Distribution and Occupancy modelling of the globally threatened Cheer pheasant Catreus wallichii in Nepal [/et_pb_text][/et_pb_column][/et_pb_row][et_pb_row _builder_version=”3.12.2″ custom_padding=”27px|0px|14px|0px|false|false”][et_pb_column type=”2_3″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″]
Sonam Tashi Lama
Institute Of Forestry, Tribhuvan University, Pokhara, Kaski, Nepal
The distribution and occurrence of cheer pheasant (Catreus wallichii) in Nepal is poorly known and studied carried out till date are concentrated on some small patches of its assumed distribution range. This talk addresses the potential distribution of the cheer pheasant in Nepal and assessed its occurrence in and around Dhorpatan Hunting Reserve, western Nepal along with perception of local people towards its conservation.
We used randomly selected calling stations and carried out replicated det/non-det surveys to produce the detection history of each calling stations. We analyzed det/non-det data using PRESENCE to model the occupancy. We also compiled cheer pheasant presence locations from previous published and unpublished sources and ran RandomForest algorithm to access the potential distribution using bio-climatic variables related to precipitation and temperature.
We produced a first quantitative potential distribution model of cheer pheasant for Nepal. The model suggests that the cheer pheasant is patchily distributed in the western mid-hills of Nepal extending to around Kaligandaki river valley in the east. Multiple occupancy models were supported by the data with occupancy estimation of 0.63. It is found that grassland areas positively influence the occurrence of cheer and the detection probability is constant.
The finding of this study will be useful in determining species ranges; and is in agreement with current species ranking and IUCN status. Also our finding can be used as a quantitative and robust baseline for developing landscape level monitoring efforts that require optimizing cost, effort and time using more ecological and social covariates to reliably estimate species occurrence and focus the species conservation efforts.[/et_pb_text][/et_pb_column][et_pb_column type=”1_3″ _builder_version=”3.12.2″ parallax=”off” parallax_method=”on”][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Subscribe” _builder_version=”3.12.2″ background_color=”rgba(0,0,0,0.37)” background_color_gradient_direction=”96deg” background_image=”http://18.104.22.168/~sccs/public_html/wp-content/uploads/2018/09/5-1.jpg” background_blend=”overlay” custom_margin=”|||” custom_padding=”0||0||true|false” saved_tabs=”all” global_module=”309″][et_pb_row _builder_version=”3.0.48″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”][et_pb_column type=”1_4″ _builder_version=”3.0.47″ parallax=”off” parallax_method=”on”][et_pb_text _builder_version=”3.12.2″]