Albatross populations in peril: a population trajectory for black-browed albatrosses at south Georgia.

Simulation modeling was used to reconstruct Black-browed Albatross (Diomedea melanophris) population trends. Close approximations to observed data were accomplished by annually varying survival rates, reproductive success, and probabilities of returning to breed given success in previous years. The temporal shift in annual values coincided with the start of longline fishing at South Georgia and potential changes in krill abundance. We used 23 years of demographic data from long-term studies of a breeding colony of this species at Bird Island, South Georgia, to validate our model. When we used annual parameter estimates for survival, reproductive success, and probabilities of returning to breed given success in previous years, our model trajectory closely followed the observed changes in breeding population size over time. Population growth rate was below replacement (lambda < 1) in most years and was most sensitive to changes in adult survival. This supports the recent IUCN uplisting of this species from "Vulnerable" to "Endangered." Comparison of pre-1988 and post-1988 demography (before and after the inception of a longline fishery in the breeding area) reveals a decrease in lambda from 0.963 to 0.910. A life table response experiment (LTRE) showed that this decline in lambda was caused mostly by declines in survival of adults. If 1988-1998 demographic rates are maintained, the model predicts a 98% chance of a population of fewer than 25 pairs within 78 years. For this population to recover to a status under which it could be "delisted," a 10% increase in survival of all age classes would be needed.     

Understanding and Estimating Effective Population Size for Practical Application in Marine Species Management

Abstract: Effective population size (N_e) determines the strength of genetic drift in a population and has long been recognized as an important parameter for evaluating conservation status and threats to genetic health of populations. Specifically, an estimate of N_e is crucial to management because it integrates genetic effects with the life history of the species, allowing for predictions of a population's current and future viability. Nevertheless, compared with ecological and demographic parameters, N_e has had limited influence on species management, beyond its application in very small populations. Recent developments have substantially improved N_e estimation; however, some obstacles remain for the practical application of N_e estimates. For example, the need to define the spatial and temporal scale of measurement makes the concept complex and sometimes difficult to interpret. We reviewed approaches to estimation of N_e over both long‐term and contemporary time frames, clarifying their interpretations with respect to local populations and the global metapopulation. We describe multiple experimental factors affecting robustness of contemporary N_e estimates and suggest that different sampling designs can be combined to compare largely independent measures of N_e for improved confidence in the result. Large populations with moderate gene flow pose the greatest challenges to robust estimation of contemporary N_e and require careful consideration of sampling and analysis to minimize estimator bias. We emphasize the practical utility of estimating N_e by highlighting its relevance to the adaptive potential of a population and describing applications in management of marine populations, where the focus is not always on critically endangered populations. Two cases discussed include the mechanisms generating N_e estimates many orders of magnitude lower than census N in harvested marine fishes and the predicted reduction in N_e from hatchery‐based population supplementation.     

Loss of Large Predatory Sharks from the Mediterranean Sea

Abstract:  Evidence for severe declines in large predatory fishes is increasing around the world. Because of its long history of intense fishing, the Mediterranean Sea offers a unique perspective on fish population declines over historical timescales. We used a diverse set of records dating back to the early 19th and mid 20th century to reconstruct long-term population trends of large predatory sharks in the northwestern Mediterranean Sea. We compiled 9 time series of abundance indices from commercial and recreational fishery landings, scientific surveys, and sighting records. Generalized linear models were used to extract instantaneous rates of change from each data set, and a meta-analysis was conducted to compare population trends. Only 5 of the 20 species we considered had sufficient records for analysis. Hammerhead ( Sphyrna spp.), blue ( Prionace glauca ), mackerel ( Isurus oxyrinchus and Lamna nasus ), and thresher sharks ( Alopias vulpinus ) declined between 96 and 99.99% relative to their former abundance. According to World Conservation Union (IUCN) criteria, these species would be considered critically endangered. So far, the lack of quantitative population assessments has impeded shark conservation in the Mediterranean Sea. Our study fills this critical information gap, suggesting that current levels of exploitation put large sharks at risk of extinction in the Mediterranean Sea. Possible ecosystem effects of these losses involve a disruption of top-down control and a release of midlevel consumers.     

