environmental risk assessment and management. Often these data are based on so-called total concentrations, whereas the exposure to the mobile or reactive fraction of these elements finally determines whether the exposed ecosystem is at risk and to which extent. The objective of our research was to develop a wider applicable method for quantitative hazard assessment of soil metal contamination attributable to the activity of man, based on and illustrated with data from the Netherlands. Since chemical availability (0.43 M HNO3 extractable concentrations) of Cd, Cu, Pb and Zn appeared strongly related to the estimated anthropogenic enrichment, we used these concentrations to assess the hazard of human-induced enrichment of these metals. We expressed the enrichment hazard using the toxic pressure concept, which estimates the fraction of biological species (varying between 0 and 1) potentially affected due to the level of exposure to single metals or their local mixtures. This is done using logistic (enrichment) concentration/response models parameterized with ecotoxicological effect data from toxicity tests and mixture models. Hazards varied from very low toxic pressures (lower than 0.01) to (most often) toxic pressure less than 0.05, whereby the latter relates to the so-called 95%-protection criterion used in some soil protection legislations. In rare cases, the toxic pressure exceeded the value of 0.05, to an upper limit of 0.054 for Cd. The rank order of metal enrichment hazards suggests that Cd enrichment induces the largest hazard increase. There are limited (rank order) differences in enrichment hazards between soil types. Comparing the judgement of soils based on soil screening levels and based on toxic pressure of anthropogenic Cd, Cu, Pb and Zn enrichments, the soil screening values appear to more conservative. This exemplifies the use of soil screening values as a method to note regulatory concern, but not always indicating an actual hazard or risk. When screening values are exceeded, refined hazard insights can be obtained, as illustrated in this paper. This provides a more refined insight in the ecotoxic implications of human-induced metal enrichments in soils, as refined basis for risk management decisions.     

AVS-SEM relationships and potential bioavailability of trace metals in sediments from the Southeastern Mediterranean sea,Egypt

In risk assessment of aquatic sediments, much attention is paid to the difference between acid volatile sulfide (AVS) and simultaneously extracted metals (SEM) as indicators of metal bioavailability. Distribution of AVS and SEM (Cd, Cu, Ni, Pb and Zn) were determined in 20 representative sampling sites collected along the Egyptian Mediterranean coast. Total SEM concentration [ΣSEM] ranged from 0.012 to 0.241 μmoleg^?1. AVS concentrations were much more variable, showing significant spatial variations. The values ranged from 0.015 to 31.326 μmoleg^?1. The different relationships between AVS and SEM to establish mechanical models such as the ratio of ΣSEM and AVS [ΣSEM/AVS], the difference between the ΣSEM and AVS [ΣSEM–AVS], or the organic carbon normalised difference between SEM and AVS [ΣSEM-AVS]/f_oc models were used as parameters to evaluate potential bioavailability. Considering SEM/AVS model, sediments at the Western Harbour, Eastern Harbour, Ras El-Burr, El-Gamil East, and Port Said stations are occasionally toxic, while according to SEM-AVS model, no indication of associated adverse toxic effect would occur at any of the sampling sites. Calculated [ΣSEM-AVS]/f_oc was<130 μmoleg^?1 in the sediments indicating that sediments should pose a low risk of adverse biological effects owing to cadmium, copper, lead, nickel and zinc. Association of adverse effects to aquatic organisms was determined, using the classification of the sediments according to the Sediment Quality Guidelines (SQGs). The marine Threshold Effect Levels (TEL) and Probable Effect Levels (PEL) were used in this work and in order to obtain a more realistic measure of predicted toxicity, mean PEL quotients (PELq) were calculated. The mean PELq calculated for the sampling sites was (0.11–1.5) and categorised as slightly toxic at all stations except Baghoush, Nobarreya and western harbour, which had PELq<0.1 and categorised as non-toxic. The obtained data concluded that the remobility and bioavailability of trace metals contained in surfacial sediments of the Egyptian Mediterranean coastal area are low.     

Crediting uncertain ecosystem services in a market

The margin of safety (MOS) approach is an increasingly prevalent tool for ensuring the integrity of market-based programs for providing ecosystem services. Over-crediting is reduced by setting aside mean estimates of uncertain services in favor of a more conservative estimate. Like many environmental policy problems, ecosystem service markets involve the aggregation of uncertainty over multiple scales, e.g. from landowners to market intermediaries to the overall market. We examine how the MOS instrument affects, and is affected by, an ecosystem services market. We show that the common bottom-up approach of imposing risk preferences at a local, disaggregated level—held over from earlier development in the context of toxics and command and control-style health risk regulation—leads to several unintended consequences. Furthermore, discounting landowner services can actually increase their profits, conditional on the elasticity of credit demand. We illustrate theoretical insights with an empirical application to greenhouse gas offset crediting in agriculture.     

