Application of regression model to identify a parameter that best defines species diversity in the coastal forests of Tanzania

Abstract

Coastal forests of Tanzania are diverse in plant species that make them included as part of the 34 world biodiversity hotspots. This study aimed at  determining plant species diversity, richness, and evenness as well as to identify the parameter that best defines plant species diversity in the three coastal forests; namely Zaraninge, Kazimzumbwi and Pande.  Transect method was used for vegetation data collection and subjected to Analysis of variance and regression techniques. The plant species composition among forests ranged between 75 and 146 with a significantly lower diversity index in Zaraninge (2.057 ± 0.112) than those in Pande (2.415 ± 0.022) and Kazimzumbwi (2.578 ± 0.092) forests. The plant species were more evenly distributed in Zaraninge (0.488 ± 0.004) than in Pande (0.452 ± 0.016) and Kazimzumbwi (0.457 ± 0.025) with no significant difference among them. The species richness per plot was significantly lower in Zaraninge forest (14 ± 1) than Kazimzumbwi forest (20 ± 2). Data on evenness and richness were correlated with plant diversity indices at different levels. A perfect positive correlated occurs with evenness (r =1) but lower with richness (Zaraninge, r = 0.88; Pande, r = 0.91 and r = 0.79 for Kazimzumbwi). This implies that richness and evenness parameters portray different ecological interpretations of the biodiversity value within an ecosystem and cannot be used interchangeably. Regression models showed that species evenness significantly determined plant species diversity, whereas richness was not significant. This study concludes that evenness stands a better chance of being the best predictor of change in plant species diversity and therefore an adequate measure of the coastal forests ' conservation value than richness.

Key words:  Coastal  forest, conservation, diversity, evenness, richness, regression model