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Habitat fragmentation results to displacement of inhabiting floral and faunal species. The resulting geographic isolation of various species affect regeneration, genetic flows and recruitment. Hence, a study was conducted in a forested area of Mt. Malindawag in Naawan, Misamis Oriental. Sampling stations were designated at the agro-forest, mid-forest and upper-forest habitat types. Species characterizations were based on DAO 2007-01 and IUCN Red List for conservation status. Results showed highest diversity index of flora at mid-forest while lowest diversity was observed in the agro-forest area. A tree species Canarium racemosum obtained highest Species Importance Value (SIV) at 38.6%, 42% and 30.8%, respectively in the three habitat types. The highest endemicity of flora was at mid-forest with 24% per DAO 2007-01 and 26% per IUCN conservation status. Majority of faunal species were birds that were mostly resident and common and were usually observed at upper-forest habitat. The relatively low diversity and endemicity of flora and fauna species could be due to the influx of human population. Various activities undertaken were threatening the inhabiting biodiversity, and therefore, demand immediate protection and conservation measures from formulating policies to increasing awareness of various stakeholders. Future related studies were recommended to increase scientific understanding on the interrelationships of socio-economic and ecological interactions of biodiversity to the inhabiting human population.
Interaction networks are a tool to visualize and to study the relationships between interacting species across and within trophic levels. Recent research uncovered many properties of such networks that remained undetected in previous food web studies. These patterns could be related to evolutionary and ecological processes. The study of interaction networks promises therefore progress in the study of constraints that act on the coevolution of interacting species and on food webs. However, there are still many pitfalls associated with the statistical analysis, the properties of the metrics involved and the appropriate null model choice. Here I review the mechanisms that shape interaction matrices, the possible internal structures and their ecological interpretation, and the analytical tools to identify matrix structure. Progress in the field needs critical meta-analytical and comparative studies that indentify the best suited null models (low type I and II error probabilities and high power to disentangle statistical from ecological processes) and clarify the interdependence of different concepts and metrics associated with network approaches. It is not improbable that many patterns recently associated with ecological and evolutionary processes might turn out to be simple side effects of the sampling from the underlying metacommunity distributions.
This paper presents data on temporal and spatial variability and ecological interactions of bacteria in a Scottish woodland over a winter – spring period (January – April). The study sites covered an area of 1 ha and a range of woodland habitats formed by beech (Fagus silvatica), birch (Betula pendula × pubescens) and oak (Quercus petraea), as well as (one site) a clearance site covered with grass (predominantly Holcus lanatus). Subsamples of fresh litter were fragmented for 60 s in a domestic food processor and were subsequently used to estimate the abundance of bacteria by counting under a fluorescent microscope. The preparation of bacterial slides involved staining with DTAF following extraction in phosphate buffer. The data on protozoa, fungi and microinvertebrates were available from parallel research and were obtained using standard methods. Numbers of bacteria appeared to be lower in sites dominated by beech. The highest average bacterial abundance (9.07 × 108 cells g⁻¹ dry litter) was registered in January, and then gradually declined till March, when the lowest (7.37 × 10⁸ cells g⁻¹ dry litter) value was found, before rising again in April. The only significant difference revealed by one-way ANOVA was between January and March results. Both date and site effects were found to be significant by two-way ANOVA, but the date × site interaction was not significant. A number of significant relationships were registered by stepwise regression analysis, ANCOVA, and correlation analysis. In stepwise regression analysis, the most important predictor for bacterial density was litter moisture content (all months but March). Further significant relationships were revealed with the abundance of fungi, nematodes, and microarthropods, and forest litter fractions of moss, needles, beech seeds and birch leaves. ANCOVA confirmed the importance of interactions with litter composition and moisture content, and the abundance of fungi and microarthropods, and revealed a relationship with the abundance of ciliates. Correlation analysis for separate months revealed various relationships with forest litter composition (including positive – with forest litter fractions of oak leaves, grass, roots, birch leaves, and negative ones – with forest litter fractions of ferns and seeds), and the abundance of other microbiota, including positive with Folsomia candida (Insecta, Apterygota, Collembola), fungi, plant and microbial feeding nematodes, tardigrades and enchytraeids, positive and negative with ciliates, and negative with predatory nematodes. Most of these relationships, plus a further correlation with the abundance of amoebae, were also revealed for the combined dataset. It should be noted that some of these interactions (e.g. with % grass, % roots, the density of Folsomia candida) were only revealed by correlation analysis, and may therefore be judged as less important than relationships registered by all statistical methods applied. The results of this study highlighted the complexity of multivariate interactions of bacteria in forest litter.
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