231 research outputs found
River dynamics shape clonal diversity and genetic structure of an Amazonian understorey herb
No full text1. Clonal herbs are an important feature of the understorey of Amazonian forests. However, little is known about the environmental drivers determining the population genetics of these herbs and about the spatial scale of gene flow. 2. With amplified fragment length polymorphism markers, we analysed the clonal diversity and genetic structure of a clonal Amazonian herb (Heliconia metallica) in south-eastern Peru at two spatial scales. First, we sampled leaves in 24 patches differing in flooding frequency and canopy openness in 2 km(2) of a floodplain forest, and second in 21 riverine populations from the Andean foothills to the Amazon lowlands along a 550-km stretch of river. 3. At the small spatial scale in the floodplain forest, the clonal diversity of H. metallica was higher at flooded than at non-flooded sites, but clonal diversity did not increase with canopy openness. 4. At the large spatial scale, clonal diversity was very low in riverine populations at up- and downriver sites, suggesting that seedling recruitment was higher at mid-altitudes where the flooding intensity is intermediate. Genetic diversity of riverine populations monotonously increased downriver, indicating unidirectional gene flow mediated by hydrochory. 5. Genetic differentiation among riverine populations was very low (F-ST = 0.06) and followed an isolation-by-distance pattern, indicating a stepping-stone type of gene flow by seeds. Despite the much smaller spatial scale, genetic differentiation among patches in the floodplain forest was higher (F-ST = 0.16), due to spatially restricted gene flow in the forest understorey. 6. Synthesis. The genetic structure of H. metallica is the result of seedling recruitment being largely limited to flooded sites and of hydrochoric seed dispersal between populations growing on river-banks. We conclude that river dynamics are the major determinant of the genetic structure of Amazonian plants and that largely undisturbed river systems, such as the Amazon, provide a crucial vector for gene flow, even at large spatial scales.German Academic Exchange Service; German National Academic Foundatio
Improved estimation of gut passage time considerably affects trait‐based dispersal models
No full text1. Animals are important vectors for transporting seeds, nutrients and microbes across landscapes. However, models that quantify the magnitude of these ecosystem services across a broad range of taxa often rely on generalised mass-based scaling parameters for gut passage time. This relationship is weak and fundamentally breaks down when considering individual species, indicating that current models may incorrectly attribute or estimate the magnitude of dispersal.
2. We collated a large dataset of gut passage time for endothermic animals measured using undigested markers (n = 391 species). For each species, we compiled trait data, including body mass, morphology, gut physiology, diet and phylogeny. We then compared the ability of five statistical models (constant, generalised least squares, phylogenetic generalised least squares, general linear model and random forest) to estimate the time of first marker appearance (transit time; TT) and mean marker retention time (MRT) for particle and solute markers in mammals and birds separately.
3. For mammals, we found that the inclusion of additional traits appreciably reduced the median root-mean
squared error across all markers in a leave-one-out cross validation. For birds, however, additional traits did not significantly improve our ability to predict gut passage time across markers. This may have occurred due to the smaller number of bird species included in our analysis or the absence of important explanatory factors such as differences in gastrointestinal morphology.
4. Using the MRTparticle random forest model from this study, we updated two trait-based dispersal models for seed and nutrient movement by mammals. The magnitude of dispersal in our updated predictions ranged from 66% to 176% of the original model formulation for different scenarios, highlighting the importance of gut passage time for dispersal models. Furthermore, the contribution by individual or groups of species was found sizeably altered in our updated models.
