Geology topics

M. Kery

Publications and source records attributed to M. Kery.

At least 19 recordsLinked to original sources

USGS2010

Predicting species distributions from checklist data using site-occupancy models

Aim: (1) To increase awareness of the challenges induced by imperfect detection, which is a fundamental issue in species distribution modelling; (2) to emphasize the value of replicate observations for species distribution modelling; and (3) to show how 'cheap' checklist data in faunal/floral databases may be used for the rigorous modelling of distributions by site-occupancy models. Location: Switzerland. Methods: We used checklist data collected by volunteers during 1999 and 2000 to analyse the distribution of the blue hawker, Aeshna cyanea (Odonata, Aeshnidae), a common dragonfly in Switzerland. We used data from repeated visits to 1-ha pixels to derive 'detection histories' and apply site-occupancy models to estimate the 'true' species distribution, i.e. corrected for imperfect detection. We modelled blue hawker distribution as a function of elevation and year and its detection probability of elevation, year and season. Results: The best model contained cubic polynomial elevation effects for distribution and quadratic effects of elevation and season for detectability. We compared the site-occupancy model with a conventional distribution model based on a generalized linear model, which assumes perfect detectability (p = 1). The conventional distribution map looked very different from the distribution map obtained using site-occupancy models that accounted for the imperfect detection. The conventional model underestimated the species distribution by 60%, and the slope parameters of the occurrence-elevation relationship were also underestimated when assuming p = 1. Elevation was not only an important predictor of blue hawker occurrence, but also of the detection probability, with a bell-shaped relationship. Furthermore, detectability increased over the season. The average detection probability was estimated at only 0.19 per survey. Main conclusions: Conventional species distribution models do not model species distributions per se but rather the apparent distribution, i.e. an unknown proportion of species distributions. That unknown proportion is equivalent to detectability. Imperfect detection in conventional species distribution models yields underestimates of the extent of distributions and covariate effects that are biased towards zero. In addition, patterns in detectability will erroneously be ascribed to species distributions. In contrast, site-occupancy models applied to replicated detection/non-detection data offer a powerful framework for making inferences about species distributions corrected for imperfect detection. The use of 'cheap' checklist data greatly enhances the scope of applications of this useful class of models. ?? 2010 Blackwell Publishing Ltd.

Journal of Biogeography↗

USGS2009

Species richness and occupancy estimation in communities subject to temporary emigration

Species richness is the most common biodiversity metric, although typically some species remain unobserved. Therefore, estimates of species richness and related quantities should account for imperfect detectability. Community dynamics can often be represented as superposition of species-specific phenologies (e. g., in taxa with well-defined flight [insects], activity [rodents], or vegetation periods [plants]). We develop a model for such predictably open communities wherein species richness is expressed as the sum over observed and unobserved species of estimated species-specific and site-specific occurrence indicators and where seasonal occurrence is modeled as a species-specific function of time. Our model is a multispecies extension of a multistate model with one unobservable state and represents a parsimonious way of dealing with a widespread form of 'temporary emigration.'' For illustration we use Swiss butterfly monitoring data collected under a robust design (RD); species were recorded on 13 transects during two secondary periods within <= 7 primary sampling periods. We compare estimates with those under a variation of the model applied to standard data, where secondary samples are pooled. The latter model yielded unrealistically high estimates of total community size of 274 species. In contrast, estimates were similar under models applied to RD data with constant (122) or seasonally varying (126) detectability for each species, but the former was more parsimonious and therefore used for inference. Per transect, 6 44 (mean 21.1) species were detected. Species richness estimates averaged 29.3; therefore only 71% (range 32-92%) of all species present were ever detected. In any primary period, 0.4-5.6 species present were overlooked. Detectability varied by species and averaged 0.88 per primary sampling period. Our modeling framework is extremely flexible; extensions such as covariates for the occurrence or detectability of individual species are easy. It should be useful for communities with a predictable form of temporary emigration where rigorous estimation of community metrics has proved challenging so far.

