Estimating survival rates of uncatchable animals: the myth of high juvenile mortality in reptiles

Survival rates of juvenile reptiles are critical population parameters but are difficult to obtain through mark-recapture programs because these small, secretive animals are rarely caught. This scarcity has encouraged speculation that survival rates of juveniles are very low, and we test this prediction by estimating juvenile survival rates indirectly. A simple mathematical model calculates the annual juvenile survival rate needed to maintain a stable population size, using published data on adult survival rates, reproductive output, and ages at maturity in 109 reptile populations encompassing 57 species. Counter to prediction, estimated juvenile survival rates were relatively high (on average, only about 13% less than those of conspecific adults) and highly correlated with adult survival rates. Overall, survival rates during both juvenile and adult life were higher in turtles than in snakes, and higher in snakes than in lizards. As predicted from life history theory, rates of juvenile survival were higher in species that produce large offspring, and higher in viviparous squamates than in oviparous species. Our analyses challenge the widely held belief that juvenile reptiles have low rates of annual survival and suggest instead that sampling problems and the elusive biology of juvenile reptiles have misled researchers in this respect.     

Geospatial analysis of vulnerable beach-foredune systems from decadal time series of lidar data

Time series of lidar data, acquired over the past decade along the North American East Coast, provide opportunities to gain new insights into 3D evolution of barrier islands and their beach and dune systems. GIS-based per grid cell statistics and map algebra was applied to time series of Digital Surface Models representing two sections of North Carolina barrier islands to quantify elevation change trends, map dynamic and stable locations, identify new and lost buildings, measure relative volume evolution in the beach and foredune systems and analyze shoreline dynamics. Results show a relatively small stable core in both study areas, with beaches and the ocean side of the dunes exhibiting systematic high rates of elevation loss while areas landward from the dunes increase slightly in elevation. Significant number of new homes have been built at locations with very small core surface elevation, and homes built within the shoreline dynamics band have already been lost. The raster-based methodology used in this study can be applied to perform similar analyses in other coastal areas where time series of lidar data are available.     

Impact of time series data on calibration and prediction uncertainty for a deterministic hydrogeochemical model

Model calibration is fundamental in applications of deterministic process-based models. Uncertainty in model predictions depends much on the input data and observations available for model calibration. Here we explored how model predictions (forecasts) and their uncertainties vary with the length of time series data used in calibration. As an example we used the hydrogeochemical model MAGIC and data from Birkenes, a small catchment in southern Norway, to simulate future water chemistry under a scenario of reduced acid deposition. A Bayesian approach with a Markov Chain Monte Carlo (MCMC) technique was used to calibrate the model to different lengths of observed data (4–29 years) and to estimate the prediction uncertainty each calibration. The results show that the difference between modelled and observed water chemistry (calibration goodness of fit) in general decreases with increasing length of the time series used in calibration. However, there are considerable differences for different time series of the same length. The results also show that the uncertainties in predicted future acid neutralizing capacity were lowest (i.e. the distribution peak narrowest) when using the longest time series for calibration. As for calibration success, there were considerable differences between the future distributions (prediction uncertainty) for the different calibrations.     

Bayesian estimation of age-specific bird nest survival rates with categorical covariates

The populations of many North American landbirds are showing signs of declining. Gathering information on breeding productivity allows critical assessment of population performance and helps identify good habitat-management practices. He (Biometrics (2003) 59 962–973) proposed a Bayesian model to estimate the age-specific nest survival rates. The model allows irregular visiting schedule under the assumption that the observed nests have homogeneous nest survival. Because nest survival studies are often conducted in different sites and time periods, it is not realistic to assume homogeneous nest survival. In this paper, we extend He’s model by incorporating these factors as categorical covariates. The simulation results show that the Bayesian hierarchical model can produce satisfactory estimates on nest survival and capture different factor effects. Finally the model is applied to a Missouri red-winged blackbird data set.     