A meta-BACI approach for evaluating management intervention on chronic wasting disease in mule deer.

Advances in acquiring and analyzing the spatial attributes of data have greatly enhanced the potential utility of wildlife disease surveillance data for addressing problems of ecological or economic importance. We present an approach for using wildlife disease surveillance data to identify areas for (or of) intervention, to spatially delineate paired treatment and control areas, and then to analyze these nonrandomly selected sites in a meta-analysis framework via before-after-control impact (BACI) estimates of effect size. We apply these methods to evaluate the effectiveness of attempts to reduce chronic wasting disease (CWD) prevalence through intensive localized culling of mule deer (Odocoileus hemionus) in north-central Colorado, USA. Areas where surveillance data revealed high prevalence or case clusters were targeted by state wildlife management agency personnel for focal scale (on average <17 km2) culling, primarily via agency sharpshooters. Each area of sustained culling that we could also identify as unique by cluster analysis was considered a potential treatment area. Treatment areas, along with spatially paired control areas that we constructed post hoc in a case-control design (collectively called "management evaluation sites"), were then delineated using home range estimators. Using meta-BACI analysis of CWD prevalence data for all management evaluation sites, the mean effect size (change of prevalence on treatment areas minus change in prevalence on their paired control areas) was 0.03 (SE = 0.03); mean effect size on treatment areas was not greater than on paired control areas. Excluding cull samples from prevalence estimates or allowing for an equal or greater two-year lag in system responses to management did not change this outcome. We concluded that management benefits were not evident, although whether this represented true ineffectiveness or was a result of lack of data or insufficient duration of treatment could not be discerned. Based on our observations, we offer recommendations for designing a management experiment with 80% power to detect a 0.10 drop in prevalence over a 6-12-year period.     

Variation in plasticity of personality traits implies that the ranking of personality measures changes between environmental contexts: calculating the cross-environmental correlation

Studies increasingly explore whether there is variation between individuals in how they adjust their behavior to different environmental contexts using random regression analysis (RR). RR estimates the between-individual variance in elevation (expected behavior in the mean environment) and variance in plasticity (individual-specific adjustment of behavior to the environment) and the covariance between these properties. These (co)variances allow deriving both environment-specific repeatability estimates and the individual-level correlation in behavior between environmental conditions, both of which are of key interest for understanding consistency in behavior over space and time, a core issue in animal personality research. However, very few studies use the RR estimates to produce this information. In this paper, I outline how to extract this information (including example code in R) and apply the approach to 16 estimates based on nine published RR studies. Despite the fact that the behaviors have a decent repeatability under different environmental conditions, I find that in half the cases, the cross-environmental correlation is low (<0.4). This implies that individuals differ in their behavioral trait values but that the ranking of these individual values tends to change between environmental conditions. This level of inconsistency in animal personality traits appears not to have been fully appreciated, and future studies on plasticity using RR should explicitly include it.     

Berücksichtigung von Variabilität und Unsicherheit in quantitativen Risikoabschätzungen (QRA)

Using the example of residential living on a contaminated site, a probabilistic exposure assessment is performed with variability and uncertainty being modelled separately. Probability distributions are used in the exposure model in order to characterize person-related variables (e.g. body weight) only; chemical-specific parameters are being held constant. In addition, uncertainty concerning one selected variable (soil ingestion rate) was modelled. Comparing these results to conventional “worst case” estimates, we find those estimates located in the uppermost range of the probabilistic estimates. The worst case estimates tend to be highly conservative and possibly unrealistic.     

Aquatic metabolism and ecosystem health assessment using dissolved O2 stable isotope diel curves.

