Assessing the status and trend of bat populations across broad geographic regions with dynamic distribution models

Bats face unprecedented threats from habitat loss, climate change, disease, and wind power development, and populations of many species are in decline. A better ability to quantify bat population status and trend is urgently needed in order to develop effective conservation strategies. We used a Bayesian autoregressive approach to develop dynamic distribution models for Myotis lucifugus, the little brown bat, across a large portion of northwestern USA, using a four-year detection history matrix obtained from a regional monitoring program. This widespread and abundant species has experienced precipitous local population declines in northeastern USA resulting from the novel disease white-nose syndrome, and is facing likely range-wide declines. Our models were temporally dynamic and accounted for imperfect detection. Drawing on species-energy theory, we included measures of net primary productivity (NPP) and forest cover in models, predicting that M. lucifugus occurrence probabilities would covary positively along those gradients. Despite its common status, M. lucifugus was only detected during -50% of the surveys in occupied sample units. The overall naive estimate for the proportion of the study region occupied by the species was 0.69, but after accounting for imperfect detection, this increased to -0.90. Our models provide evidence of an association between NPP and forest cover and M. lucifugus distribution, with implications for the projected effects of accelerated climate change in the region, which include net aridification as snowpack and stream flows decline. Annual turnover, the probability that an occupied sample unit was a newly occupied one, was estimated to be low (-0.04-0.14), resulting in flat trend estimated with relatively high precision (SD = 0.04). We mapped the variation in predicted occurrence probabilities and corresponding prediction uncertainty along the productivity gradient. Our results provide a much needed baseline against which future anticipated declines in M. lucifugus occurrence can be measured. The dynamic distribution modeling approach has broad applicability to regional bat monitoring efforts now underway in several countries and we suggest ways to improve and expand our grid-based monitoring program to gain robust insights into bat population status and trend across large portions of North America.     

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.     

Spatial Patterns of Breeding Success of Grizzly Bears Derived from Hierarchical Multistate Models

Conservation programs often manage populations indirectly through the landscapes in which they live. Empirically, linking reproductive success with landscape structure and anthropogenic change is a first step in understanding and managing the spatial mechanisms that affect reproduction, but this link is not sufficiently informed by data. Hierarchical multistate occupancy models can forge these links by estimating spatial patterns of reproductive success across landscapes. To illustrate, we surveyed the occurrence of grizzly bears (Ursus arctos) in the Canadian Rocky Mountains Alberta, Canada. We deployed camera traps for 6 weeks at 54 surveys sites in different types of land cover. We used hierarchical multistate occupancy models to estimate probability of detection, grizzly bear occupancy, and probability of reproductive success at each site. Grizzly bear occupancy varied among cover types and was greater in herbaceous alpine ecotones than in low‐elevation wetlands or mid‐elevation conifer forests. The conditional probability of reproductive success given grizzly bear occupancy was 30% (SE = 0.14). Grizzly bears with cubs had a higher probability of detection than grizzly bears without cubs, but sites were correctly classified as being occupied by breeding females 49% of the time based on raw data and thus would have been underestimated by half. Repeated surveys and multistate modeling reduced the probability of misclassifying sites occupied by breeders as unoccupied to <2%. The probability of breeding grizzly bear occupancy varied across the landscape. Those patches with highest probabilities of breeding occupancy—herbaceous alpine ecotones—were small and highly dispersed and are projected to shrink as treelines advance due to climate warming. Understanding spatial correlates in breeding distribution is a key requirement for species conservation in the face of climate change and can help identify priorities for landscape management and protection. Patrones Espaciales del Éxito Reproductivo de Osos Pardos, Derivados de Modelos Jerárquicos Multi‐Estado     

Improving occupancy estimation when two types of observational error occur: non-detection and species misidentification

Efforts to draw inferences about species occurrence frequently account for false negatives, the common situation when individuals of a species are not detected even when a site is occupied. However, recent studies suggest the need to also deal with false positives, which occur when species are misidentified so that a species is recorded as detected when a site is unoccupied. Bias in estimators of occupancy, colonization, and extinction can be severe when false positives occur. Accordingly, we propose models that simultaneously account for both types of error. Our approach can be used to improve estimates of occupancy for study designs where a subset of detections is of a type or method for which false positives can be assumed to not occur. We illustrate properties of the estimators with simulations and data for three species of frogs. We show that models that account for possible misidentification have greater support (lower AIC for two species) and can yield substantially different occupancy estimates than those that do not. When the potential for misidentification exists, researchers should consider analytical techniques that can account for this source of error, such as those presented here.     