Estimating the extinction date of the thylacine with mixed certainty data

The thylacine (Thylacinus cynocephalus), one of Australia's most characteristic megafauna, was the largest marsupial carnivore until hunting, and potentially disease, drove it to extinction in 1936. Although thylacines were restricted to Tasmania for 2 millennia prior to their extinction, recent so‐called plausible sightings on the Cape York Peninsula in northern Queensland have emerged, leading some to speculate the species may have persisted undetected. We compiled a data set that included physical evidence, expert‐validated sightings, and unconfirmed sightings up to the present day and implemented a range of extinction models (focusing on a Bayesian approach that incorporates all 3 types of data by modeling valid and invalid sightings as independent processes) to evaluate the likelihood of the thylacine's persistence. Although the last captive individual died in September 1936, our results suggested that the most likely extinction date would be 1940. Our other extinction models estimated the thylacine's extinction date between 1936 and 1943, and the most optimistic scenario indicated that the species did not persist beyond 1956. The search for the thylacine, much like similar efforts to rediscover other recently extinct charismatic taxa, is likely to be fruitless, especially given that persistence on Tasmania would have been no guarantee the species could reappear in regions that had been unoccupied for millennia. The search for the thylacine may become a rallying point for conservation and wildlife biology and could indirectly help fund and support critical research in understudied areas such as Cape York. However, our results suggest that attempts to rediscover the thylacine will be unsuccessful and that the continued survival of the thylacine is entirely implausible based on most current mathematical theories of extinction.     

Age-dependent survival analyzed with Bayesian models of mark-recapture data

We describe a Bayesian random effects model of mark-recapture data that accounts for age-dependence in survival and individual heterogeneity in capture probabilities and survival. The model is applied to data on the Glanville fritillary butterfly (Melitaea cinxia) collected from a population enclosed in a large cage in the field. The cage population consisted of a mixture of butterflies originating from newly established and old populations in a large metapopulation in the Aland Islands in Finland. The explanatory variables in the model included the effects of temperature, sex, and population type (new vs. old) on capture probabilities, and the effects of age, sex, population type, and day vs. night on survival. We found that mortality rate increased with age, that mortality rate was much higher during the day than during the night, and that the life span of females originating from newly established populations was shorter than the life span of females from old populations. Capture probability decreased with increasing temperature and decreased with increasing mobility of individuals.     

Estimating an homogeneous series of a population abundance indicator despite changes in data collection procedure: A hierarchical Bayesian modelling approach

Abundance indicators are required both to assess and to manage wild populations. As new techniques are developed and teams in charge of gathering the data change, data collection procedures (DCPs) can evolve in space and time. How to estimate an homogeneous series of abundance indicator despite changes in DCP? To tackle this question a hierarchical Bayesian modelling (HBM) approach is proposed. It integrates multiple DCPs in order to derive a single abundance indicator that can be compared over space and time irrespective of the DCP used. Compared to single DCP models, it takes further advantage for abundance estimation of the joint treatment of a larger set of spatio-temporal units. After presenting the general formulation of our HBM approach, it is applied to the juvenile Atlantic salmon (Salmo salar L.) population of the River Nivelle (France). Posterior model checking, using χ² discrepancy measure, do not reveal any inadequacy between the model and the data. Despite a change in the DCP used (successive removals to catch-per-unit of effort), a unique abundance indicator for the 425 spatio-temporal units (site × year) sampled over twenty-four years (1985-2008) is estimated. The HBM approach allows the assessment of precision of the abundance estimates and shows variation between DCPs: a reduction in precision is observed during the most recent years (2005-2008) when only the catch-per-unit of effort DCP was used. The merits and generality of our HBM approach are discussed. We contend it extends previous single DCP models or inter-calibration of two DCPs, and it could be applied to a wide range of specific situations (taxon and DCPs).     