Ecology↗

USGS2009

Inference about species richness and community structure using species-specific occupancy models in the National Swiss Breeding Bird Survey MUB

Species richness is the most widely used biodiversity measure. Virtually always, it cannot be observed but needs to be estimated because some species may be present but remain undetected. This fact is commonly ignored in ecology and management, although it will bias estimates of species richness and related parameters such as occupancy, turnover or extinction rates. We describe a species community modeling strategy based on species-specific models of occurrence, from which estimates of important summaries of community structure, e.g., species richness, occupancy, or measures of similarity among species or sites, are derived by aggregating indicators of occurrence for all species observed in the sample, and for the estimated complement of unobserved species. We use data augmentation for an efficient Bayesian approach to estimation and prediction under this model based on MCMC in WinBUGS. For illustration, we use the Swiss breeding bird survey (MHB) that conducts 2?3 territory-mapping surveys in a systematic sample of 267 1 km2 units on quadrat-specific routes averaging 5.1 km to obtain species-specific estimates of occupancy, and estimates of species richness of all diurnal species free of distorting effects of imperfect detectability. We introduce into our model species-specific covariates relevant to occupancy (elevation, forest cover, route length) and sampling (season, effort). From 1995 to 2004, 185 diurnal breeding bird species were known in Switzerland, and an additional 13 bred 1?3 times since 1900. 134 species were observed during MHB surveys in 254 quadrats surveyed in 2001, and our estimate of 169.9 (95% CI 151?195) therefore appeared sensible. The observed number of species ranged from 4 to 58 (mean 32.8), but with an estimated 0.7?11.2 (mean 2.6) further, unobserved species, the estimated proportion of detected species was 0.48?0.98 (mean 0.91). As is well known, species richness declined at higher elevation and fell above the timberline, and most species showed some preferred elevation. Route length had clear effects on occupancy, suggesting it is a proxy for the size of the effectively sampled area. Detection probability of most species showed clear seasonal patterns and increased with greater survey effort; these are important results for the planning of focused surveys. The main benefit of our model, and its implementation in WinBUGS for which we provide code, is its conceptual simplicity. Species richness is naturally expressed as the sum of occurrences of individual species. Information about species is combined across sites, which yields greater efficiency or may even enable estimation for sites with very few observed species in the first place. At the same time, species detections are clearly segregated into a true state process (occupancy) and an observation process (detection, given occupancy), and covariates can be readily introduced, which provides for efficient introduction of such additional information as well as sharp testing of such relationships.

Book chapter↗

USGS2008

Reduced fecundity in small populations of the rare plant Gentianopsis ciliate (Gentianaceae)

Habitat destruction is the main cause for the biodiversity crisis. Surviving populations are often fragmented, i.e., small and isolated from each other. Reproduction of plants in small populations is often reduced, and this has been attributed to inbreeding depression, reduced attractiveness for pollinators, and reduced habitat quality in small populations. Here we present data on the effects of fragmentation on the rare, self-compatible perennial herb Gentianopsis ciliata (Gentianaceae), a species with very small and presumably well-dispersed seeds. We studied the relationship between population size, plant size, and the number of flowers produced in 63 populations from 1996-1998. In one of the years, leaf and flower size and the number of seeds produced per fruit was studied in a subset of 25 populations. Plant size, flower size, and the number of seeds per fruit and per plant increased with population size, whereas leaf length and the number of flowers per plant did not. The effects of population size on reproduction and on flower size remained significant if the effects were adjusted for differences in plant size, indicating that they could not be explained by differences in habitat quality. The strongly reduced reproduction in small populations may be due to pollination limitation, while the reduced flower size could indicate genetic effects.