Species traits as predictors of lepidopteran composition in restored and remnant tallgrass prairies.

Restoration ecologists are increasingly turning to the development of trait-filter models, which predict how evolved traits limit species membership within assemblages depending on existing abiotic or biotic constraints, as a tool to explain how species move from a regional species pool into a restored community. Two often untested assumptions of these models, however, are that species traits can reliably predict species' broadscale distribution and that the effects of traits on community membership do not vary between restored and remnant habitats. The goals of this study were to determine whether combinations of ecological traits predispose moth species toward recolonization of restored prairies and to assess the degree to which restored prairies contain moth assemblages comparable with prairie remnants. In 2004, we collected 259 moth species from 13 tallgrass prairie remnants and restorations in central Iowa. Principal components analysis (PCA) was used to identify significant combinations of ecological traits that were shared by groups of moth species. Logistic regression was then employed to test for significant effects of the trait combinations on the frequency of prairie sites occupied by moth species. PCA partitioned moth traits into four axes that explained a total of 81.6% of the variance. Logistic regression detected significant effects for all four PCA axes on the fraction of sites occupied by moths. Species frequently filtered from the regional species pool into prairies were those that had long flight periods and were multivoltine, displayed a feeding preference for legumes but not other forb families, and were regionally abundant but relatively small in body size. Ordination revealed significant differences in moth communities among prairies, suggesting that species traits and habitat characteristics likely interact to create observed patterns of species recolonization of restorations. Thus, the optimal approach to restoring the lepidopteran fauna of tallgrass prairies may involve locating prairie plantings adjacent to habitat remnants.     

Mutualism as reciprocal exploitation: African plant-ants defend foliar but not reproductive structures

The foundation of many plant-ant mutualisms is ant protection of plants from herbivores in exchange for food and/or shelter. While the role of symbiotic ants in protecting plants from stem- and leaf-feeding herbivores has been intensively studied, the relationship between ant defense and measures of plant fitness has seldom been quantified. We studied ant aggression, damage by herbivores and seed predators, and fruit production among Acacia drepanolobium trees occupied by four different acacia-ant species in an East African savanna. Levels of ant aggression in response to experimental disturbance differed strongly among the four species. All four ant species recruited more strongly to new leaf growth on host plants following disturbance, while recruitment to developing fruits was on average an order of magnitude lower. Host plants occupied by more aggressive ant species suffered significantly less vegetative damage from leaf-feeding insects, stem-boring beetles, and vertebrate browsers than host plants occupied by less aggressive ant species. However, there were no differences among fruiting host plants occupied by different ant species in levels of seed predation by bruchid seed predators. Fruit production on host trees was significantly correlated with tree stem diameter but not with the identity of resident ants. Our results demonstrate that defense of host plants may differ substantially among ant species and between vegetative and reproductive structures and that fruit production is not necessarily correlated with high levels of aggression by resident ants.     

Regional Decline of an Iconic Amphibian Associated with Elevation,Land‐Use Change,and Invasive Species

Abstract: Ecological theory predicts that species with restricted geographic ranges will have the highest probability of extinction, but species with extensive distributions and high population densities can also exhibit widespread population losses. In the western United States populations of northern leopard frogs (Lithobates pipiens)—historically one of the most widespread frogs in North America—have declined dramatically in abundance and geographic distribution. To assess the status of leopard frogs in Colorado and evaluate causes of decline, we coupled statewide surveys of 196 historically occupied sites with intensive sampling of 274 wetlands stratified by land use. We used an information‐theoretic approach to evaluate the contributions of factors at multiple spatial extents in explaining the contemporary distribution of leopard frogs. Our results indicate leopard frogs have declined in Colorado, but this decline was regionally variable. The lowest proportion of occupied wetlands occurred in eastern Colorado (2–28%), coincident with urban development and colonization by non‐native bullfrogs (Lithobates catesbeianus). Variables at several spatial extents explained observed leopard frog distributional patterns. In low‐elevation wetlands introduced fishes, bullfrogs, and urbanization or suburbanization associated negatively with leopard frog occurrence, whereas wetland area was positively associated with occurrence. Leopard frogs were more abundant and widespread west of the Continental Divide, where urban development and bullfrog abundance were low. Although the pathogenic chytrid Batrachochytrium dendrobatidis (Bd) was not selected in our best‐supported models, the nearly complete extirpation of leopard frogs from montane wetlands could reflect the individual or interactive effects of Bd and climate patterns. Our results highlight the importance of considering multiple, competing hypotheses to explain species declines, particularly when implicated factors operate at different spatial extents.     

