SCALING FOREST HABITAT SUITABILITY USING AN ENDANGEREDSPECIES AS BIOINDICATOR

Posted by on November 20, 2020 in Paper Presentations | 0 comments

Isabella Pauline L. Quijano (1), Chito L. Patino (1), Mary Joyce L. Flores (1)(2)

ABSTRACT

Despite its biological significance, the role of forests in food security and
sustainability is often overlooked. Forests are vital in providing ecosystem services and have
contributed directly and indirectly to the livelihoods of an estimated one billion people globally. The
continued degradation of forests prompts the need for timely and scientific approaches to guide
conservation targets and policies. This study aimed to assess forest biodiversity and habitat quality
through the habitat suitability model of the black shama (Copsychus cebuensis) as a biodiversity and
environmental indicator for terrestrial biodiversity areas in Cebu Island, Philippines. A multi-criteria
approach was explored using a predictive habitat suitability model based on Weights-of-Evidence
method through the estimation of a set of environmental predictor variables from GIS and remotelysensed data. With the use of Bayesian rules, evidence layers in the form of environmental variables
were then combined in a weighted spatial overlay to produce a single map of probability and
occurrence. The resulting map was divided into 3 ranges of suitability, 3 as highly suitable, 2 as
moderately suitable, and 1 as not suitable. The majority of the island especially those in the key
biodiversity areas are only classified as moderately suitable (76.8%). Highly suitable areas for the
black shama are very limited (1.26%) which indicates the need to expand the priority areas and focus
conservation in key biodiversity areas with lower habitat suitability indices. The suitability model
can be used to locate and prioritize critical areas that will serve as basis for developing appropriate
conservation plans throughout Cebu island.

KEY WORDS

Biodiversity Conservation; Black Shama; Habitat Suitability Model, GIS

Link to the Article

https://a-a-r-s.org/proceeding/ACRS2020/lobwva.pdf