Plant Biology↗

USGS2008

Hierarchical Bayes estimation of species richness and occupancy in spatially replicated surveys

1. Species richness is the most widely used biodiversity metric, but cannot be observed directly as, typically, some species are overlooked. Imperfect detectability must therefore be accounted for to obtain unbiased species-richness estimates. When richness is assessed at multiple sites, two approaches can be used to estimate species richness: either estimating for each site separately, or pooling all samples. The first approach produces imprecise estimates, while the second loses site-specific information. 2. In contrast, a hierarchical Bayes (HB) multispecies site-occupancy model benefits from the combination of information across sites without losing site-specific information and also yields occupancy estimates for each species. The heart of the model is an estimate of the incompletely observed presence-absence matrix, a centrepiece of biogeography and monitoring studies. We illustrate the model using Swiss breeding bird survey data, and compare its estimates with the widely used jackknife species-richness estimator and raw species counts. 3. Two independent observers each conducted three surveys in 26 1-km(2) quadrats, and detected 27-56 (total 103) species. The average estimated proportion of species detected after three surveys was 0.87 under the HB model. Jackknife estimates were less precise (less repeatable between observers) than raw counts, but HB estimates were as repeatable as raw counts. The combination of information in the HB model thus resulted in species-richness estimates presumably at least as unbiased as previous approaches that correct for detectability, but without costs in precision relative to uncorrected, biased species counts. 4. Total species richness in the entire region sampled was estimated at 113.1 (CI 106-123); species detectability ranged from 0.08 to 0.99, illustrating very heterogeneous species detectability; and species occupancy was 0.06-0.96. Even after six surveys, absolute bias in observed occupancy was estimated at up to 0.40. 5. Synthesis and applications. The HB model for species-richness estimation combines information across sites and enjoys more precise, and presumably less biased, estimates than previous approaches. It also yields estimates of several measures of community size and composition. Covariates for occupancy and detectability can be included. We believe it has considerable potential for monitoring programmes as well as in biogeography and community ecology.

Journal of Applied Ecology↗

USGS2008

Importance of sampling design and analysis in animal population studies: a comment on Sergio et al

1. The use of predators as indicators and umbrellas in conservation has been criticized. In the Trentino region, Sergio et al. (2006; hereafter SEA) counted almost twice as many bird species in quadrats located in raptor territories than in controls. However, SEA detected astonishingly few species. We used contemporary Swiss Breeding Bird Survey data from an adjacent region and a novel statistical model that corrects for overlooked species to estimate the expected number of bird species per quadrat in that region. 2. There are two anomalies in SEA which render their results ambiguous. First, SEA detected on average only 6.8 species, whereas a value of 32 might be expected. Hence, they probably overlooked almost 80% of all species. Secondly, the precision of their mean species counts was greater in two-thirds of cases than in the unlikely case that all quadrats harboured exactly the same number of equally detectable species. This suggests that they detected consistently only a biased, unrepresentative subset of species. 3. Conceptually, expected species counts are the product of true species number and species detectability p. Plenty of factors may affect p, including date, hour, observer, previous knowledge of a site and mobbing behaviour of passerines in the presence of predators. Such differences in p between raptor and control quadrats could have easily created the observed effects. Without a method that corrects for such biases, or without quantitative evidence that species detectability was indeed similar between raptor and control quadrats, the meaning of SEA's counts is hard to evaluate. Therefore, the evidence presented by SEA in favour of raptors as indicator species for enhanced levels of biodiversity remains inconclusive. 4. Synthesis and application. Ecologists should pay greater attention to sampling design and analysis in animal population estimation. Species richness estimation means sampling a community. Samples should be representative for the community studied and the sampling fraction among communities compared should be the same on average, otherwise formal estimation approaches must be applied to avoid misleading inference.

Journal of Applied Ecology↗

USGS2007

Survival of timber rattlesnakes ( Crotalus horridus ) estimated by capture-recapture models in relation to age, sex, color morph, time, and birthplace