Synchronized breeding events in sympatric marine invertebrates: role of behavior and fine temporal windows in maintaining reproductive isolation

While breeding synchrony among conspecifics is increasingly well understood with regards to the reproductive success of vertebrate and invertebrate taxa, the occurrence of simultaneous multispecies breeding events remains intriguing. The fairly recent discovery of mass annual spawnings in reef corals has provided a first glimpse at putative strategies of reproductive isolation during such events. However, the mechanisms and advantages of same-day heterospecific breeding are still poorly understood and have not yet been investigated in non-coral taxa with different life history strategies. In an effort to bridge this gap, we investigated spawning periodicity and synchrony among 26 sympatric species of free-spawning, capsule-laying, and brood-protecting macroinvertebrates belonging to six different phyla. Twenty-four of these species released gametes or larvae between early March and late April. We analyzed the events over fine temporal scales to test the hypothesis that breeding activities were not random in time or relative to each other. We found that the two main reproductive pulses followed a lunar periodicity and that consistent species- and gender-specific modulations in the timing of spawning occurred during same-day episodes involving up to six free-spawning species. Mass spawning accounts from the literature were reviewed and compared. This work suggests that many species participate in synchronous heterospecific spawning events either because they respond to the same environmental cues or rely on cross-cueing and that reproductive isolation is favored by species-specific circadian patterns, spawning behaviors and cross-gender signaling.     

Building statistical models to analyze species distributions.

Models of the geographic distributions of species have wide application in ecology. But the nonspatial, single-level, regression models that ecologists have often employed do not deal with problems of irregular sampling intensity or spatial dependence, and do not adequately quantify uncertainty. We show here how to build statistical models that can handle these features of spatial prediction and provide richer, more powerful inference about species niche relations, distributions, and the effects of human disturbance. We begin with a familiar generalized linear model and build in additional features, including spatial random effects and hierarchical levels. Since these models are fully specified statistical models, we show that it is possible to add complexity without sacrificing interpretability. This step-by-step approach, together with attached code that implements a simple, spatially explicit, regression model, is structured to facilitate self-teaching. All models are developed in a Bayesian framework. We assess the performance of the models by using them to predict the distributions of two plant species (Proteaceae) from South Africa's Cape Floristic Region. We demonstrate that making distribution models spatially explicit can be essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results. Adding hierarchical levels to the models has further advantages in allowing human transformation of the landscape to be taken into account, as well as additional features of the sampling process.     

The Reliability of Using Population Viability Analysis for Risk Classification of Species

I examine whether or not it is appropriate to use extinction probabilities generated by population viability analyses, based on best estimates for model parameters, as criteria for listing species in Red Data Book categories as recently proposed by the World Conservation Union. Such extinction probabilities are influenced by how accurately model parameters are estimated and by how accurately the models depict actual population dynamics. I evaluate the effect of uncertainty in parameter estimation through simulations. Simulations based on Steller sea lions were used to evaluate bias and precision in estimates of probability of extinction and to consider the performance of two proposed classification schemes. Extinction time estimates were biased (because of violation of the assumption of stable age distribution) and underestimated the variability of probability of extinction for a given time (primarily because of uncertainty in parameter estimation). Bias and precision in extinction probabilities are important when these probabilities are used to compare the risk of extinction between species. Suggestions are given for population viability analysis techniques that incorporate parameter uncertainty. I conclude that testing classification schemes with simulations using quantitative performance objectives should precede adoption of quantitative listing criteria.     