5. Future modelling studies of dispersal by mammals, for which empirical estimates of gut passage time are absent, will benefit from predicting gut passage time using statistical models that incorporate traits including animal morphology, diet and gut physiology
Anpassen oder aussterben: Artenvielfalt im Klimawandel : wie Pflanzen und Tiere aufeinander angewiesen sind
No full textHeute sterben zehn- bis hundertmal mehr Arten aus, als dies ohne die massiven Einflüsse des Menschen auf das System »Erde«der Fall wäre. Der Artenforscher Matthias Schleuning untersucht, wie Pflanzen und Tiere in komplexen Ökosystemen voneinander abhängen. Damit kann er voraussagen, wer zu den Gewinnern und den Verlierern von Klimawandel und stärkerer Landnutzung werden wird
Growth form and fruit and flower numbers of plants on Kilimanjaro
No full textThis dataset includes the identities of all fruiting and flowering plants on all study sites sampled for mutualistic interactions (i.e. all except for Hel and Fer). We identified plants to species level, and recorded some general information on size and growth form (e.g. tree, shrub, liana). Additonally, we counted the number of fruits and flowers on each plant individual to estimate crop size
Elevation-dependent effects of forest fragmentation on plant-bird interaction networks in the tropical Andes
No full textDirect and indirect effects of plant and frugivore diversity on structural and functional components of fruit removal by birds
No full textMorphometric fruit traits of plants on Kilimanjaro
No full textThis dataset includes morphological trait measurements of all plant species recorded on the study sites of Mt. Kilimanjaro. We measured traits related to size and supply of the fruits. For instance, the size and shape of a fruit determines which species may successfully exploit it. Additionally, the forest stratum in which plants display their fruits will attract a specific range of species. The data was collected in the field from 3 individuals for each species
Bird communities on the study plots of the Kilimanjaro Research Unit (only bird individuals that have been observed below maximum vegetation height)
No full textWe used audiovisual point counts on eight subplots per plot to record birds in the warm dry season (December to March) and in the cold dry season (July to October). We established circles with a 20-m radius in densely vegetated habitats (savanna and all forest habitats) and 35.5 m × 35.5 m squares at Helichrysum plots, covering the same sampling area in all habitat types. All birds heard or seen in one subplot were counted for 10 min and identified to species level. Point counts started 15 min before sunrise and were completed before 09:00. All 480 point counts (30 plots × 8 subplots × 2 seasons) were conducted by the same observer (Ferger S. W.) to reduce inter-observer variability. This dataset contains (a) only subplots at which birds were observed and (b) no birds that were observed higher than maximum canopy height of the respective subplot in the respective season, which was achieved by combining "Data on bird communities on the study plots of the Kilimanjaro Research Unit including all bird individuals observed during point counts" and "Data on habitat characters and fruit and flower abundance on the study plots of the Kilimanjaro Research Unit". (a) is a simple modification and (b) is a common (and often necessary!) modification of ornithological datasets. Note, however, that this causes three plots to be removed from the dataset, as no birds were observed on those: hel4 in the cold season and hel3 in both seasons. This means that if you e.g. intend to calculate species richness across all plots, you may want to include them manually with a species richness of zero. If you do not know what these modifications mean in terms of suitability of this dataset for your intended analyses, you should definitively contact the data owner and maybe consider the original and complete dataset "Data on bird communities on the study plots of the Kilimanjaro Research Unit including all bird individuals observed during point counts"
Data on bird communities on the study plots of the Kilimanjaro Research Unit including all bird individuals observed during point counts
No full textWe used audiovisual point counts on eight subplots per plot to record birds in the warm dry season (December to March) and in the cold dry season (July to October). We established circles with a 20-m radius in densely vegetated habitats (savanna and all forest habitats) and 35.5 m × 35.5 m squares at Helichrysum plots, covering the same sampling area in all habitat types. All birds heard or seen in one subplot were counted for 10 min and identified to species level. Point counts started 15 min before sunrise and were completed before 09:00. All 480 point counts (30 plots × 8 subplots × 2 seasons) were conducted by the same observer (Ferger S. W.) to reduce inter-observer variability. This dataset contains also bird individuals that were recorded higher than maximum canopy height at the respective subplots in the respective season (see publication for details)
Data on vegetation structure on the study plots of the Kilimanjaro Research Unit
No full textWe established eight subplots per plot. These subplots consisted of circles with a 20-m radius in densely vegetated habitats (savanna and all forest habitats) and 35.5 m × 35.5 m squares at Helichrysum plots, covering the same sampling area in all habitat types. We quantified vertical vegetation heterogeneity, canopy height and canopy closure on every subplot in the warm dry season (December to March) and in the cold dry season (July to October). To obtain vertical vegetation heterogeneity, we first estimated the vegetation cover in layers at 0, 1, 2, 4, 8, 16, 32 and 64 m above ground and then calculated the Shannon–Wiener diversity index across these eight strata. Canopy height was measured with a laser rangefinder as maximum canopy height above ground. Canopy closure was measured as the mean percentage of closed cells from four spherical canopy densitometer readings taken at the centre of each subplot in the four cardinal directions
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