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 idividual 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 > 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. Previous and foreseeable future applications of CR in plant ecology Since the paper by Alexander et al. (1997), it has become increasingly clear that CR models may be useful for demographic analysis of plant populations. In the future, we are likely to see increasing use of these methods that were originally developed for animal populations. Here is a summary about all previous applications that I have come across. I am grateful if readers point out to me any titles that I may have missed. If a reliable way to mark seeds can be devised, CR might indeed provide the analysis tool for tackling one of the ultimate frontiers in plant population ecology: the dynamics of the seed bank. Indeed, the first ever application of CR to plants that I have come across (Naylor, 1972) used a fluorescent dye to mark seeds and a Lincoln–Peterson–type estimator to estimate the seed bank size in an agricultural weed. The application of CR to plants with dormancy has been treated by hefferson et al. (2001, 2003), Kéry et al. (submitted) and Kéry & Gregg (submitted). Population size, and survival rates of plants whose aboveground states are easily overlooked have been estimated for an elusive prairie plant (Alexander et al., 1997; Slade et al., 2003) and for a tropical savannah tree (Lahoreau et al., 2003). For plot–based plant demographic studies, we have shown previously that (not surprisingly) different life–states may have different detection probabilities, and that this may seriously bias inference from population modelling (Kéry & Gregg, 2003). It is somewhat astonishing that there still appear to be no applications of CR to the analysis of plant populations and communities. For instance, species richness, patch occupancy, population extinction rates, and species turnover in communities are all still based on adding up the raw data, even though the animal literature has plenty of papers showing more adequate ways of estimating these quantities (e.g., Boulinier et al., 1998; Nichols et al., 1998). I have submitted a note (Kéry, submitted) describing the use of the Cormack–Jolly–Seber model to estimate extinction probabilities for plant populations in a manner exactly analogous to patch occupancy models (MacKenzie et al., 2002, 2003). It is perhaps in plant community ecology where we will see most future applications of CR

In-situ Clean-up and OPLC Fractionation of Chamomile Flower Extract Searching Active Components by Bioautography

Bioassay-guided isolation of antibacterial components of chamomile flower methanol extract was performed by OPLC with on-line detection, fractionation combined with sample clean-up in-situ in the adsorbent bed after sample application. The antibacterial effect of the fractions and the separated compounds remained on the adsorbent layer (do not overrun during OPLC separation) was tested with direct bioautography (DB) against the bioluminescent Pseudomonas savastanoi pv. maculicola and Vibrio fischeri. The fractions with great biologically activity were analysed by SPME-GC-MS and LC-MS/MS and the two active uneluted compounds were characterized by OPLC-MS using interface. Mainly essential oil components, coumarins, flavonoids, phenolic acids and fatty acids were identified in the fractions

Principes d’un bon monitoring

Si un monitoring respecte certaines règles, il permettra de formuler des énoncés fiables sur l’état et l’évolution des effectifs des organismes étudiés. La sélection scrupuleuse des échantillons et la réduction de l’erreur de mesure revêtent une importance capitale

Prinzipien eines guten Monitorings

Werden bei einem Monitoring bestimmte Regeln eingehalten, können zuverlässige Aussagen zum Zustand und zur Veränderung der Bestände der untersuchten Organismen gemacht werden. Von zentraler Bedeutung sind eine saubere Auswahl der Stichprobe und die Minimierung des Messfehlers

Accounting for heterogeneity when estimating stopover duration, timing and population size of red knots along the Luannan Coast of Bohai Bay, China

To successfully perform their long‐distance migrations, migratory birds require sites along their migratory routes to rest and refuel. Monitoring the use of so‐called stopover and staging sites provides insights into (a) the timing of migration and (b) the importance of a site for migratory bird populations. A recently developed Bayesian superpopulation model that integrates mark–recapture data and ring density data enabled the estimation of stopover timing, duration, and population size. Yet, this model did not account for heterogeneity in encounter (p) and staying (ϕ) probabilities. Here we extended the integrated superpopulation model by implementing finite mixtures to account for heterogeneity in p and ϕ. We used simulations and real data (from 2009–2016) on red knots Calidris canutus, mostly of the subspecies piersmai, staging in Bohai Bay, China, during spring migration to (a) show the importance of accounting for heterogeneity in encounter and staying probabilities to get unbiased estimates of stopover timing, duration, and numbers of migratory birds at staging sites and (b) get accurate stopover parameter estimates for a migratory bird species at a key staging site that is threatened by habitat destruction. Our simulations confirmed that heterogeneity in p affected stopover parameter estimates more than heterogeneity in ϕ, especially when most birds had low p. Bias was particularly severe when most birds had both low ϕ and p. Bias was largest for population size, intermediate for stopover duration and negligible for stopover timing. A total of 50,000–100,000 red knots were estimated to annually stop for 5–9 days in Bohai Bay between 10 and 30 May. This shows the key importance of this staging site for this declining species. There were no clear changes in stopover parameters over time, although stopover population size was substantially lower in 2016 than in preceding years. Our study shows the importance of accounting for heterogeneity in both encounter and staying probabilities for accurately estimating stopover duration and population size and provides an appropriate modeling framework