Juvenile survival is one of the least known elements of the life history of many species, in particular snakes. We conducted a mark–recapture study of Crotalus horridus from 1978–2002 in northeastern New York near the northern limits of the species' range. We marked 588 neonates and estimated annual age-, sex-, and morph-specific recapture and survival rates using the Cormack-Jolly-Seber (CJS) model. Wild-caught neonates (field-born, n = 407) and neonates produced by captive-held gravid females (lab-born, n = 181) allowed comparison of the birthplace, or lab treatment effect, in estimated survival. Recapture rates declined from about 10–20% over time while increasing from young to older age classes. Estimated survival rates ( S ± 1 SE) in the first year were significantly higher among field-born (black morph: S = 0.773 ± 0.203; yellow morph: S = 0.531 ± 0.104) than among lab-born snakes (black morph: S = 0.411 ± 0.131; yellow morph: S = 0.301 ± 0.081). Lower birth weights combined with a lack of field exposure until release apparently contributed to the lower survival rate of lab-born snakes. Subsequent survival estimates for 2–4-yr-old snakes were S = 0.845 ± 0.084 for the black morph and S = 0.999 (SE not available) for the yellow morph, and for ≥5-yr-old snakes S = 0.958 ± 0.039 (black morph) and S = 0.822 ± 0.034 (yellow morph). The most parsimonious model overall contained an independent time trend for survival of each age, morph, and lab-treatment group. For snakes of the first two age groups (ages 1 yr and 2–4 yr), survival tended to decline over the years for both morphs, while for adult snakes (5 yr and older), survival was constant or even slightly increased. Our data on survival and recapture are among the first rigorous estimates of these parameters in a rattlesnake and among the few yet available for any viperid snake. These data are useful for analyses of the life-history strategy, population dynamics, and conservation of this long-lived snake.

Copeia↗

USGS2007

A Bayesian state-space formulation of dynamic occupancy models

Species occurrence and its dynamic components, extinction and colonization probabilities, are focal quantities in biogeography and metapopulation biology, and for species conservation assessments. It has been increasingly appreciated that these parameters must be estimated separately from detection probability to avoid the biases induced by nondetection error. Hence, there is now considerable theoretical and practical interest in dynamic occupancy models that contain explicit representations of metapopulation dynamics such as extinction, colonization, and turnover as well as growth rates. We describe a hierarchical parameterization of these models that is analogous to the state-space formulation of models in time series, where the model is represented by two components, one for the partially observable occupancy process and another for the observations conditional on that process. This parameterization naturally allows estimation of all parameters of the conventional approach to occupancy models, but in addition, yields great flexibility and extensibility, e.g., to modeling heterogeneity or latent structure in model parameters. We also highlight the important distinction between population and finite sample inference; the latter yields much more precise estimates for the particular sample at hand. Finite sample estimates can easily be obtained using the state-space representation of the model but are difficult to obtain under the conventional approach of likelihood-based estimation. We use R and Win BUGS to apply the model to two examples. In a standard analysis for the European Crossbill in a large Swiss monitoring program, we fit a model with year-specific parameters. Estimates of the dynamic parameters varied greatly among years, highlighting the irruptive population dynamics of that species. In the second example, we analyze route occupancy of Cerulean Warblers in the North American Breeding Bird Survey (BBS) using a model allowing for site-specific heterogeneity in model parameters. The results indicate relatively low turnover and a stable distribution of Cerulean Warblers which is in contrast to analyses of counts of individuals from the same survey that indicate important declines. This discrepancy illustrates the inertia in occupancy relative to actual abundance. Furthermore, the model reveals a declining patch survival probability, and increasing turnover, toward the edge of the range of the species, which is consistent with metapopulation perspectives on the genesis of range edges. Given detection/non-detection data, dynamic occupancy models as described here have considerable potential for the study of distributions and range dynamics.

Ecology↗

USGS2007

Hierarchical spatial models of abundance and occurrence from imperfect survey data