Effects of Habitat Fragmentation on Effective Population Size in the Endangered Rio Grande Silvery Minnow

Abstract:  We assessed spatial and temporal patterns of genetic diversity to evaluate effects of river fragmentation on remnant populations of the federally endangered Rio Grande silvery minnow ( Hybognathus amarus ). Analysis of microsatellite and mitochondrial DNA detected little spatial genetic structure over the current geographic range, consistent with high gene flow despite fragmentation by dams. Maximum-likelihood analysis of temporal genetic data indicated, however, that present-day effective population size ( N_eV ) of the largest extant population of this species was 78 and the ratio of effective size to adult numbers ( N_eV/N ) was ∼ 0.001 during the study period (1999 to 2001). Coalescent-based analytical methods provided an estimate of historical (river fragmentation was completed in 1975) effective size ( N_eI  ) that ranged between 10⁵ and 10⁶. We propose that disparity between contemporary and historical estimates of N_e and low contemporary N_e/N result from recent changes in demography related to river fragmentation. Rio Grande silvery minnows produce pelagic eggs and larvae subject to downstream transport through diversion dams. This life-history feature results in heavy losses of yearly reproductive effort to emigration and mortality, and extremely large variance in reproductive success among individuals and spawning localities. Interaction of pelagic early life history and river fragmentation has altered demographic and genetic dynamics of remnant populations and reduced N_e to critically low values over ecological time.     

Infectious Diseases and Extinction Risk in Wild Mammals

Abstract:  Parasite-driven declines in wildlife have become increasingly common and can pose significant risks to natural populations. We used the IUCN Red List of Threatened and Endangered Species and compiled data on hosts threatened by infectious disease and their parasites to better understand the role of infectious disease in contemporary host extinctions. The majority of mammal species considered threatened by parasites were either carnivores or artiodactyls, two clades that include the majority of domesticated animals. Parasites affecting host threat status were predominantly viruses and bacteria that infect a wide range of host species, including domesticated animals. Counter to our predictions, parasites transmitted by close contact were more likely to cause extinction risk than those transmitted by other routes. Mammal species threatened by parasites were not better studied for infectious diseases than other threatened mammals and did not have more parasites or differ in four key traits demonstrated to affect parasite species richness in other comparative studies. Our findings underscore the need for better information concerning the distribution and impacts of infectious diseases in populations of endangered mammals. In addition, our results suggest that evolutionary similarity to domesticated animals may be a key factor associated with parasite-mediated declines; thus, efforts to limit contact between domesticated hosts and wildlife could reduce extinction risk.     

Risk-Based Viable Population Monitoring

Abstract:  We describe risk-based viable population monitoring, in which the monitoring indicator is a yearly prediction of the probability that, within a given timeframe, the population abundance will decline below a prespecified level. Common abundance-based monitoring strategies usually have low power to detect declines in threatened and endangered species and are largely reactive to declines. Comparisons of the population's estimated risk of decline over time will help determine status in a more defensible manner than current monitoring methods. Monitoring risk is a more proactive approach; critical changes in the population's status are more likely to be demonstrated before a devastating decline than with abundance-based monitoring methods. In this framework, recovery is defined not as a single evaluation of long-term viability but as maintaining low risk of decline for the next several generations. Effects of errors in risk prediction techniques are mitigated through shorter prediction intervals, setting threshold abundances near current abundance, and explicitly incorporating uncertainty in risk estimates. Viable population monitoring also intrinsically adjusts monitoring effort relative to the population's true status and exhibits considerable robustness to model misspecification. We present simulations showing that risk predictions made with a simple exponential growth model can be effective monitoring indicators for population dynamics ranging from random walk to density dependence with stable, decreasing, or increasing equilibrium. In analyses of time-series data for five species, risk-based monitoring warned of future declines and demonstrated secure status more effectively than statistical tests for trend.     