Estimating age from recapture data: integrating incremental growth measures with ancillary data to infer age-at-length

Estimating the age of individuals in wild populations can be of fundamental importance for answering ecological questions, modeling population demographics, and managing exploited or threatened species. Significant effort has been devoted to determining age through the use of growth annuli, secondary physical characteristics related to age, and growth models. Many species, however, either do not exhibit physical characteristics useful for independent age validation or are too rare to justify sacrificing a large number of individuals to establish the relationship between size and age. Length-at-age models are well represented in the fisheries and other wildlife management literature. Many of these models overlook variation in growth rates of individuals and consider growth parameters as population parameters. More recent models have taken advantage of hierarchical structuring of parameters and Bayesian inference methods to allow for variation among individuals as functions of environmental covariates or individual-specific random effects. Here, we describe hierarchical models in which growth curves vary as individual-specific stochastic processes, and we show how these models can be fit using capture-recapture data for animals of unknown age along with data for animals of known age. We combine these independent data sources in a Bayesian analysis, distinguishing natural variation (among and within individuals) from measurement error. We illustrate using data for African dwarf crocodiles, comparing von Bertalanffy and logistic growth models. The analysis provides the means of predicting crocodile age, given a single measurement of head length. The von Bertalanffy was much better supported than the logistic growth model and predicted that dwarf crocodiles grow from 19.4 cm total length at birth to 32.9 cm in the first year and 45.3 cm by the end of their second year. Based on the minimum size of females observed with hatchlings, reproductive maturity was estimated to be at nine years. These size benchmarks are believed to represent thresholds for important demographic parameters; improved estimates of age, therefore, will increase the precision of population projection models. The modeling approach that we present can be applied to other species and offers significant advantages when multiple sources of data are available and traditional aging techniques are not practical.     

Spectral analysis of forest model time series

The use of spectral analysis to elucidate the cyclic behavior in time series generated by a forest stand growth simulation model is discussed. A stand-level simulator, FORET, for an Appalachian deciduous forest is described. An estimate of the power spectral density of the total biomass time series is calculated. The power spectral density estimate indicates a dominant cyclic behavior with a period of about 200 years. In addition the spectral density is approximately bandlimited. This characteristic makes possible the application of the sampling theorem for analysis of sampling rates.     

Comparison of sequential monitoring methods for environmental time series with stochastic cycle

The paper compares recursive methods for detecting change points in environmental time series. Timely identification of peaks and troughs is important for planning defense actions and preventing risks. We consider linear nonparametric methods, such as time-varying coefficients, double exponential smoothers and prediction error statistics. These methods are often used in surveillance, forecasting and control, and their common features are sequential computation and exponential weighting of data. The new approach proposed here is to select their coefficients by maximizing the difference between subsequent peaks and troughs detected on past data. We compare the methods with applications to meteorological, astronomical and ecological data, and Monte-Carlo simulations.     

Modelling non-proportional hazard for survival data with different systematic components

Environmental and Ecological Statistics - We propose a new extended regression model based on the logarithm of the generalized odd log-logistic Weibull distribution with four systematic components...     

Explanation of fluctuations in water use time series

This paper presents techniques for studying the influence of the climatic and other variables for the explanation of the water use with an example of time series in Gainesville, Florida. A statistical methodology is described for separating the different time scale components in time series of water use, namely, long term component, seasonal component, and short term component. We analyze each component separately and we prove that the temperature, precipitation, soil temperature, and relative humidity time series are the main climatic factors for the explanation of the long term, seasonal and short term component of the water use time series. Part of the residuals derived from the linear regression of the long term component of the water use can be explained by the unemployment rate. We also show that with the decomposition of the water use time series the explanation of the water use has been improved approximately two times. The explanation of the long term component of water use by the long term regional weather parameters can enable us to the long term regional prediction of the water resources availabilities. This methodology can be applied for studying the water use time series in other locations, as well.     

月径流时间序列的混沌特性分析

研究根据混沌理论,分别采用功率谱分析方法、G-P关联维识别合水水库月径流量的混沌特性,并且得出主要的混沌特性指标.以Tanks嵌入定理为理论基础,进行短期预测,能够取得精度较高的预测结果.研究表明:月径流序列存在一定的混沌特性,其最小嵌入维数在m=8对应的吸引子维数D=-0.95,最大李雅谱诺夫指数σ=0.325,采用加权一阶权域方法预测时,预测最大时限为4个月,其预测精度均在5%以内.     

