Temporal Samples (temporal + sample)

Distribution by Scientific Domains


Selected Abstracts


Are ectoparasite communities structured?

JOURNAL OF ANIMAL ECOLOGY, Issue 6 2006
Species co-occurrence, null models, temporal variation
Summary 1We studied temporal variation in the structure of flea communities on small mammalian hosts from eastern Slovakia using null models. We asked (a) whether flea co-occurrences in infracommunities (in the individual hosts) in different hosts as well as in the component communities (in the host species) demonstrate a non-random pattern; (b) whether this pattern is indicative of either positive or negative flea species interactions; (c) whether this pattern varies temporally; and (d) whether the expression of this pattern is related to population size of either fleas or hosts or both. 2We constructed a presence/absence matrix of flea species for each temporal sample of a host species and calculated four metrics of co-occurrence, namely the C -score, the number of checkerboard species pairs, the number of species combinations and the variance ratio (V -ratio). Then we compared these metrics with the respective indices calculated for 5000 null matrices that were assembled randomly using two algorithms, namely fixed-fixed (FF) and fixed-equiprobable (FE). 3Most co-occurrence metrics calculated for real data did not differ significantly from the metrics calculated for simulated matrices using the FF algorithm. However, the indices observed for 42 of 75 presence/absence matrices differed significantly from the null expectations for the FE models. Non-randomness was detected mainly by the C -score and V -ratio metrics. In all cases, the direction of non-randomness was the same, namely the aggregation, not competition, of flea species in host individuals and host species. 4The inclusion or exclusion of the uninfested hosts in the FE models did not affect the results for individual host species. However, exclusion of the uninfested host species led to the acceptance of the null hypothesis for only six of 13 temporal samples of the component flea communities for which non-randomness was detected when the uninfested hosts were included in the analysis. 5In most host species, the absolute values of the standardized size effect of both the C -score and V -ratio increased with an increase in host density and a concomitant decrease in flea abundance and prevalence. 6Results of this study demonstrated that (a) flea assemblages on small mammalian hosts were structured at some times, whereas they appeared to be randomly assembled at other times; (b) whenever non-randomness of flea co-occurrences was detected, it suggested aggregation but never segregation of flea species in host individuals or populations; and (c) the expression of structure in flea assemblages depended on the level of density of both fleas and hosts. [source]


Ecological boundary detection using Carlin,Chib Bayesian model selection

DIVERSITY AND DISTRIBUTIONS, Issue 6 2005
Ralph Mac Nally
ABSTRACT Sharp ecological transitions in space (ecotones, edges, boundaries) often are where ecologically important events occur, such as elevated or reduced biodiversity or altered ecological functions (e.g. changes in productivity, pollination rates or parasitism loads, nesting success). While human observers often identify these transitions by using intuitive or gestalt assignments (e.g. the boundary between a remnant woodland patch and the surrounding farm paddock seems obvious), it is clearly desirable to make statistical assessments based on measurements. These assessments often are straightforward to make if the data are univariate, but identifying boundaries or transitions using compositional or multivariate data sets is more difficult. There is a need for an intermediate step in which pairwise similarities between points or temporal samples are computed. Here, I describe an approach that treats points along a transect as alternative hypotheses (models) about the location of the boundary. Carlin and Chib (1995) introduced a Bayesian technique for comparing non-hierarchical models, which I adapted to compute the probabilities of each boundary location (i.e. a model) relative to the ensemble of models constituting the set of possible points of the boundary along the transect. Several artificial data sets and two field data sets (on vegetation and soils and on cave-dwelling invertebrates and microclimates) are used to illustrate the approach. The method can be extended to cases in with several boundaries along a gradient, such as where there is an ecotone of non-zero thickness. [source]


Are ectoparasite communities structured?

JOURNAL OF ANIMAL ECOLOGY, Issue 6 2006
Species co-occurrence, null models, temporal variation
Summary 1We studied temporal variation in the structure of flea communities on small mammalian hosts from eastern Slovakia using null models. We asked (a) whether flea co-occurrences in infracommunities (in the individual hosts) in different hosts as well as in the component communities (in the host species) demonstrate a non-random pattern; (b) whether this pattern is indicative of either positive or negative flea species interactions; (c) whether this pattern varies temporally; and (d) whether the expression of this pattern is related to population size of either fleas or hosts or both. 2We constructed a presence/absence matrix of flea species for each temporal sample of a host species and calculated four metrics of co-occurrence, namely the C -score, the number of checkerboard species pairs, the number of species combinations and the variance ratio (V -ratio). Then we compared these metrics with the respective indices calculated for 5000 null matrices that were assembled randomly using two algorithms, namely fixed-fixed (FF) and fixed-equiprobable (FE). 3Most co-occurrence metrics calculated for real data did not differ significantly from the metrics calculated for simulated matrices using the FF algorithm. However, the indices observed for 42 of 75 presence/absence matrices differed significantly from the null expectations for the FE models. Non-randomness was detected mainly by the C -score and V -ratio metrics. In all cases, the direction of non-randomness was the same, namely the aggregation, not competition, of flea species in host individuals and host species. 4The inclusion or exclusion of the uninfested hosts in the FE models did not affect the results for individual host species. However, exclusion of the uninfested host species led to the acceptance of the null hypothesis for only six of 13 temporal samples of the component flea communities for which non-randomness was detected when the uninfested hosts were included in the analysis. 5In most host species, the absolute values of the standardized size effect of both the C -score and V -ratio increased with an increase in host density and a concomitant decrease in flea abundance and prevalence. 6Results of this study demonstrated that (a) flea assemblages on small mammalian hosts were structured at some times, whereas they appeared to be randomly assembled at other times; (b) whenever non-randomness of flea co-occurrences was detected, it suggested aggregation but never segregation of flea species in host individuals or populations; and (c) the expression of structure in flea assemblages depended on the level of density of both fleas and hosts. [source]