Effects of Recent Environmental Change on Accuracy of Inferences of Extinction Status

Correctly classifying a species as extinct or extant is of critical importance if current rates of biodiversity loss are to be accurately quantified. Observing an extinction event is rare, so in many cases extinction status is inferred using methods based on the analysis of records of historic sighting events. The accuracy of such methods is difficult to test. However, results of recent experiments with microcosm communities suggest that the rate at which a population declines to extinction, potentially driven by varying environmental conditions, may alter one's ability accurately to infer extinction status. We tested how the rate of population decline, driven by historic environmental change, alters the accuracy of 6 commonly applied sighting‐based methods used to infer extinction. We used data from small‐scale experimental communities and recorded wild population extirpations. We assessed how accuracy of the different methods was affected by rate of population decline, search effort, and number of sighting events recorded. Rate of population decline and historic population size of the species affected the accuracy of inferred extinction dates; however, faster declines produced more accurate inferred dates of extinction, but only when population sizes were higher. Optimal linear estimation (OLE) offered the most reliable and robust estimates, though no single method performed best in all situations, and it may be appropriate to use a different method if information regarding historic search efforts is available. OLE provided the most accurate estimates of extinction when the number of sighting events used was >10, and future use of this method should take this into account. Data from experimental populations provide added insight into testing techniques to discern wild extirpation events. Care should be taken designing such experiments so that they mirror closely the abundance dynamics of populations affected by real‐world extirpation events. Efectos del Cambio Ambiental Reciente sobre la Precisión de las Inferencias sobre el Estado de Extinción     

The Pending Extinction of the Uncompahgre Fritillary Butterfly

Previous mark-recapture studies and population size estimates indicated that in the 1980s populations of the endangered Uncompahgre fritillary ( Boloria acrocnema ) declined precipitously, apparently leading to extirpation at its type locality. This locality and a nearby second site, both high in the San Juan Mountains of southwestern Colorado, constitute the total known range of B. acrocnema . A survey of 20 presumptive allozyme loci from one B. acrocnema population and seven populations of the closely related B. improba , sampled along the Rocky Mountain cordillera from the Yukon Territory, Canada, to the Wind River Range of Wyoming, revealed that B. acrocnema populations are less variable genetically than are B. improba populations in the northern part of that species' North American range. Genetic variability was also assayed at 18 presumptive allozyme loci in populations of Boloria titania, which were sampled from five of the same locations for comparison with the B. improba-B. acrocnema clade. Low estimates of heterozygosity indicate that genetic and demographic factors preceded by historic human disturbance are causing the decline of B. acrocnema . In addition, persistent drought conditions in southwestern Colorado throughout the 1980s probably contributed to the population declines. Conservation recommendations include strict enforcement of the ban against collecting pursuant to the Endangered Species Act, careful monitoring of known B. acrocnema sites, and possible translocations. Extreme measures beyond these will be costly and may be inappropriate for this relict species, which has an unknown capacity to persist in the face of local weather fluctuations and predicted regional warming trends.     

Demographic Consequences of Changed Pupping and Hauling Sites of the Hawaiian Monk Seal

Abstract: During the last 30 years, changes in the size of Hawaiian monk seal populations at several locations have been associated with the amount and type of human disturbance. Recreational beach activities caused monk seals to alter their pupping and hauling patterns. Survival of pups in suboptimal habitats was low, leading to gradual population declines. During the last decade at Kure Atoll, the process has been reversed human disturbance on beaches has decreased and traditional pupping and hauling sites have been reestablished Subsequently, high survival rates of young seals, coupled with two successful enhancement programs for female pups, have led to dramatic changes in the age and sex composition of the population. Based on these changes, the monk seal population at Kure Atoll soon should begin to increase. Apparently small behavioral changes in such vital activities as feeding and reproduction can have large demographic consequences. Therefore, monitoring of endangered species should include data on habitat use and age and sex composition, as well as estimates of abundance.     

Effects of sampling error and temporal correlations in population growth on process variance estimators

Estimates of a population’s growth rate and process variance from time-series data are often used to calculate risk metrics such as the probability of quasi-extinction, but temporal correlations in the data from sampling error, intrinsic population factors, or environmental conditions can bias process variance estimators and detrimentally affect risk predictions. It has been claimed (McNamara and Harding, Ecol Lett 7:16–20, 2004) that estimates of the long-term variance that incorporate observed temporal correlations in population growth are unaffected by sampling error; however, no estimation procedures were proposed for time-series data. We develop a suite of such long-term variance estimators, and use simulated data with temporally autocorrelated population growth and sampling error to evaluate their performance. In some cases, we get nearly unbiased long-term variance estimates despite ignoring sampling error, but the utility of these estimators is questionable because of large estimation uncertainty and difficulties in estimating correlation structure in practice. Process variance estimators that ignored temporal correlations generally gave more precise estimates of the variability in population growth and of the probability of quasi-extinction. We also found that the estimation of probability of quasi-extinction was greatly improved when quasi-extinction thresholds were set relatively close to population levels. Because of precision concerns, we recommend using simple models for risk estimates despite potential biases, and limiting inference to quantifying relative risk; e.g., changes in risk over time for a single population or comparative risk among populations.     