Day/night vertical distribution of euphausiids in the eastern tropical Pacific

Between March 23 and April 4, 1981, samples were taken in the eastern tropical Pacific. The day/night vertical distribution of euphausiid species and biomass are described and contrasted in detail on two eastern tropical Pacific stations, the DOME station, in a region of continuous upwelling and the BIOSTAT station, in a nonupwelling area. The effects of various biological parameters, such as temperature, salinity and oxygen concentrations on the distributions of the species are examined. The numbers of euphausiids m^-2 on both stations were highest during the day, indicating that avoidance of the sampler was not a problem. During the day the largest concentration of adult euphausiids was between 300 and 350 m whereas the juveniles were concentrated between 170 and 80 m on both stations. Very few individuals were found within the oxygen minimum layer, but low concentrations of some species were found below the oxygen minimum down to 1 000 m. At night the euphausiid concentration migrated upward into the mixed layer (20 to 30 m) at BIOSTAT and to the base of the mixed layer at the DOME. Significant differences in the night depths of the species were found on both stations. The oxygen minimum layer appeared to act as a barrier to the vertical distribution of all species. Only two species were found in water with an oxygen concentration of <0.1 ml O₂ l^-1. Twentyone species of euphausiids were found on the two stations but the adult population was dominated by only two or three species on both stations. The reproductive state of the species suggested that some species reproduced earlier on the DOME than on BIOSTAT. Analysis of the depth distribution by cluster analysis showed that the most abundant species occupied different depths during the night and day at BIOSTAT but the two most abundant species were concentrated at the same depth at the DOME station although portions of each species population occupied different pelagic zones.     

Accounting for Uncertainty in Risk Assessment: Case Study of Hector's Dolphin Mortality due to Gillnet Entanglement

Abstract: Uncertainties about biological data and human effects often delay decisions on management of endangered species. Some decision makers argue that uncertainty about the risk posed to a species should lead to precautionary decisions, whereas others argue for delaying protective measures until there is strong evidence that a human activity is having a serious effect on the species. We have developed a method that incorporates uncertainty into the estimate of risk so that delays in action can be reduced or eliminated. We illustrate our method with an actual situation of a deadlock over how to manage Hector's dolphin ( Cephalorhychus hectori ). The management question is whether sufficient risk is posed to the dolphins by mortalities in gillnets to warrant regulating the fisheries. In our quantitative risk assessment, we use a population model that incorporates both demographic ( between-individual) and environmental ( between-year) stochasticity. We incorporate uncertainty in estimates of model parameters by repeatedly running the model for different combinations of survival and reproductive rates. Each value is selected at random from a probability distribution that represents the uncertainty in estimating that parameter. Before drawing conclusions, we perform sensitivity analyses to see whether model assumptions alter conclusions and to recommend priorities for future research. In this example, uncertainty did not alter the conclusion that there is a high risk of population decline if current levels of gillnet mortality continue. Sensitivity analyses revealed this to be a robust conclusion. Thus, our analysis removes uncertainty in the scientific data as an excuse for inaction.     

Effects of uncertainty and variability on population declines and IUCN Red List classifications

The International Union for Conservation of Nature (IUCN) Red List Categories and Criteria is a quantitative framework for classifying species according to extinction risk. Population models may be used to estimate extinction risk or population declines. Uncertainty and variability arise in threat classifications through measurement and process error in empirical data and uncertainty in the models used to estimate extinction risk and population declines. Furthermore, species traits are known to affect extinction risk. We investigated the effects of measurement and process error, model type, population growth rate, and age at first reproduction on the reliability of risk classifications based on projected population declines on IUCN Red List classifications. We used an age‐structured population model to simulate true population trajectories with different growth rates, reproductive ages and levels of variation, and subjected them to measurement error. We evaluated the ability of scalar and matrix models parameterized with these simulated time series to accurately capture the IUCN Red List classification generated with true population declines. Under all levels of measurement error tested and low process error, classifications were reasonably accurate; scalar and matrix models yielded roughly the same rate of misclassifications, but the distribution of errors differed; matrix models led to greater overestimation of extinction risk than underestimations; process error tended to contribute to misclassifications to a greater extent than measurement error; and more misclassifications occurred for fast, rather than slow, life histories. These results indicate that classifications of highly threatened taxa (i.e., taxa with low growth rates) under criterion A are more likely to be reliable than for less threatened taxa when assessed with population models. Greater scrutiny needs to be placed on data used to parameterize population models for species with high growth rates, particularly when available evidence indicates a potential transition to higher risk categories.     

Estimation of abundance from presence–absence maps using cluster models

A presence–absence map consists of indicators of the occurrence or nonoccurrence of a given species in each cell over a grid, without counting the number of individuals in a cell once it is known it is occupied. They are commonly used to estimate the distribution of a species, but our interest is in using these data to estimate the abundance of the species. In practice, certain types of species (in particular flora types) may be spatially clustered. For example, some plant communities will naturally group together according to similar environmental characteristics within a given area. To estimate abundance, we develop an approach based on clustered negative binomial models with unknown cluster sizes. Our approach uses working clusters of cells to construct an estimator which we show is consistent. We also introduce a new concept called super-clustering used to estimate components of the standard errors and interval estimators. A simulation study is conducted to examine the performance of the estimators and they are applied to real data.     