Estimating Sonoran pronghorn abundance and survival with fecal DNA and capture–recapture methods

Population abundance estimates are important for management but can be challenging to determine in low‐density, wide‐ranging, and endangered species, such as Sonoran pronghorn (Antilocapra americana sonoriensis). The Sonoran pronghorn population has been increasing; however, population estimates are currently derived from a biennial aerial count that does not provide survival or recruitment estimates. We identified individuals through noninvasively collected fecal DNA and used robust‐design capture–recapture to estimate abundance and survival for Sonoran pronghorn in the United States from 2013 to 2014. In 2014 we generated separate population estimates for pronghorn gathered near 13 different artificial water holes and for pronghorn not near water holes. The population using artificial water holes had 116 (95% CI 102–131) and 121 individuals (95% CI 112–132) in 2013 and 2014, respectively. For all locations, we estimated there were 144 individuals (95% CI 132–157). Adults had higher annual survival probabilities (0.83, 95% CI 0.69–0.92) than fawns (0.41, 95% CI 0.21–0.65). Our use of targeted noninvasive genetic sampling and capture–recapture with Sonoran pronghorn fecal DNA was an effective method for monitoring a large proportion of the population. Our results provided the first survival estimates for this population in over 2 decades and precise estimates of the population using artificial water holes. Our method could be used for targeted sampling of broadly distributed species in other systems, such as in African savanna ecosystems, where many species congregate at watering sites.     

Estimating annual survival in sexually dimorphic species from proportions of first-year birds

We estimated the annual adult survival (S) of several species of temperate and tropical sexually dimorphic tanagers and manakins from the proportions of first-year and older individuals in museum collections. In the case of the sexually dimorphic species included here, young males through their first year resemble females and can be distinguished from older males. We assumed that the sex ratio among first-year birds is even and estimated the number of immature females (IF) in a sample as equal to the number of immature males (IM), using sex information on museum labels. The number of adult females (AF) is the total number of females (F) minus IF, and survival can be estimated by S = AF/F. We used a Monte Carlo resampling approach to estimate standard errors and 1% and 99% confidence limits on S for samples of size N, based on 1000 sets of N randomly drawn individuals and observed proportions of the F, IM, and AM classes. Estimates of annual survival (S) were 0.60-0.73 in four North American species of Piranga tanagers and 0.50 and 0.62 in two montane Central American species. Values of S in lowland species of Habia and Ramphocelus tanagers ranged between 0.68 and 0.82. Annual survival in Pipra manakins was 0.53-0.68, and values for two Manacus species were 0.69 and 0.70. Where estimates of apparent survival (phi) from capture-recapture studies were available, the two methods produced correlated results with a tendency for phi < S. Among tanagers, only Ramphocelus dimidiatus (0.82) and Piranga bidentata (0.50) stood out from the other species, for which S averaged 0.66 +/- 0.05 (SD). Annual survival rates of temperate and tropical species overlapped broadly in this analysis, reinforcing the conclusion that distinctive life-history traits of temperate vs. tropical species cannot be explained completely by the adjustment of reproductive investment in relation to adult survival and the expectation of future life.     

Regression analysis of forest inventory data with time and space dependencies

In this paper the data of a forest health inventory are analyzed. Since 1983 the degree of defoliation, together with various explanatory variables (covariates) concerning stand, site, soil and weather, are recorded by the second of the two authors, in the forest district of Rothenbuch (Spessart, Bavaria). The focus is on the space and time dependencies of the data. The mutual relationship of space-time functions and the set of covariates is evaluated. For this we use generalized linear models (GLMs) for ordinal response variables and semiparametric estimation approaches. By using goodness-of-fit measures it turns out that (i) the contribution of space-time functions is quantitatively comparable with that of the set of covariates, (ii) the contribution of space-time functions is small compared with the contribution of a set of variables describing the last-year and neighboring response values. By applying appropriate residual methods a detailed analysis of the individual sites in the area can be carried out. This analysis reveals where the predictive power of the covariates fail to explain the observed defoliation.     