Use of Integrated Modeling to Enhance Estimates of Population Dynamics Obtained from Limited Data

Abstract:  Demographic data of rare and endangered species are often too sparse to estimate vital rates and population size with sufficient precision for understanding population growth and decline. Yet, the combination of different sources of demographic data into one statistical model holds promise. We applied Bayesian integrated population modeling to demographic data from a colony of the endangered greater horseshoe bats (Rhinolophus ferrumequinum) . Available data were the number of subadults and adults emerging from the colony roost at dusk, the number of newborns from 1991 to 2005, and recapture data of subadults and adults from 2004 and 2005. Survival rates did not differ between sexes, and demographic rates remained constant across time. The greater horseshoe bat is a long-lived species with high survival rates (first year: 0.49 [SD 0.06]; adults: 0.91 [SD 0.02]) and low fecundity (0.74 [SD 0.12]). The yearly average population growth was 4.4% (SD 0.1%) and there were 92 (SD 10) adults in the colony in year 2005. Had we analyzed each data set separately, we would not have been able to estimate fecundity, the estimates of survival would have been less precise, and the estimate of population growth biased. Our results demonstrate that integrated models are suitable for obtaining crucial demographic information from limited data.     

Using Age Structure to Detect Impacts on Threatened Populations: a Case Study with Steller Sea Lions

Abstract:   A delayed response to change is often a characteristic of long-lived species and presents a major challenge to monitoring their status. However, rapid shifts in age structure can occur even while population size remains relatively static. We used time-varying matrix models to study age-structure information as a tool for improving detection of survivorship and fecundity change and status. We applied the methods to Steller sea lions (  Eumetopias jubatus ), a long-lived endangered marine mammal found throughout the North Pacific Rim. Population and newborn counts were supplemented with information on the fraction of the population that was juvenile, obtained by measuring animals in aerial photographs taken during range-wide censuses. By fitting the model to 1976–1998 data, we obtained maximum-likelihood estimates and 95% confidence intervals for juvenile survivorship, adult survivorship, and adult fecundity in the mid-1980s, late 1980s, and 1990s. We used a series of nested models to test whether the data were best fit by a model with one, two, or three temporal changes in demographic rates, and we fit the models to different lengths of data to test the number of years of data needed to detect a demographic change. The declines in the early 1980s were associated with severely low juvenile survivorship, whereas declines in the 1990s were associated with disproportionately low fecundity. We repeated these analyses, fitting only to the count data without the juvenile-fraction information, to determine whether the age-structure information changed the conclusions and/or changed the certainty and speed with which demographic-rate changes could be detected. The juvenile-fraction data substantially improved the degree to which estimates from the model were consistent with field data and significantly improved the speed and certainty with which changes in demographic rates were detected.     

An Age-Structured Demographic Model for the Endangered Stephens' Kangaroo Rat

Effective conservation of endangered species often is hampered by inadequate knowledge of demography. We extracted information on survival and fecundity from an 18-month, live-trapping study of Dipodomys stephensi , and from this we developed an age-structured demographic model to assess population viability. Adult Stephens' kangaroo rats persisted longer than juveniles, and adult females persisted longer than adult males. Disappearance rates were high in the first months after initial capture. Thereafter, the fraction of animals persisting decreased slowly and in an approximately linear fashion on a semilogarithmic scale, suggesting age-independent mortality factors such as predation. Juvenile persistence did not differ substantially between two years of strikingly different rainfall. Onset of breeding followed the start of winter rains. Length of the breeding season, average number of litters per female, and the fraction of first-year females breeding were much greater in the year of higher rainfall. We propose a birth-pulse demographic model for D. stephensi that distinguishes juvenile and adult age classes. Temporal environmental variation can be modeled adequately with a constant survivorship schedule and variable fecundity determined by yearly precipitation. Several issues should be resolved, however, before conservation decisions are based on the model. Better estimates of juvenile survivorship are critical, the quantitative relationship between precipitation and fecundity must be determined, and the potential for density dependence and source-sink population dynamics must be evaluated.     

