Computational aspects of N-mixture models

The N-mixture model is widely used to estimate the abundance of a population in the presence of unknown detection probability from only a set of counts subject to spatial and temporal replication (Royle, 2004, Biometrics 60,105–115). We explain and exploit the equivalence of N-mixture and multivariate Poisson and negative-binomial models, which provides powerful new approaches for fitting these models. We show that particularly when detection probability and the number of sampling occasions are small, infinite estimates of abundance can arise. We propose a sample covariance as a diagnostic for this event, and demonstrate its good performance in the Poisson case. Infinite estimates may be missed in practice, due to numerical optimization procedures terminating at arbitrarily large values. It is shown that the use of a bound, K, for an infinite summation in the N-mixture likelihood can result in underestimation of abundance, so that default values of K in computer packages should be avoided. Instead we propose a simple automatic way to choose K. The methods are illustrated by analysis of data on Hermann’s tortoise Testudo hermanni

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

Improving estimates of environmental change using multilevel regression models of Ellenberg indicator values

Ellenberg indicator values (EIVs) are a widely used metric in plant ecology comprising a semi-quantitative description of species‘ ecological requirements. Typically, point estimates of mean EIV scores are compared over space or time to infer differences in the environmental conditions structuring plant communities – particularly in resurvey studies where no historical environmental data are available. However, the use of point estimates as a basis for inference does not take into account variance among species EIVs within sampled plots, and gives equal weighting to means calculated from plots with differing numbers of species. Traditional methods are also vulnerable to inaccurate estimates where only incomplete species lists are available. We present a set of multilevel (hierarchical) models – fitted with and without group-level predictors (for eg. habitat type) – to improve precision and accuracy of plot mean EIV scores, and to provide more reliable inference on changing environmental conditions over spatial and temporal gradients in resurvey studies. We compare multilevel model performance to GLMM’s fitted to point estimates of mean EIVs. We also test the reliability of this method to improve inferences with incomplete species lists in some or all sample plots. Hierarchical modelling led to more accurate and precise estimates of plot-level differences in mean EIV scores between time-periods, particularly for datasets with incomplete records of species occurrence. Furthermore, hierarchical models revealed directional environmental change within ecological habitat types, which less precise estimates from GLMM’s of raw mean EIVs were inadequate to detect. The ability to compute separate residual variance and adjusted R^2 parameters for plot mean EIVs and temporal differences in plot mean EIVs in multilevel models also allowed us to uncover a prominent role of hydrological differences as a driver of community compositional change in our case study, which traditional use of EIVs would fail to reveal. Assessing environmental change underlying ecological communities is a vital issue in the face of accelerating anthropogenic change. We have demonstrated that multilevel modelling of EIVs allows for a nuanced estimation of such from plant assemblage data changes at local scales and beyond, leading to a better understanding of temporal dynamics of ecosystems. Further, the ability of these methods to perform well with missing data should increase the total set of historical data which can be used to this end

Foundations of bird surveys and implications for the collection and analysis of data in large-scale monitoring programs