Cross-correlation bias in lag analysis of aquatic time series

Cross-correlation analysis is the most valuable and widely used statistical tool for evaluating the strength and direction of time-lagged relationships between ecological variables. Although it is well understood that temporal autocorrelation can inflate estimates of cross correlations and cause high rates of incorrectly concluding that lags exist among time series (i.e. type I error), in this study we show that a problem we term intra-multiplicity can cause substantial bias in cross-correlation analysis even in the absence of autocorrelation. Intra-multiplicity refers to the numerous time lags examined and cross-correlation coefficients computed within a pair of time series during cross-correlation analysis. We show using Monte Carlo simulations that intra-multiplicity can spuriously inflate estimates of cross correlations by identifying incorrect time lags. Further, unlike autocorrelation, which generally identifies lags close to the true lag, intra-multiplicity can erroneously identify lags anywhere in the time series and commonly results in a direction change of the correlation (i.e. positive or negative). Using Monte Carlo simulations we develop formulas that quantify the bias introduced by intra-multiplicity as a function of sample size, true cross correlation between the series, and the number of time lags examined. A priori these formulas enable researchers to determine the sample size needed to minimize the biases introduced by intra-multiplicity. A posteriori the formulas can be used to predict the expected bias and type I error rate associated with the data at hand, as well as the maximum number of time lags that can be analyzed to minimize the effects of intra-multiplicity. We examine the relationship between commercial catch of chum salmon and surface temperatures of the North Pacific (1925–1992) to illustrate the problems of intra-multiplicity in fisheries studies and the application of our formulas. These analyses provide a more robust framework to assess the temporal relationships between ecological variables. Received: 28 July 2000 / Accepted: 6 December 2000     

Estimating digestion time in gelatinous predators: a methodological comparison with the scyphomedusa Aurelia aurita

In order to quantify the trophic impact of gelatinous predators, digestion time estimates are commonly applied to counts of prey in the guts. Three primary approaches are used, the Manual-feeding, Natural-feeding and Steady-state methods; these differ in methodology and their underlying assumptions. The criteria used to define the end-point of digestion, and the resolution at which digestion progress is observed, also vary across studies. To understand the impact of such differences, we estimate digestion times of the scyphomedusa Aurelia aurita fed adult females of the copepod Acartia tonsa using these various approaches. We find ~fourfold differences which can be attributed to bias towards the slowest rates of digestion by some end-point criteria, and overestimation from low observation resolution. Artificial manipulation and the degree to which swimming and feeding behaviour are natural may also influence estimates. We provide recommendations for those quantifying digestion times of Aurelia aurita medusae and gelatinous predators.     

Estimating the status of nearshore rockfish (Sebastes spp.) populations with length frequency data.

Ecologists often point to excessive truncation of a population's size-structure as a deleterious effect of exploitation, yet the effect of this truncation on population persistence is seldom quantified. While persistence of marine populations requires maintenance of a sufficient level of lifetime reproduction, fishing reduces lifetime reproduction by increasing the total mortality rate, preventing individuals from growing old, large, and highly fecund. We employ a new method of estimating changes in lifetime egg production (LEP) using two samples of the size structure, one in the past and one current, to assess persistence of five species of nearshore rockfish (Sebastes spp.) in California and Oregon, U.S.A. Using length frequency data from catch in the recreational fishery, we estimate that since 1980, four of the five rockfish species considered have experienced declines in LEP to levels that suggest that persistence is impaired. When changes in LEP were estimated for subsets of the data corresponding to neighboring geographical regions, differences in LEP levels were apparent in the neighboring regions, implying that the effects of fishing mortality are not evenly distributed over space. We conclude by discussing the use of this estimation approach to assess the status of other species in data-poor situations.