Estimating species richness and accumulation by modeling species occurrence and detectability

A statistical model is developed for estimating species richness and accumulation by formulating these community-level attributes as functions of model-based estimators of species occurrence while accounting for imperfect detection of individual species. The model requires a sampling protocol wherein repeated observations are made at a collection of sample locations selected to be representative of the community. This temporal replication provides the data needed to resolve the ambiguity between species absence and nondetection when species are unobserved at sample locations. Estimates of species richness and accumulation are computed for two communities, an avian community and a butterfly community. Our model-based estimates suggest that detection failures in many bird species were attributed to low rates of occurrence, as opposed to simply low rates of detection. We estimate that the avian community contains a substantial number of uncommon species and that species richness greatly exceeds the number of species actually observed in the sample. In fact, predictions of species accumulation suggest that even doubling the number of sample locations would not have revealed all of the species in the community. In contrast, our analysis of the butterfly community suggests that many species are relatively common and that the estimated richness of species in the community is nearly equal to the number of species actually detected in the sample. Our predictions of species accumulation suggest that the number of sample locations actually used in the butterfly survey could have been cut in half and the asymptotic richness of species still would have been attained. Our approach of developing occurrence-based summaries of communities while allowing for imperfect detection of species is broadly applicable and should prove useful in the design and analysis of surveys of biodiversity.     

Benefits of large broods by higher chick survival and better territories in a precocial shorebird

When reproductive success is constant in one breeding phase, different tactics that increase variation in reproductive success among individuals may evolve in other phases. For instance, in shorebirds, which usually have a limited clutch size of four eggs, variation in reproductive tactics among individuals is expected either before egg-laying (e.g. diverse mating systems) or after hatching of the young (e.g. diverse parental care). In this paper, I studied the pied avocet (Recurvirostra avosetta), a shorebird with a modal clutch size of four eggs, to test whether post-hatch chick adoption as an alternative tactic can be linked to increased variation in annual reproductive success. When predation was high, naturally adopting pairs produced more filial fledglings than did pairs not adopting chicks and not losing chicks to adoption. The number of filial fledglings increased with the number of adopted young, possibly through diluting the chances of predation on filial young. Experimental chick addition did not lead to more fledged young due to low brood integrity as shown by the frequent loss of chicks from some experimental broods. When predation was low, larger broods occupied feeding territories with higher prey abundance than smaller broods, possibly due to their dominance over smaller ones. Pairs that lost chicks to adoption (donors) fledged as many filial young in their broods as did non-adopters/non-donors, whereas the total number of donors’ filial fledglings, including those raised in adopting broods, approached that of adopters. These findings show, for the first time, that post-hatch alternative reproductive tactics can lead to variation in annual reproductive success and to higher success for some pairs even in species where past adaptations limit variation in reproductive success in a certain phase of reproduction.     

General methods for sensitivity analysis of equilibrium dynamics in patch occupancy models

Sensitivity analysis is a useful tool for the study of ecological models that has many potential applications for patch occupancy modeling. Drawing from the rich foundation of existing methods for Markov chain models, I demonstrate new methods for sensitivity analysis of the equilibrium state dynamics of occupancy models. Estimates from three previous studies are used to illustrate the utility of the sensitivity calculations: a joint occupancy model for a prey species, its predators, and habitat used by both; occurrence dynamics from a well-known metapopulation study of three butterfly species; and Golden Eagle occupancy and reproductive dynamics. I show how to deal efficiently with multistate models and how to calculate sensitivities involving derived state variables and lower-level parameters. In addition, I extend methods to incorporate environmental variation by allowing for spatial and temporal variability in transition probabilities. The approach used here is concise and general and can fully account for environmental variability in transition parameters. The methods can be used to improve inferences in occupancy studies by quantifying the effects of underlying parameters, aiding prediction of future system states, and identifying priorities for sampling effort.     