Distribution of genetic variation in farmed and natural stocks of european eel

JOURNAL OF FISH BIOLOGY, Issue 2004
J. M. Pujolar
European eel (Anguilla anguilla; Teleostei) is a valuable commercial species. However, over the past 25 years, the population of European eel has been declining to such a degree that major concerns have been raised for its long-term conservation. Since little information is available on the life-cycle and genetic structure of European eel, it has been difficult to evaluate the existence of any population substructuring. Molecular genetic methods contribute to a better knowledge of the demography and population structure in marine fish. In addition, management strategies and conservation goals must consider information on genetic substructuring as well as on life history patterns. The aim of the study is to provide more detailed knowledge on the genetic variability, demography and population substructuring of European eel by analysing and comparing natural and farmed individuals. Natural eel samples have been obtained in two geographical sites (Netherlands, France) including temporal samples in a short-scale (within years) and a long-scale (between years). Simultaneously, farmed glass eels have been grown in two separate batches during one year. Batches have been monitored and genetic samples have been obtained during the year. A combination of selection-sensitive (allozymes) and selection-neutral markers (microsatellites) has been used in the study since selection seems to play an important role in the determination of the quality of future eel spawners. Results suggest a positive correlation between growth and genetic variability since individuals attaining a large length and mass present significant higher heterozygosities. [source]


THE STATE OF THE FIELD: Combining contemporary and ancient DNA in population genetic and phylogeographical studies

MOLECULAR ECOLOGY RESOURCES, Issue 5 2010
MIGUEL NAVASCUÉS
Abstract The analysis of ancient DNA in a population genetic or phylogeographical framework is an emerging field, as traditional analytical tools were largely developed for the purpose of analysing data sampled from a single time point. Markov chain Monte Carlo approaches have been successfully developed for the analysis of heterochronous sequence data from closed panmictic populations. However, attributing genetic differences between temporal samples to mutational events between time points requires the consideration of other factors that may also result in genetic differentiation. Geographical effects are an obvious factor for species exhibiting geographical structuring of genetic variation. The departure from a closed panmictic model require researchers to either exploit software developed for the analysis of isochronous data, take advantage of simulation approaches using algorithms developed for heterochronous data, or explore approximate Bayesian computation. Here, we review statistical approaches employed and available software for the joint analysis of ancient and modern DNA, and where appropriate we suggest how these may be further developed. [source]


Temporal genetic heterogeneity of juvenile orange-spotted grouper (Epinephelus coioides, Pisces: Serranidae)

AQUACULTURE RESEARCH, Issue 10 2009
Panuwat Pumitinsee
Abstract Juveniles of orange-spotted grouper (Epinephelus coioides), a tropical serranid species, are heavily harvested for aquaculture seeds from nursing grounds in several Southeast Asian countries. Because juveniles of similar sizes are present in a nursery area throughout the year, we aimed to determine whether more than one genetically distinct population contributes to juvenile aggregations. We examined the temporal genetic heterogeneity of juvenile aggregations collected at four different times of the year at a nursery area in coastal waters of the Andaman Sea in Trang province, Thailand. Also, we examined the differences between these temporal samples and an outgroup collected from the Gulf of Thailand (Chantaburi). The genetic variation at six polymorphic microsatellite loci within each sample was moderate, with observed heterozygosities across all loci ranging from 0.551 to 0.629 and number of alleles per locus ranging from 7.0 to 8.33. Results indicated substantial genetic differences between the two geographically distant samples, Trang and Chantaburi (Fst=0.040,0.050, P<0.005), and between the July sample and the remaining samples from Trang (Fst=0.096,0.106, P<0.005). The observed temporal genetic heterogeneity of E. coioides juveniles may reflect high variability in the reproductive success of each spawning event and the existence of spatially isolated groups of spawners. [source]