Demographic drivers of decline and recovery in an Afro-Palaearctic migratory bird population

Across Europe, rapid population declines are ongoing in many Afro-Palaearctic migratory bird species, but the development of appropriate conservation actions across such large migratory ranges is severely constrained by lack of understanding of the demographic drivers of these declines. By constructing regional integrated population models (IPMs) for one of the suite of migratory species that is declining in the south-east of Britain but increasing in the north-west, we show that, while annual population growth rates in both regions vary with adult survival, the divergent regional trajectories are primarily a consequence of differences in productivity. Between 1994 and 2012, annual survival and productivity rates ranged over similar levels in both regions, but high productivity rates were rarer in the declining south-east population and never coincided with high survival rates. By contrast, population growth in the north-west was fuelled by several years in which higher productivity coincided with high survival rates. Simulated population trajectories suggest that realistic improvements in productivity could have reversed the decline (i.e. recovery of the population index to ≥ 1) in the south-east. Consequently, actions to improve productivity on European breeding grounds are likely to be a more fruitful and achievable means of reversing migrant declines than actions to improve survival on breeding, passage or sub-Saharan wintering grounds

Models of spatiotemporal variation in rabbit abundance reveal management hot spots for an invasive species

First published: 25 January 2020The European rabbit (Oryctolagus cuniculus) is a notorious economic and environmental pest species in its invasive range. To better understand the population and range dynamics of this species, 41 years of abundance data have been collected from 116 unique sites across a broad range of climatic and environmental conditions in Australia. We analyzed this time series of abundance data to determine whether inter‐annual variation in climatic conditions can be used to map historic, contemporary, and potential future fluctuations in rabbit abundance from regional to continental scales. We constructed a hierarchical Bayesian regression model of relative abundance that corrected for observation error and seasonal biases. The corrected abundances were regressed against environmental and disease variables in order to project high spatiotemporal resolution, continent‐wide rabbit abundances. We show that rabbit abundance in Australia is highly variable in space and time, being driven primarily by inter‐annual variation in temperature and precipitation in concert with the prevalence of a non‐pathogenic virus. Moreover, we show that inter‐annual variation in local spatial abundances can be mapped effectively at a continental scale using highly resolved spatiotemporal predictors, allowing “hotspots” of persistently high rabbit abundance to be identified. Importantly, cross‐validated model performance was fair to excellent within and across distinct climate zones. Long‐term monitoring data for invasive species can be used to map fine‐scale spatiotemporal fluctuations in abundance patterns when accurately accounting for inherent sampling biases. Our analysis provides ecologists and pest managers with a clearer understanding of the determinants of rabbit abundance in Australia, offering an important new approach for predicting spatial abundance patterns of invasive species at the near‐term temporal scales that are directly relevant to resource management.Stuart C Brown, Konstans Wells, Emilie Roy-Dufresne, Susan Campbell, Brian Cooke, Tarnya Cox, Damien A. Fordha

Estimating arthropod survival probability from field counts: a case study with monarch butterflies