Multimethod,multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves

In many cases, the first step in large‐carnivore management is to obtain objective, reliable, and cost‐effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators over large areas is difficult, and the data have a high level of uncertainty. We devised a practical multimethod and multistate modeling approach based on Bayesian hierarchical‐site‐occupancy models that combined multiple survey methods to estimate different population states for use in monitoring large predators at a regional scale. We used wolves (Canis lupus) as our model species and generated reliable estimates of the number of sites with wolf reproduction (presence of pups). We used 2 wolf data sets from Spain (Western Galicia in 2013 and Asturias in 2004) to test the approach. Based on howling surveys, the naïve estimation (i.e., estimate based only on observations) of the number of sites with reproduction was 9 and 25 sites in Western Galicia and Asturias, respectively. Our model showed 33.4 (SD 9.6) and 34.4 (3.9) sites with wolf reproduction, respectively. The number of occupied sites with wolf reproduction was 0.67 (SD 0.19) and 0.76 (0.11), respectively. This approach can be used to design more cost‐effective monitoring programs (i.e., to define the sampling effort needed per site). Our approach should inspire well‐coordinated surveys across multiple administrative borders and populations and lead to improved decision making for management of large carnivores on a landscape level. The use of this Bayesian framework provides a simple way to visualize the degree of uncertainty around population‐parameter estimates and thus provides managers and stakeholders an intuitive approach to interpreting monitoring results. Our approach can be widely applied to large spatial scales in wildlife monitoring where detection probabilities differ between population states and where several methods are being used to estimate different population parameters.     

Variability in Population Abundance and the Classification of Extinction Risk

Abstract: Classifying species according to their risk of extinction is a common practice and underpins much conservation activity. The reliability of such classifications rests on the accuracy of threat categorizations, but very little is known about the magnitude and types of errors that might be expected. The process of risk classification involves combining information from many sources, and understanding the quality of each source is critical to evaluating the overall status of the species. One common criterion used to classify extinction risk is a decline in abundance. Because abundance is a direct measure of conservation status, counts of individuals are generally the preferred method of evaluating whether populations are declining. Using the thresholds from criterion A of the International Union for Conservation of Nature (IUCN) Red List (critically endangered, decline in abundance of >80% over 10 years or 3 generations; endangered, decline in abundance of 50–80%; vulnerable, decline in abundance of 30–50%; least concern or near threatened, decline in abundance of 0–30%), we assessed 3 methods used to detect declines solely from estimates of abundance: use of just 2 estimates of abundance; use of linear regression on a time series of abundance; and use of state‐space models on a time series of abundance. We generated simulation data from empirical estimates of the typical variability in abundance and assessed the 3 methods for classification errors. The estimates of the proportion of falsely detected declines for linear regression and the state‐space models were low (maximum 3–14%), but 33–75% of small declines (30–50% over 15 years) were not detected. Ignoring uncertainty in estimates of abundance (with just 2 estimates of abundance) allowed more power to detect small declines (95%), but there was a high percentage (50%) of false detections. For all 3 methods, the proportion of declines estimated to be >80% was higher than the true proportion. Use of abundance data to detect species at risk of extinction may either fail to detect initial declines in abundance or have a high error rate.     

稀土尾矿区土壤重金属污染与优势植物累积特征

矿山废弃地不仅占用大量土地,而且还是严重的污染源,因此,矿山废弃地的生态恢复己成为一项紧迫而重要的研究课题。对广东省河源市和平县下车镇内的稀土矿区土壤的重金属污染情况进行调查,并对该区优势植物对重金属的富集特征进行分析,以期对稀土尾矿区的生态系统的恢复和重建提供理论依据。主要研究的3种植物分别是：马唐草（Digitaria sanguinalis）,香根草（Vetiveria zizanioides）,望江南（Cassia occidentalis）。采用原子吸收分光光度法测定稀土矿区废弃地土壤和植被中Mn、Pb、Zn的含量,并计算优势植物对重金属的生物富集系数BAC（Biological Accumulating Coefficient）和生物转移系数BTC（Biological Transfer Coefficient）。结果表明：研究区域的土壤中重金属含量Mn、Pb、Zn的平均含量均超出广东省土壤背景值和中国土壤背景值,土壤受Mn污染最严重,其次是Pb、Zn的污染。3种草本植物对于Pb的BAC和BTC均小于1,说明这3种植物对Pb的富集和运输能力都很弱。香根草对于Mn和Zn的BAC分别为0.9和0.4,小于1,BTC分别为3.7和1.1,大于1,说明香根草对Mn和Zn的富集能力不强,但吸收后的运输能力很强。马唐草和望江南2种植物对于Mn和Zn的BAC和BTC均大于1,说明它们对重金属Mn和Zn具有较强的吸收和转移能力,是Mn和Zn的超富集植物。马唐草覆盖率高,抗病虫能力强,可作为该矿区生态恢复的先锋植物,望江南可以间作种植。