Critique of the Evolutionarily Significant Unit as a Definition for "Distinct Population Segments" under the U.S. Endangered Species Act

The U.S. Endangered Species Act grants protection to species, subspecies, and "distinct population segments" of vertebrate species. Historically, Congress included distinct population segments into endangered species legislation to enable the U.S. Fish and Wildlife Service to implement a flexible and pragmatic approach in listing populations of vertebrate species. Recently, the U.S. Fish and Wildlife Service and the National Marine Fisheries Service have proposed a policy that would narrowly define distinct population segments as evolutionarily significant units based on morphological and genetic distinctiveness between populations. Historically, the power to list species or populations as distinct population segments has been used to tailor management practices to unique circumstances; grant varied levels of protection in different parts of a species' range; protect species from extinction in significant portions of their ranges as well as to protect populations that are unique evolutionary entities. A strict redefinition of distinct population segments as evolutionarily significant units will compromise management efforts because the role of demographic and behavioral data will be reduced. Furthermore, strictly cultural, economic, or geographic justifications for listing populations as threatened or endangered will be greatly curtailed.     

Increasing the Accuracy of Productivity and Survival Estimates in Assessing Landbird Population Status

Abstract:  The conservation of species with declining populations requires information on population demography and identification of factors that limit population growth. For landbird species, an understanding of large-scale population declines often requires assessment of local population processes, including the production of offspring, the survival of those offspring, and adult survival. Population growth has been modeled for several species of landbirds to date, and these studies have provided important information on relationships between population status and population-limiting factors. Several recent studies have illuminated field methods and analytical techniques that can aid in increasing the accuracy of productivity and survival estimates for population models. We reviewed these methods and recommend their implementation, including quantification of the season-long productivity of individuals, collection of empirical data on juvenile survival during the postfledging and overwintering periods, and incorporation of adult breeding dispersal into annual adult survival estimates. Such methods will allow for more accurate assessment of population status and provide a better understanding of the factors on which to focus our conservation efforts.     

Linking irreplaceable landforms in a self‐organizing landscape to sensitivity of population vital rates for an ecological specialist

Irreplaceable, self‐organizing landforms and the endemic and ecologically specialized biodiversity they support are threatened globally by anthropogenic disturbances. Although the outcome of disrupting landforms is somewhat understood, little information exists that documents population consequences of landform disturbance on endemic biodiversity. Conservation strategies for species dependent upon landforms have been difficult to devise because they require understanding complex feedbacks that create and maintain landforms and the consequences of landform configuration on demography of species. We characterized and quantified links between landform configuration and demography of an ecological specialist, the dunes sagebrush lizard (Sceloporus arenicolus), which occurs only in blowouts (i.e., wind‐blown sandy depressions) of Shinnery oak (Quercus havardii) sand‐dune landforms. We used matrix models to estimate vital rates from a multisite mark‐recapture study of 6 populations occupying landforms with different spatial configurations. Sensitivity and elasticity analyses demonstrated demographic rates among populations varied in sensitivity to different landform configurations. Specifically, significant relationships between blowout shape complexity and vital rate elasticities suggested direct links between S. arenicolus demography and amount of edge in Shinnery oak sand‐dune landforms. These landforms are irreplaceable, based on permanent transition of disturbed areas to alternative grassland ecosystem states. Additionally, complex feedbacks between wind, sand, and Shinnery oak maintain this landform, indicating restoration through land management practices is unlikely. Our findings that S. arenicolus population dynamics depended on landform configuration suggest that failure to consider processes of landform organization and their effects on species’ population dynamics may lead to incorrect inferences about threats to endemic species and ineffective habitat management for threatened or endangered species. As such, successful conservation of these systems and the biodiversity they support must be informed by research linking process‐oriented studies of self‐organized landforms with studies of movement, behavior, and demography of species that dwell in them.     