Dissolved O2 concentration and delta18O-O2 diel curves can be combined to assess aquatic photosynthesis, respiration, and metabolic balance, and to disentangle some of the confounding factors associated with interpretation of traditional O2 concentration curves. A dynamic model is used to illustrate how six key environmental and biological parameters interact to affect diel O2 saturation and delta18O-O2 curves, thereby providing a fundamental framework for the use of delta18O-O2 in ecosystem productivity studies. delta18O-O2 provides information unavailable from concentration alone because delta18O-O2 and saturation curves are not symmetrical and can be used to constrain gas exchange and isotopic fractionation by eliminating many common assumptions. Changes in key parameters affect diel O2 saturation and delta18O-O2 curves as follows: (1) an increase in primary production and respiration rates increases the diel range of O2 saturation and delta18O-O2 and decreases the mean delta18O-O2 value; (2) a decrease in the primary production to respiration ratio (P:R) decreases the level of O2 saturation and increases the delta18O-O2 values; (3) an increase in the gas exchange rate decreases the diel range of O2 saturation and delta18O-O2 values and moves the mean O2 saturation and delta18O-O2 values toward atmospheric equilibrium; (4) a decrease in strength of the respiratory isotopic fractionation (alphaR closer to 1) has no effect on O2 saturation and decreases the delta18O-O2 values; (5) an increase in the delta18O of water has no effect on O2 saturation and increases the minimum (daytime) delta18O-O2 value; and (6) an increase in temperature reduces O2 solubility and thus increases the diel range of O2 saturation and delta18O-O2 values. Understanding the interplay between these key parameters makes it easier to decipher the controls on O2 and delta18O-O2, compare aquatic ecosystems, and make quantitative estimates of ecosystem metabolism. The photosynthesis to respiration to gas exchange ratio (P:R:G) is better than the P:R ratio at describing and assessing the vulnerability of aquatic ecosystems under various environmental stressors by providing better constrained estimates of ecosystem metabolism and gas exchange.     

A robust-design formulation of the incidence function model of metapopulation dynamics applied to two species of rails

The incidence function model (IFM) uses area and connectivity to predict metapopulation dynamics. However, false absences and missing data can lead to underestimates of the number of sites contributing to connectivity, resulting in overestimates of dispersal ability and turnovers (extinctions plus colonizations). We extend estimation methods for the IFM by using a hierarchical Bayesian model to account both for false absences due to imperfect detection and for missing data due to sites not surveyed in some years. We compare parameter estimates, measures of metapopulation dynamics, and forecasts using stochastic patch occupancy models (SPOMs) among three IFM models: (1) a Bayesian formulation assuming no false absences and omitting site-year combinations with missing data; (2) a hierarchical Bayesian formulation assuming no false absences but incorporating missing data; and (3) a hierarchical Bayesian formulation allowing for imperfect detection and incorporating missing data. We fit the models to multiyear data sets of occupancy for two bird species that differ in body size and presumed dispersal ability but inhabit the same network of sites: the small Black Rail (Laterallus jamaicensis) and the medium-sized Virginia Rail (Rallus limicola). Incorporating missing data affected colonization parameters and led to lower estimates of dispersal ability for the Black Rail. Detection rates were high for the Black Rail in most years but moderate for the Virginia Rail. Incorporating imperfect detection resulted in higher occupancy and lower turnover rates for both species, with largest effects for the Virginia Rail. Forecasts using SPOMs were sensitive to both missing data and false absences; persistence in models assuming no false absences was more optimistic than from robust models. Our results suggest that incorporating false absences and missing data into the IFM can improve (1) estimates of dispersal ability and the effect of connectivity on colonization, (2) the scaling of extinction risk with patch area, and (3) forecasts of occupancy and turnover rates.     

Health risk assessment for arsenic contaminated soil

This paper describes risk assessment methods for two chronic exposure pathways involving arsenic contaminated soil, namely inhalation of fugitive dust emissions over a lifetime, and inadvertent soil/house dust ingestion. The endpoint in the first case is assumed to be lung cancer and in the second case skin cancer. In order to estimate exposures, inhalation rates and soil/dust ingestion rates are estimated for different age groups; indoor/outdoor time budgets for different age groups are developed; and indoor surface dust and air arsenic concentrations are estimated based on outdoor concentration measurements. Differences observed in indoor/outdoor ratios and arsenic containing dust particle size among different types of communities are noted, as well as possible relationship of particle size to bioavailability. Calculations of risk are presented using cancer potency factors developed by the U.S. Environmental Protection Agency, and uncertainties in these toxicity estimates are described based on: (1) evidence that arsenic may be neither a cancer initiator nor promotor, but may act instead as a late stage carcinogen and (2) evidence that the arsenic dose-response relationship for ingestion may be nonlinear at low doses due to increasing methylation of inorganic arsenic. The first of these considerations influences the relative importance ascribed to arsenic doses in different age groups. The latter consideration indicates that the risk estimates described here are probably very conservative.     