Survival probability is fundamental for understanding population dynamics. Methods for estimating survival probability from field data typically require marking individuals, but marking methods are not possible for arthropod species that molt their exoskeleton between life stages. We developed a novel Bayesian state‐space model to estimate arthropod larval survival probability from stage‐structured count data. We performed simulation studies to evaluate estimation bias due to detection probability, individual variation in stage duration, and study design (sampling frequency and sample size). Estimation of cumulative survival probability from oviposition to pupation was robust to potential sources of bias. Our simulations also provide guidance for designing field studies with minimal bias. We applied the model to the monarch butterfly (Danaus plexippus), a declining species in North America for which conservation programs are being implemented. We estimated cumulative survival from egg to pupation from monarch counts conducted at 18 field sites in three landcover types in Iowa, USA, and Ontario, Canada: road right‐of‐ways, natural habitats (gardens and restored meadows), and agricultural field borders. Mean predicted survival probability across all landcover types was 0.014 (95% CI: 0.004–0.024), four times lower than previously published estimates using an ad hoc estimator. Estimated survival probability ranged from 0.002 (95% CI: 7.0E−7 to 0.034) to 0.058 (95% CI: 0.013–0.113) at individual sites. Among landcover types, agricultural field borders in Ontario had the highest estimated survival probability (0.025 with 95% CI: 0.008–0.043) and natural areas had the lowest estimated survival probability (0.008 with 95% CI: 0.009–0.024). Monarch production was estimated as adults produced per milkweed stem by multiplying survival probabilities by eggs per milkweed at these sites. Monarch production ranged from 1.0 (standard deviation [SD] = 0.68) adult in Ontario natural areas in 2016 to 29.0 (SD = 10.42) adults in Ontario agricultural borders in 2015 per 6809 milkweed stems. Survival estimates are critical to monarch population modeling and habitat restoration efforts. Our model is a significant advance in estimating survival probability for monarch butterflies and can be readily adapted to other arthropod species with stage‐structured life histories

Causes and consequences of spatial variation in sex ratios in a declining bird species

1. Male-biased sex ratios occur in many bird species, particularly in those with small or declining populations, but the causes of these skews and their consequences for local population demography are rarely known. Within-species variation in sex ratios can help to identify the demographic and behavioural processes associated with such biases. 2. Small populations may be more likely to have skewed sex ratios if sex differences in survival, recruitment or dispersal vary with local abundance. Analyses of species with highly variable local abundances can help to identify these mechanisms and the implications for spatial variation in demography. Many migratory bird species are currently undergoing rapid and severe declines in abundance in parts of their breeding ranges, and thus have sufficient spatial variation in abundance to explore the extent of sex ratio biases, their causes and implications. 3. Using national-scale bird ringing data for one such species (willow warbler, Phylloscopus trochilus), we show that sex ratios vary greatly across Britain, and that male-biased sites are more frequent in areas of low abundance, which are now widespread across much of south and east England. These sex ratio biases are sufficient to impact local productivity, as the relative number of juveniles caught at survey sites declines significantly with increasing sex ratio skew. 4. Sex differences in survival could influence this sex ratio variation, but we find little evidence for sex differences in survival increasing with sex ratio skew. In addition, sex ratios have become male-biased over the last two decades but there are no such trends in adult survival rates for males or females. This suggests that lower female recruitment into low abundance sites is contributing to these skews. 5. These findings suggest that male-biased sex ratios in small and declining populations can arise through local-scale sex-differences in survival and dispersal, with females recruiting disproportionately into larger populations. Given the high level of spatial variation in population declines and abundance of many migratory bird species across Europe at present, male-biased small populations may be increasingly common. As singing males are the primary records used in surveys of these species, and as unpaired males often sing throughout the breeding season, local sex ratio biases could also be masking the true extent of these population declines

Network‐scale effects of invasive species on spatially‐structured amphibian populations

Understanding the factors affecting the dynamics of spatially‐structured populations (SSP) is a central topic of conservation and landscape ecology. Invasive alien species are increasingly important drivers of the dynamics of native species. However, the impacts of invasives are often assessed at the patch scale, while their effects on SSP dynamics are rarely considered. We used long‐term abundance data to test whether the impact of invasive crayfish on subpopulations can also affect the whole SSP dynamics, through their influence on source populations. From 2010 to 2018, we surveyed a network of 58 ponds and recorded the abundance of Italian agile frog clutches, the occurrence of an invasive crayfish, and environmental features. Using Bayesian hierarchical models, we assessed relationhips between frog abundance in ponds and a) environmental features; b) connectivity within the SSP; c) occurrence of invasive species at both the patch‐ and the SSP‐levels. If spatial relationships between ponds were overlooked, we did not detect effects of crayfish presence on frog abundance or trends. When we jointly considered habitat, subpopulation and SSP features, processes acting at all these levels affected frog abundance. At the subpopulation scale, frog abundance in a year was related to habitat features, but was unrelated to crayfish occurrence at that site during the previous year. However, when we considered the SSP level, we found a strong negative relationship between frog abundance in a given site and crayfish frequency in surrounding wetlands during the previous year. Hence, SSP‐level analyses can identify effects that would remain unnoticed when focussing on single patches. Invasive species can affect population dynamics even in not invaded patches, through the degradation of subpopulation networks. Patch‐scale assessments of the impact of invasive species can thus be insufficient: predicting the long‐term interplay between invasive and native populations requires landscape‐level approaches accounting for the complexity of spatial interactions