Many estimation and inference problems arising from large-scale animal surveys are focused on developing an understanding of patterns in abundance or occurrence of a species based on spatially referenced count data. One fundamental challenge, then, is that it is generally not feasible to completely enumerate ('census') all individuals present in each sample unit. This observation bias may consist of several components, including spatial coverage bias (not all individuals in the Population are exposed to sampling) and detection bias (exposed individuals may go undetected). Thus, observations are biased for the state variable (abundance, occupancy) that is the object of inference. Moreover, data are often sparse for most observation locations, requiring consideration of methods for spatially aggregating or otherwise combining sparse data among sample units. The development of methods that unify spatial statistical models with models accommodating non-detection is necessary to resolve important spatial inference problems based on animal survey data. In this paper, we develop a novel hierarchical spatial model for estimation of abundance and occurrence from survey data wherein detection is imperfect. Our application is focused on spatial inference problems in the Swiss Survey of Common Breeding Birds. The observation model for the survey data is specified conditional on the unknown quadrat population size, N(s). We augment the observation model with a spatial process model for N(s), describing the spatial variation in abundance of the species. The model includes explicit sources of variation in habitat structure (forest, elevation) and latent variation in the form of a correlated spatial process. This provides a model-based framework for combining the spatially referenced samples while at the same time yielding a unified treatment of estimation problems involving both abundance and occurrence. We provide a Bayesian framework for analysis and prediction based on the integrated likelihood, and we use the model to obtain estimates of abundance and occurrence maps for the European Jay (Garrulus glandarius), a widespread, elusive, forest bird. The naive national abundance estimate ignoring imperfect detection and incomplete quadrat coverage was 77 766 territories. Accounting for imperfect detection added approximately 18 000 territories, and adjusting for coverage bias added another 131 000 territories to yield a fully corrected estimate of the national total of about 227 000 territories. This is approximately three times as high as previous estimates that assume every territory is detected in each quadrat.

Ecological Monographs↗

USGS2006

Short-term oscillations in avian molt intensity: evidence from the golden eagle Aquila chrysaetos

From a year-long study of molt in the golden eagle Aquila chrysaetos, we recorded 2069 contour feathers replaced in 137 d (6 May-19 September). Very few contour feathers were lost outside this period. From precise daily counts of feathers lost, and using time series analysis, we identified short-term fluctuations (i.e., 19-d subcycles) around a midsummer peak (i.e., a left-skewed normal distribution). Because these subcycles have never before been reported and because the physiological basis for many aspects of avian molt is poorly known, we offer only hypothetical explanations for the controls responsible for the subcycles.

Journal of Avian Biology↗

USGS2004

Extinction rate estimates for plant populations in revisitation studies: Importance of detectability

Many researchers have obtained extinction-rate estimates for plant populations by comparing historical and current records of occurrence. A population that is no longer found is assumed to have gone extinct. Extinction can then be related to characteristics of these populations, such as habitat type, size, or species, to test ideas about what factors may affect extinction. Such studies neglect the fact that a population may be overlooked, however, which may bias estimates of extinction rates upward. In addition, if populations are unequally detectable across groups to be compared, such as habitat type or population size, comparisons become distorted to an unknown degree. To illustrate the problem, I simulated two data sets, assuming a constant extinction rate, in which populations occurred in different habitats or habitats of different size and these factors affected their detectability The conventional analysis implicitly assumed that detectability equalled 1 and used logistic regression to estimate extinction rates. It wrongly identified habitat and population size as factors affecting extinction risk. In contrast, with capture-recapture methods, unbiased estimates of extinction rates were recovered. I argue that capture-recapture methods should be considered more often in estimations of demographic parameters in plant populations and communities.

Conservation Biology↗

USGS2004

Monitoring programs need to take into account imperfect species detectability

Biodiversiry monitoring is important to identify biological units in need of conservation and to check the effectiveness of conservation actions. Programs generally monitor species richness and its changes (trend). Usually, no correction is made for imperfect species detectability. Instead, it is assumed that each species present has the same probability of being recorded and that there is no difference in this detectability across space and time, e.g. among observers and habitats. Consequently, species richness is determined by enumeration as the sum of species recorded. In Switzerland, the federal government has recently launched a comprehensive program that aims at detecting changes in biodiversity at all levels of biological integration. Birds are an important part of that program. Since 1999, 23 visits per breeding season are made to each of >250 1 km2 squares to map the territories of all detected breeding bird species. Here, we analyse data from three squares to illustrate the use of capture-recapture models in monitoring to obtain detectability-corrected estimates of species richness and trend. Species detectability averaged only 85%. Hence an estimated 15% of species present remained overlooked even after three visits. Within a square, changes in detectability for different years were of the same magnitude when surveys were conducted by the same observer as when they were by different observers. Estimates of trend were usually biased and community turnover was overestimated when based on enumeration. Here we use bird data as an illustration of methods. However, species detectability for any taxon is unlikely ever to be perfect or even constant across categories to be compared. Therefore, monitoring programs should correct for species detectability.