Froude scaling limitations in modeling of turbidity currents

The scaling problem associated with the modeling of turbidity currents has been recognized but is yet to be explored systematically. This paper presents an analysis of the dimensionless governing equations of turbidity currents to investigate the scale effect. Three types of flow conditions are considered: (i) conservative density current; (ii) purely depositional turbidity current; and (iii) mixed erosional/depositional turbidity current. Two controlling dimensionless numbers, the Froude number and the Reynolds number, appear in the non-dimensional governing equations. When densimetric Froude similarity is satisfied, the analysis shows that the results would be scale-invariant for conservative density current under the rough turbulent condition. In the case of purely depositional flows, truly scale-invariant results cannot be obtained, as the Reynolds-mediated scale effects appear in the bottom boundary conditions of the flow velocity and sediment fall velocity. However, the scale effect would be relatively modest. The Reynolds effect becomes more significant for erosional or mixed erosional/depositional turbidity currents as Reynolds-mediated scale effects also appear in the sediment entrainment relation. Numerical simulations have been conducted at three different scales by considering densimetric Froude scaling alone as well as combined densimetric Froude and Reynolds similarity. Simulation results confirm that although the scaling of densimetric Froude number alone can produce scale-invariable results for conservative density currents, variations occur in the case of turbidity currents. The results become scale invariant when densimetric Froude and Reynolds similarities are satisfied simultaneously.     

Regression equations for estimating throughfall nutrient fluxes using wet deposition data and their applicability for simulating the soil acid–base status using the dynamic forest soil–atmosphere model SAFE

Throughfall may contribute large amounts of nutrients to forest soils via the leaching of accumulated dry particulates on the canopy, and by altering incoming precipitation, it may have some control on the acid–base status of the soil. Unfortunately, information about throughfall in forests is sparse and thus, scientists must deal with this gap in knowledge before conducting regional applications of dynamic soil acidification models. The first objective of this paper was to test the possibility of developing regression equations that could allow modellers to estimate throughfall nutrient fluxes using wet deposition nutrient fluxes as input data. The second objective was to test the relative importance of this simplification on regional applications of the dynamic soil–atmosphere model Soil Acidification in Forested Ecosystems (SAFE) using one published application of this model as the base case. Annual throughfall nutrient fluxes were estimated successfully from annual wet deposition fluxes for individual ions. The success of these relationships were however inversely proportional to the intensity at which an ion was involved in exchange reactions: models generally performed better with more conservative ions. The simulation of the soil acid–base status with SAFE suggested that it was appropriate to use the throughfall estimates yielded using the regression equations. Also, testing of the SAFE output using different regression equations in throughfall showed that, in the case of base cations, the key for modelling the soil acid–base status was to produce accurate throughfall estimates of Ca and Mg, and that K had marginal effects. However, a small bias in solution pH was introduced as the balance between alkalinity and acidity in the different categories of deposition appeared to be diverging from the base case (measured) values. The use of our approach at other sites may indicate if there is a systematic bias or not in the regressions. Yet, results suggest that the regression equations are appropriate for the purpose of modelling the soil acid–base status at the scale of the landscape because it assures that the same set of assumptions in throughfall are used for each application.     

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.     

Reconstructing the historic demography of an endangered seabird

Reducing extinction risk for threatened species requires determining which demographic parameters are depressed and causing population declines. Museum collections may constitute a unique, underutilized resource for measuring demographic changes over long time periods using age-ratio analysis. We reconstruct the historic demography of a U.S. federally endangered seabird, the Marbled Murrelet (Brachyramphus marmoratus), from specimens collected approximately 100 years ago for comparison with predictions from comparative analyses and with results from contemporary field studies using both age-ratio analysis and conventional demographic estimators. Reproduction in the late 1800s and early 1900s matched predictions from comparative analysis, but was 8-9 times greater than contemporary estimates, whereas adult survival was unchanged. Historic reproductive rates would support stable populations, but contemporary levels should result in population declines. Contemporary demographic estimates derived from age-ratio analysis were similar to estimates from conventional estimators. Using museum specimens to reconstruct historic demography provides a unique approach to identify causes of decline and to set demographic benchmarks for recovery of endangered species that meet most assumptions of age-ratio analysis.     

