Wildlife Habitat Management, and Deer

Ecology ◽  
1974 ◽  
Vol 55 (3) ◽  
pp. 684-685
Author(s):  
Frank E. Egler
Keyword(s):  
Habitat Management ◽  
Wildlife Habitat

Techniques for Wildlife Habitat Management of Wetlands

1994 ◽  
Vol 58 (3) ◽  
pp. 589
Author(s):  
Robert L. Meeks ◽  
Neil F. Payne
Keyword(s):  
Habitat Management ◽  
Wildlife Habitat

scholarly journals Spatial Modeling of Forage Crops for Tiger Prey Species in the Area Surrounding Highway 304 in the Dong Phayayen-Khao Yai Forest Complex

2021 ◽  
Vol 19 (3) ◽  
pp. 220-229
Author(s):  
Paanwaris Paansri ◽  
◽  
Natcha Sangprom ◽  
Warong Suksavate ◽  
Aingorn Chaiyes ◽  
...  
Keyword(s):  
Spatial Modeling ◽  
Habitat Management ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Linear Models ◽  
Wildlife Habitat ◽  
Forage Crops ◽  
Grass Biomass ◽  
Sambar Deer ◽  
Shrub Biomass

Spatial modeling is an analytical procedure that simulates real-world conditions using remote sensing and geographic information systems. The field data in this study were collected from 318 survey plots in the area surrounding highway 304 in the Dong Phayayen-Khao Yai Forest Complex (DPKY-FC) during the 2019 rainy season. Forage-crop biomass was collected from all plots. We focused on sambar deer (Rusa unicolor) and gaur (Bos gaurus), which are the main prey for tigers in this area. We created spatial models using generalized linear models with stepwise regression. The results indicated that the normalized difference vegetation index (NDVI) varied directly with grass biomass but inversely with shrub biomass (p<0.05). Elevation varied directly with forb biomass but inversely with shrub biomass (p<0.05). The probability of occurrence of sambar deer varied directly with distance from disturbance variables, distance from the stream, and grass biomass (p<0.001), but inversely with NDVI (p<0.05). The occurrence of gaur varied directly with NDVI (p=0.08), but varied inversely with slope, distance from the road, and distance from the stream (p<0.05). Our results demonstrate that spatial modeling can be an effective tool for wildlife habitat management in the area surrounding highway 304.

scholarly journals Gunnison Forks wildlife habitat management plan : CO-03 WHA-T2 /

1981 ◽  
Author(s):  
James R. Ferguson ◽  
Michael J. Blymyer ◽  
◽  
◽  
◽  
...  
Keyword(s):  
Habitat Management ◽  
Management Plan ◽  
Wildlife Habitat

Quantifying physical characteristics of wildland fuels using the Fuel Characteristic Classification SystemThis article is one of a selection of papers published in the Special Forum on the Fuel Characteristic Classification System.

2007 ◽  
Vol 37 (12) ◽  
pp. 2413-2420 ◽  
Author(s):  
Cynthia L. Riccardi ◽  
Susan J. Prichard ◽  
David V. Sandberg ◽  
Roger D. Ottmar
Keyword(s):  
Classification System ◽  
Habitat Management ◽  
Expert Knowledge ◽  
Fire Effects ◽  
Physical Characteristics ◽  
Wildlife Habitat ◽  
Fuel Characteristics ◽  
Fuel Characteristic ◽  
Fuel Components ◽  
Selection Of

Wildland fuel characteristics are used in many applications of operational fire predictions and to understand fire effects and behaviour. Even so, there is a shortage of information on basic fuel properties and the physical characteristics of wildland fuels. The Fuel Characteristic Classification System (FCCS) builds and catalogues fuelbed descriptions based on realistic physical properties derived from direct or indirect observation, inventories, expert knowledge, inference, or simulated fuel characteristics. The FCCS summarizes and calculates wildland fuel characteristics, including fuel depth, loading, and surface area. Users may modify fuelbeds and thereby capture changing fuel conditions over time and (or) under different management prescriptions. Fuel loadings from four sample fuelbed pairs (i.e., pre- and post-prescribed fire) were calculated and compared by using FCCS to demonstrate the versatility of the system and how individual fuel components, such as shrubs, nonwoody fuels, woody fuels, and litter, can be calculated and summarized. The ability of FCCS to catalogue and summarize complex fuelbeds and reflect dynamic fuel conditions allows calculated results to be used in a variety of applications including surface and crown fire predictions, carbon assessments, and wildlife habitat management.

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