Basic and Applied Ecology↗

USGS2004

Summer diet of the Peregrine Falcon in faunistically rich and poor zones of Arizona analyzed with capture-recapture modeling

We collected prey remains from 25 Peregrine Falcon ( Falco peregrinus ) territories across Arizona from 1977 to 1988 yielding 58 eyrie-years of data. Along with 793 individual birds (107 species and six additional genera), we found seven mammals and nine insects. In addition, two nestling peregrines were consumed. We found a larger dependence upon White-throated Swifts ( Aeronautes saxatalis ) and birds on migration in northern Arizona, while in southeastern and central Arizona average prey mass was greater and columbiforms formed the largest dietary component. In northern, central, and southeastern Arizona, 74, 66, and 56 avian prey taxa, respectively, were recorded. We used capture-recapture modeling to estimate totals of 111 ± 9.5, 113 ± 10.5, and 86 ± 7.9 (SE) avian taxa taken in these same three areas. These values are counterintuitive inasmuch as the southeast has the richest avifauna. For the entire study area, 156 ± 9.3 avian taxa were estimated to be taken by peregrines.

Arizona↗

USGS2004

Sex ratio estimation and survival analysis for Orthetrum coerulescens (Odonata, Libellulidae)

There is controversy over whether uneven sex ratios observed in mature dragonfly populations are a mere artifact resulting from the higher observability of males. Previous studies have at best made indirect inference about sex ratios by analysis of survival or recapture rates. Here, we obtain direct estimates of sex ratio from capture-recapture data based on the Cormack-Jolly-Seber model. We studied Orthetrum coerulescens (Fabricius, 1798) at three sites in the Swiss Jura Mountains over an entire activity period. Recapture rates per 5-day interval were 3.5 times greater for males (0.67, SE 0.02) than for females (0.19, SE 0.02). At two sites, recapture rate increased over the season for males and was constant for females, and at one site it decreased with precipitation for both sexes. In addition, recapture rate was higher with higher temperature for males only. We found no evidence for higher male survival rates in any population. Survival per 5-day interval for both sexes was estimated to be 0.77 ( 95% CI 0.75–0.79 ) without significant site or time-specific variation. There were clear effects of temperature (positive) and precipitation (negative) on survival rate at two sites. Direct estimates of sex ratios were not significantly different from 1 for any time interval. Hence, the observed male-biased sex ratio in adult O. coerulescens was an artifact resulting from the better observability of males. The method presented in this paper is applicable to sex ratio estimation in any kind of animal.