Use made in marine ecology of methods for estimating demographic parameters from size/frequency data

Macdonald and Pitcher's method of decomposing a sizefrequency histogram into cohorts (mathematical optimization of the fit of the distribution function to the histogram) has been used to estimate the composition of random samples drawn from populations with known cohort structure. The large-sample behaviour of the method is in accordance with the results of asymptotic theory. With sample sizes typical of those used in many ecological studies, good estimates often could not be obtained without imposing constraints upon the estimation procedure, even when the number of age classes in the population was known. If the number of age classes was not known, it was frequently difficult to determine from small samples. When unconstrained solutions were obtainable, confidence limits about estimates were often very wide. Our results and information in the theoretical literature indicate that if the Petersen method (whereby several modes on a size-frequency histogram are taken to represent single age classes and all age classes to be present) does not work, accurate estimates of demographic parameters are unlikely to be obtainable using more rigorous methods. In view of these difficulties, we recommend that an iptimization method, such as that described by Macdonald and Pitcher, be used to estimate demographic parameters. Standard errors of estimates should be reported. Optimization methods give an indication when the data is inadequate to obtain accurate parameter estimates, either by failing to converge or by placing large standard errors about the estimates. Graphical methods do not give a clear warning of this, and should be avoided except where the modes on the size-frequency histogram are very well separated and sample sizes are large. Often, assumptions must be made about population parameters to enable their estimation. This may involve constraining some parameters to particular values, assuming a fixed relationship between cohort mean sizes and their standard deviations, or by assuming that individuals grow according to a von Bertalanffy curve. Any such assumptions need detailed justification in each case.     

Application of partial order to stream channel assessment at bridge infrastructure for mitigation management

Many environmental systems and infrastructure systems are monitored using a set of indicator values that assess several aspects of site condition. The values of different indicators often convey different comparative messages for the study sites. The comparability between sites is analyzed within the theory of partially-ordered sets and visualized with Hasse diagrams. This paper demonstrates that properties of partially-ordered sets and the conversion of indicator scores into a rank matrix provide a manager or decision-maker with valuable information that would be lost if the monitored indicator data were aggregated only into a single index. Stream stability assessment data from 49 bridge crossing sites in the United States are used to demonstrate these techniques in prioritization and identification of restoration and maintenance needs of stream channels at bridge crossings.     

Interpretation bodennaher Ozonkonzentrationen für die gesundheitliche Bewertung

Stable sunny weather conditions with photochemical smog situations have appeared more frequently during the last few summers in Germany. This became more and more attentive to the public as well as those involved in environment and health politics have recently become more and more attentive to this situation. Meanwhile, a problem of misunderstanding has grown through the use data consisting of different mean values for the interpretation of the ozone health impact on man. The false conception that ozone concentrations measured in rural sites are principally higher than those of inner-city sites has frequently dominated. Using selected examples, this article serves to contribute a better understanding and interpretation of ambient ozone concentrations as related to health impact assessment. The analyses are focused on comparisons between inner-city and rural measurements concerning short-term maximum ozone concentrations, as well as on the problems of selecting air monitoring stations which are appropriate for the ozone exposure and risk assessment.     

Population growth in snow geese: a modeling approach integrating demographic and survey information

There are few analytic tools available to formally integrate information coming from population surveys and demographic studies. The Kalman filter is a procedure that facilitates such integration. Based on a state-space model, we can obtain a likelihood function for the survey data using a Kalman filter, which we may then combine with a likelihood for the demographic data. In this paper, we used this combined approach to analyze the population dynamics of a hunted species, the Greater Snow Goose (Chen caerulescens atlantica), and to examine the extent to which it can improve previous demographic population models. The state equation of the state-space model was a matrix population model with fecundity and regression parameters relating adult survival and harvest rate estimated in a previous capture-recapture study. The observation equation combined the output from this model with estimates from an annual spring photographic survey of the population. The maximum likelihood estimates of the regression parameters from the combined analysis differed little from the values of the original capture-recapture analysis, though their precision improved. The model output was found to be insensitive to a wide range of coefficient of variation (CV) in fecundity parameters. We found a close match between the surveyed and smoothed population size estimates generated by the Kalman filter over an 18-year period, and the estimated CV of the survey (0.078-0.150) was quite compatible with its assumed value (approximately 0.10). When we used the updated parameter values to predict future population size, the model underestimated the surveyed population size by 18% over a three-year period. However, this could be explained by a concurrent change in the survey method. We conclude that the Kalman filter is a promising approach to forecast population change because it incorporates survey information in a formal way compared with ad hoc approaches that either neglect this information or require some parameter or model tuning.