Großräumige Monitoringprogramme stellen eine zweistufige Stichprobe dar: Zuerst wird eine räumliche Stichprobe ausgewählt und danach eine Stichprobe an beobachteten Individuen, besetzten Flächen oder Arten. Damit die in Monitoringprogrammen gewonnenen Zahlen interpretierbar bleiben, muss die räumliche Stichprobe „definiert zufällig“ erfolgen, ansonsten können Verfälschungen auftreten. Außerdem muss beachtet werden, dass Zählungen und Vorkommensbeobachtungen („Präsenz-Absenz-Daten“) binomiale Zufallsgrößen sind, ganz analog zum Wurf einer Münze. Die Binomialverteiltung stellt sozusagen das „Grundgesetz der Bestandserhebung“ dar und besagt, dass Zählungen (Z) erstens auch unter identischen Bedingungen automatisch streuen, und dass sie zweitens im Durchschnitt einem Anteil p der vorhandenen Bestände N entsprechen, wobei p die Antreffwahrscheinlichkeit darstellt. Drittens beinhaltet ein Vergleich zwischen zwei oder mehr Zählungen immer gleichzeitig einen Vergleich der Bestände N und der Antreffwahrscheinlichkeit p. Das bedeutet, dass ein Zeittrend in Zählungen zustande kommen kann durch einen realen Bestandstrend, durch einen Trend in der Antreffwahrscheinlichkeit oder durch eine Kombination von beidem. Eine direkte Interpretation von Zählungen impliziert immer die Annahme, dass p = 1 oder dass p konstant sei. Es ist nützlich, sich die Entstehung von Vogelzählungen hierarchisch, d. H. mehrstufig vorzustellen: In einem ersten Schritt entstehen die wahren Bestände und im zweiten die Zählungen in Abhängigkeit der Bestände und der Antreffwahrscheinlichkeit p. Extrainformation ist nötig, um die wahren Bestände korrigiert für p zu schätzen. Diese Extrainformation besteht in der Regel aus Distanzinformation oder aus wiederholten Beobachtungen, woraus Distance-Sampling- und Fangwiederfang- Methoden die echten Bestände oder das wahre Vorkommen zu schätzen vermögen. In den vergangenen Jahren haben wir im Schweizer Brutvogelmonitoringprogramm MHB mehrere Analyseverfahren vom Fangwiederfang-Typ getestet und stellen diese und unsere Befunde zusammenfassend kurz vor. Diese Methoden korrigieren für den binomialen „Beobachtungsfehler“, der allen Vogelzählungen und Vorkommensbeobachtungen inhärent ist. Wir glauben, dass man an Methoden wie den hier illustrierten eigentlich nicht vorbei kommt, wenn bei Monitoringprogrammen absolute Bestandsgrößen vonnöten sind oder wenn man für „gefährliche Muster“ in der Antreffwahrscheinlichkeit, z. B. Zeittrends in p, korrigieren möchte.Large-scale monitoring programs represent a two-level, nested sampling scheme: first, a spatial sample of quadrats or other study sites is selected, within which a second sample, of individuals, occupied quadrats or species, is chosen. To produce meaningful numbers, a monitoring program ought to be based on a spatial probability sample, otherwise the inferences obtained may be biased with respect to the desired statistical population about which one wants to learn something. Moreover, all bird counts and detection-nondetection records (misleadingly also called “presence-absence data”) are binomial random variables, much like the flip of a coin. The binomial distribution is the theoretical basis of all animal or plant surveys and explains and predicts all of their most salient features: 1. repeated counts C vary automatically, even under identical conditions; 2. on average, a count amounts to a proportion p of true population size N , where p is the detection probability, and 3. any comparison between two or more counts represents the simultaneous comparison of the associated true population size N and of the detection probability p. For instance, a temporal trend in counts may be due to a genuine trend in the underlying population size or to a trend in detection probability or to a combination of the two. Any direct interpretation of counts always implies one of two assumptions, either that of p = 1 or that of p < 1 constant. It is useful to think about the genesis of bird counts in a hierarchical way. In a first random process, the true population sizes are generated. In a second random process, the actual counts are generated conditional on these true population sizes and on detection probability. For inference about the underlying true population size free from distorting effects of the observation process, extra information is required, which usually comes as distance information or as repeated observation of a system within a period of closure. Then, distance sampling and capturerecapture methods can be used to estimate true population size or true distributions, corrected for imperfect detection. During the past few years, we have used data from the Swiss breeding bird survey MHB to experiment with, adapt and develop several such methods of the capture-recapture type. Here, we review these briefly, describe some of our key findings and provide pointers to more specific work. These methods correct counts and detection-nondetection data for the binomial observation error inherent in all bird observations. We believe that use of these methods is hard to avoid in a monitoring program if absolute population size or the absolute extent of distributional ranges, corrected for imperfect detection, are required, or if one needs to correct for “dangerous patterns” in detection probability, for instance time trends in p

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

Positive effects of cyanogenic glycosides in food plants on larval development of the common blue butterfly

Cyanogenesis is a widespread chemical defence mechanism in plants against herbivory. However, some specialised herbivores overcome this protection by different behavioural or metabolic mechanisms. In the present study, we investigated the effect of presence or absence of cyanogenic glycosides in birdsfoot trefoil (Lotus corniculatus, Fabaceae) on oviposition behaviour, larval preference, larval development, adult weight and nectar preference of the common blue butterfly (Polyommatus icarus, Lycaenidae). For oviposition behaviour there was a female-specific reaction to cyanogenic glycoside content; i.e. some females preferred to oviposit on cyanogenic over acyanogenic plants, while other females behaved in the opposite way. Freshly hatched larvae did not discriminate between the two plant morphs. Since the two plant morphs differed not only in their content of cyanogenic glycoside, but also in N and water content, we expected these differences to affect larval growth. Contrary to our expectations, larvae feeding on cyanogenic plants showed a faster development and stronger weight gain than larvae feeding on acyanogenic plants. Furthermore, female genotype affected development time, larval and pupal weight of the common blue butterfly. However, most effects detected in the larval phase disappeared for adult weight, indicating compensatory feeding of larvae. Adult butterflies reared on the two cyanogenic glycoside plant morphs did not differ in their nectar preference. But a gender-specific effect was found, where females preferred amino acid-rich nectar while males did not discriminate between the two nectar mimics. The presented results indicate that larvae of the common blue butterfly can metabolise the surplus of N in cyanogenic plants for growth. Additionally, the female-specific behaviour to oviposit preferably on cyanogenic or acyanogenic plant morphs and the female-genotype-specific responses in life history traits indicate the genetic flexibility of this butterfly species and its potential for local adaptatio

Bioinformatikai adatbázisok

Tipikus bioinformatikai feladatokat megoldó eljárások készítése adatbázisokhoz, adatbáziskezelők hatékonyságának összehasonlítása ezen műveletek végzése során