Jura Mountains↗

USGS2004

Demographic estimation methods for plants with dormancy

Demographic studies in plants appear simple because unlike animals, plants do not run away. Plant individuals can be marked with, e.g., plastic tags, but often the coordinates of an individual may be sufficient to identify it. Vascular plants in temperate latitudes have a pronounced seasonal life–cycle, so most plant demographers survey their study plots once a year often during or shortly after flowering. Life–states are pervasive in plants, hence the results of a demographic study for an individual can be summarized in a familiar encounter history, such as 0VFVVF000. A zero means that an individual was not seen in a year and a letter denotes its state for years when it was seen aboveground. V and F here stand for vegetative and flowering states, respectively. Probabilities of survival and state transitions can then be obtained by mere counting. Problems arise when there is an unobservable dormant state, i.e., when plants may stay belowground for one or more growing seasons. Encounter histories such as 0VF00F000 may then occur where the meaning of zeroes becomes ambiguous. A zero can either mean a dead or a dormant plant. Various ad hoc methods in wide use among plant ecologists have made strong assumptions about when a zero should be equated to a dormant individual. These methods have never been compared among each other. In our talk and in Kéry et al. (submitted), we show that these ad hoc estimators provide spurious estimates of survival and should not be used. In contrast, if detection probabilities for aboveground plants are known or can be estimated, capturerecapture (CR) models can be used to estimate probabilities of survival and state–transitions and the fraction of the population that is dormant. We have used this approach in two studies of terrestrial orchids, Cleistes bifaria (Kéry et al., submitted) and Cypripedium reginae (Kéry & Gregg, submitted) in West Virginia, U.S.A. For Cleistes, our data comprised one population with a total of 620 marked ramets over 10 years, and for Cypripedium , two populations with 98 and 258 marked ramets over 11 years. We chose the ramet (= single stem or shoot) as the demographic unit of our study since there was no way distinguishing among genets (genet = genetical individual, i.e., the “individual” that animal ecologists are mostly concerned with). This will introduce some non–independence into the data, which can nevertheless be dealt with easily by correcting variances for overdispersion. Using ramets instead of genets has the further advantage that individuals can be assigned to a state such as flowering or vegetative in an unambiguous manner. This is not possible when genets are the demographic units. In all three populations, auxiliary data was available to show that detection probability of aboveground plants was m 0.995 We fitted multistate models in program MARK by specifying three states (D, V, F), even though the dormant state D does not occur in the encounter histories. Detection probability is fixed at 1 for the vegetative (V) and the flowering state (F) and at zero for the dormant state (D). Rates of survival and of state transitions as well as slopes of covariate relationships can be estimated and LRT or the AIC machinery be used to select among models. To estimate the fraction of the population in the unobservable dormant state, the encounter histories are collapsed to 0 (plant not observed aboveground) and 1 (plant observed aboveground). The Cormack–Jolly–Seber model without constraints on detection probability is used to estimate detection probability, the complement of which is the estimated fraction of the population in the dormant state. Parameter identifiability is an important issue in multi state models. We used the Catchpole–Morgan–Freeman approach to determine which parameters are estimable in principle in our multi state models. Most of 15 tested models were indeed estimable with the notable exception of the most general model, which has fully interactive state- and time-dependent survival and state transition rates. This model would become identifiable if at least some plants would be excavated in years when they do not show up aboveground. Our analyses for three analyzed populations of Cleistes and Cypripedium yielded annual ramet survival rates ranging from 0.86–0.96. Estimates of the average fraction dormant ranged from 0.02–0.30, but with up to half a population in the dormant state in some years. Ultrastructural modeling enables interesting hypotheses to be tested about the relationships of demographic rates with climatic covariates for instance. Such covariate modeling makes the CR approach particularly interesting for evolutionary–ecological questions about, e.g., the adaptive significance of the dormant state.

Animal Biodiversity and Conservation↗

USGS2003

Timing of autumn bird migration under climate change: advances in long–distance migrants, delays in short–distance migrants

As a response to increasing spring temperature in temperate regions in recent years, populations of many plant and animal species, including migratory birds, have advanced the seasonal start of their reproduction or growth. However, the effects of climate changes on subsequent events of the annual cycle remain poorly understood. We investigated long–term changes in the timing of autumn migration in birds, a key event in the annual cycle limiting the reproductive period. Using data spanning a 42–year period, we analysed long–term changes in the passage of 65 species of migratory birds through Western Europe. The autumn passage of migrants wintering south of the Sahara has advanced in recent years, presumably as a result of selection pressure to cross the Sahel before its seasonal dry period. In contrast, migrants wintering north of the Sahara have delayed autumn passage. In addition, species with a variable rather than a fixed number of broods per year have delayed passage, possibly because they are free to attempt more broods. Recent climate changes seem to have a simple unidirectional effect on the seasonal onset of reproduction, but complex and opposing effects on the timing of subsequent events in the annual cycle, depending on the ecology and life history of a species. This complicates predictions of overall effects of global warming on avian communities.

