1/15/2023 0 Comments Intr earthnet![]() ![]() Then the collection of birth and death data over twelve months provided an instantaneous growth rate which was necessary for modelling a logistic population growth curve. ![]() A mark-recapture population survey using an adjusted Lincoln Peterson estimate was undertaken to infer the current population density. A lack of weather data for the north-eastern side of Unguja, Zanzibar also prompted an onsite weather station to gather baseline data for a better understanding of the island’s climate conditions.Ī comprehensive vegetation assessment confirmed suitable species diversity and phenology for the maintenance of the duiker population. The outcomes of the study would be instrumental in managing the sub population and creating more suitable conditions to provide a sustainable harvest for reintroduction of offspring into other conservation sites (to be identified by the Government) in the rest of the Zanzibar Archipelago.Ī variety of inquiries were planned and performed to obtain an overview of the natural habitat, interactions of the Aders’ duiker with its environment, each other, and with the Suni antelope. Due to the Aders’ duiker’s endangered status, this natural habitat is essential to the survival of the species.įor the first time, a study was initiated to investigate the ecology of the Aders’ duiker on Mnemba island to aid in determining a feasible carrying capacity to better manage the protected species on this Island. Together, the two antelope species have been negatively impacted the island’s natural forest condition over time. ![]() Additionally, no Aders’ duiker has been removed since introduction to the island. ![]() Despite government attempts to control the population size of Suni antelope, the tiny antelope proliferates at a seemingly high rate. Mnemba Island has been housing a population of the vulnerable Aders’ duiker (Cephalophus adersi) since 2005, sharing the small, forested area with a prolific population of Suni antelope (Neotragus moschatus) (both species re-introduced). The explanatory power of the models produced was low, and I discuss reasons for this – including the use of a novel response variable. I find that drivers differ across varying initial forest cover, with this factor being the largest predictor of deforestation severity. To determine drivers of deforestation severity in coastal Tanzania, I use generalised additive models (GAMs) to describe the non-linear relationship between deforestation severity and a set of geographic, biogeographic, and socioeconomic data. Here I examine 3 sources of remotely sensed data (supplemented Landsat data, MOD12Q1 data, and MOD44B data) and their suitability for use in deforestation models. It can also be used to analyse protected area effectiveness. Model development can be used to elucidate the drivers of deforestation, predict the location of future deforestation, produce scenarios of future deforestation rates, inform the design of government policy, and provide a baseline against which to test for additionality in programmes such as REDD+. These forest remnants are threatened by further fragmentation, degradation, and deforestation. Closer examination has revealed a unique and diverse ecosystem, home to exceptional levels of endemism across many major taxa, distributed heterogeneously across several hundred forest patches. The coastal forests of Tanzania have typically been overlooked in favour of the more spectacular Eastern Arc Mountains. ![]()
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