Estimating demographic parameters for a critically endangered marine species with frequent reproductive omission: hawksbill turtles nesting at Varanus Island,Western Australia

The hawksbill marine turtle (Eretmochelys imbricata) is listed on the IUCN Red List as critically endangered but little is known about its demography to support robust diagnosis of population trends. Moreover, adult female hawksbills do not nest each year due to environmentally mediated physiological constraints and this skipped breeding behaviour presents a major challenge in data collection and for estimating demographic parameters from such data sets. We estimated demographic parameters such as survival and breeding probabilities for a major Indo-Pacific nesting hawksbill population using a capture-mark-recapture (CMR) study and a multistate open robust design statistical modelling approach, which accounts for breeding omission and the staggered arrival and departure of nesters during each season. Our study used CMR histories for 413 nesting hawksbills tagged on Varanus Island (Western Australia) over a 4-month sampling period each year for 20 austral summer nesting seasons between 1987 and 2007. The estimated annual survival probability for these nesting hawksbills was constant over the 20 years at ca. 0.947 (95% CI: 0.91–0.97), which is encouragingly high for a population associated with industry. The estimated annual conditional nesting (breeding) probability for female hawksbills that had skipped the previous nesting season was time-specific ranging from 0.07 to 0.29 (mean = 0.18, CV = 41.3%), which presumably reflects the interaction between turtle physiology and in-water habitat quality. The mean conditional probability of breeding again having skipped 2 prior consecutive nesting seasons was ca. 0.83 (95% CI: 0.73–0.89), indicating a high frequency of breeding season omission. The annual nesting probability for females that had nested the previous season was 0, reflecting known obligate skipped breeding (reproductive omission) that is characteristic of hawksbill populations in response to high energy demands of vitellogenesis and breeding migration. These are the first estimates of annual survival and state-dependent breeding probabilities for any Indo-Pacific hawksbill stock that provide a basis for developing a better understanding of regional population dynamics for this critically endangered species.     

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.     

Determining Optimal Population Monitoring for Rare Butterflies

Abstract: Determining population viability of rare insects depends on precise, unbiased estimates of population size and other demographic parameters. We used data on the endangered St. Francis' satyr butterfly (Neonympha mitchellii francisci) to evaluate 2 approaches (mark–recapture and transect counts) for population analysis of rare butterflies. Mark–recapture analysis provided by far the greatest amount of demographic information, including estimates (and standard errors) of population size, detection, survival, and recruitment probabilities. Mark–recapture analysis can also be used to estimate dispersal and temporal variation in rates, although we did not do this here. Models of seasonal flight phenologies derived from transect counts (Insect Count Analyzer) provided an index of population size and estimates of survival and statistical uncertainty. Pollard–Yates population indices derived from transect counts did not provide estimates of demographic parameters. This index may be highly biased if detection and survival probabilities vary spatially and temporally. In terms of statistical performance, mark–recapture and Pollard–Yates indices were least variable. Mark–recapture estimates were less likely to fail than Insect Count Analyzer, but mark–recapture estimates became less precise as sampling intensity decreased. In general, count‐based approaches are less costly and less likely to cause harm to rare insects than mark–recapture. The optimal monitoring approach must reconcile these trade‐offs. Thus, mark–recapture should be favored when demographic estimates are needed, when financial resources enable frequent sampling, and when marking does not harm the insect populations. The optimal sampling strategy may use 2 sampling methods together in 1 overall sampling plan: limited mark–recapture sampling to estimate survival and detection probabilities and frequent but less expensive transect counts.     

The Importance of Analysis Method for Breeding Bird Survey Population Trend Estimates

Population trends from the Breeding Bird Survey are widely used to focus conservation efforts on species thought to be in decline and to test preliminary hypotheses regarding the causes of these declines. A number of statistical methods have been used to estimate population trends, but there is no consensus as to which is the most reliable. We quantified differences in trend estimates or different analysis methods applied to the same subset of Breeding Bird Survey data. We estimated trends for 115 species in British Columbia using three analysis methods: U.S. National Biological Service route regression, Canadian Wildlife Service route regression, and nonparametric rank-trends analysis. Overall, the number of species estimated to be declining was similar among the three methods, but the number of statistically significant declines was not similar (15, 8, and 29 respectively). In addition, many differences existed among methods in the trend estimates assigned to individual species. Comparing the two route regression methods, Canadian Wildlife Service estimates had a greater absolute magnitude on average than those of the U.S. National Biological Service method. U.S. National Biological Service estimates were on average more positive than the Canadian Wildlife Service estimates when the respective agency's data selection criteria were applied separately. These results imply that our ability to detect population declines and to prioritize species of conservation concern depend strongly upon the analysis method used. This highlights the need for further research to determine how best to accurately estimate trends from the data. We suggest a method for evaluating the performance of the analysis methods by using simulated Breeding Bird Survey data.