Proceedings of the Royal Society B: Biological Sci↗

USGS2003

Effects of life-state on detectability in a demographic study of the terrestrial orchid Cleistes bifaria

1. Most plant demographic studies follow marked individuals in permanent plots. Plots tend to be small, so detectability is assumed to be one for every individual. However, detectability could be affected by factors such as plant traits, time, space, observer, previous detection, biotic interactions, and especially by life - state . 2. We used a double-observer survey and closed population capture-recapture modelling to estimate state -specific detectability of the orchid Cleistes bifaria in a long-term study plot of 41.2 m 2 . Based on AIC c model selection, detectability was different for each life - state and for tagged vs. previously untagged plants. There were no differences in detectability between the two observers. 3. Detectability estimates (SE) for one-leaf vegetative, two-leaf vegetative, and flowering/fruiting states correlated with mean size of these states and were 0.76 (0.05), 0.92 (0.06), and 1 (0.00), respectively, for previously tagged plants, and 0.84 (0.08), 0.75 (0.22), and 0 (0.00), respectively, for previously untagged plants. (We had insufficient data to obtain a satisfactory estimate of previously untagged flowering plants). 4. Our estimates are for a medium-sized plant in a small and intensively surveyed plot. It is possible that detectability is even lower for larger plots and smaller plants or smaller life -states (e.g. seedlings) and that detectabilities < I are widespread in plant demographic studies. 5. State -dependent detectabilities are especially worrying since they will lead to a size- or state -biased sample from the study plot. Failure to incorporate detectability into demographic estimation methods introduces a bias into most estimates of population parameters such as fecundity, recruitment, mortality, and transition rates between life -states. We illustrate this by a simple example using a matrix model, where a hypothetical population was stable but, due to imperfect detection, wrongly projected to be declining at a rate of 8% per year. 6. Almost all plant demographic studies are based on models for discrete states. State and size are important predictors both for demographic rates and detectability . We suggest that even in studies based on small plots, state - or size-specific detectability should be estimated at least at some point to avoid biased inference about the dynamics of the population sampled.

Journal of Ecology↗

USGS2003

Demographic stochasticity in small remnant populations of the declining distylous plant Primula veris

We studied ecological consequences of distyly for the declining perennial plant Primula veris in the Swiss Jura. Distyly favours cross-fertilization and avoids inbreeding, but may lead to pollen limitation and reduced reproduction if morph frequencies deviate from 50 %. Disassortative mating is promoted by the reciprocal position of stigmas and anthers in the two morphs (pin and thrum) and by intramorph incompatibility and should result in equal frequencies of morphs at equilibrium. However, deviations could arise because of demographic stochasticity, the lower intra-morph incompatibility of the pin morph, and niche differentiation between morphs. Demographic stochasticity should result in symmetric deviations from an even morph frequency among populations and in increased deviations with decreasing population size. If crosses between pins occurred, these would only generate pins, and this could result in a pin-bias of morph frequencies in general and in small populations in particular. If the morphs have different niches, morph frequencies should be related to environmental factors, morphs might be spatially segregated, and morphological differences between morphs would be expected. We tested these hypotheses in the declining distylous P. veris. We studied morph frequencies in relation to environmental conditions and population size, spatial segregation in field populations, morphological differences between morphs, and growth responses to nutrient addition. Morph frequencies in 76 populations with 1 - 80000 flowering plants fluctuated symmetrically about 50 %. Deviations from 50 % were much larger in small populations, and sixof the smallest populations had lost one morph altogether. In contrast, morph frequencies were neither related to population size nor to 17 measures of environmental conditions. We found no spatial segregation or morphological differences in the field or in the common garden. The results suggest that demographic stochasticity caused deviations of the morph ratiofrom unity in small populations. Demographic stochasticity was probably caused by the random elimination of plants during the fragmentation of formerly large continuous populations. Biased morph frequencies may be one of the reasons for the strongly reduced reproduction in small populations of P. veris.

Basic and Applied Ecology↗