Effect of climate seasonality and vegetation cover on floral resource selection by two stingless bee species

2021 ◽

Vol 918 (1) ◽

pp. 012004

Author(s):

R S Wahyuningtyas ◽

W Halwany ◽

S Siswadi ◽

S S Hakim ◽

B Rahmanto ◽

...

Keyword(s):

Vegetation Cover ◽

Stingless Bee ◽

Food Sources ◽

Plant Identification ◽

Honey Production ◽

Average Production ◽

Location Type ◽

Type 3

Abstract Honey production depends on the availability of the landscape as a habitat for producing bee’s food sources. The purpose of this study was to determine different landscapes as a habitat for kelulut (Heterotrigona itama) bees in producing honey from 5 different stingless bee locations. The research was conducted in three districts: Hulu Sungai Tengah, Hulu Sungai Selatan and Tapin District, South Kalimantan Province. This research was conducted to record the types of vegetation in each landscape, which can be divided into three categories; 1 location was a combination type of forest and garden (type 1), 2 locations was a combination type of settlement, shrub, and paddy fields (type 2), and 1 location was a combination type of settlement, plantation, and shrub (type 3). Each meliponiculture also recorded the honey production every month. The results showed that the farmers’ number of beehives was between 96 and 252 hives/farmer. The average production in the rainy season is 0.17 L hive-1year−1, and the dry season is 0.24 L hive−1year−1. Honey production per year for each location was as follows: location type 1 produces 1.59 L hive−1, location type 2 produces 1.85 L hive−1, and location type 3 produces 2.41 L hive−1. Plant identification results at each type of location showed that the number of species found at vegetation cover type 1, 2, and 3 was 116, 128, and 107 species, respectively. At the farms with vegetation cover types 2 and 3, many different flowering shrubs provide year-round forage for the stingless bee.

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Floral resource selection by wild bees and honey bees in the Midwest U.S.A: Implications for designing pollinator habitat

Restoration Ecology ◽

10.1111/rec.13456 ◽

2021 ◽

Author(s):

Stacy C. Simanonok ◽

Clint R.V. Otto ◽

Deborah A. Buhl

Keyword(s):

Honey Bees ◽

Resource Selection ◽

Wild Bees ◽

Floral Resource

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Habitat selection by wolves and mountain lions during summer in western Montana

PLoS ONE ◽

10.1371/journal.pone.0254827 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254827

Author(s):

Collin J. Peterson ◽

Michael S. Mitchell ◽

Nicholas J. DeCesare ◽

Chad J. Bishop ◽

Sarah S. Sells

Keyword(s):

Habitat Selection ◽

Vegetation Cover ◽

Resource Selection ◽

Puma Concolor ◽

The United States ◽

Western Montana ◽

Predator Species ◽

Mountain Lions ◽

Out Of Sample ◽

Hunting Mode

In the Northern Rockies of the United States, predators like wolves (Canis lupus) and mountain lions (Puma concolor) have been implicated in fluctuations or declines in populations of game species like elk (Cervus canadensis) and mule deer (Odocoileus hemionus). In particular, local distributions of these predators may affect ungulate behavior, use of space, and dynamics. Our goal was to develop generalizable predictions of habitat selection by wolves and mountain lions across western Montana. We hypothesized both predator species would select habitat that maximized their chances of encountering and killing ungulates and that minimized their chances of encountering humans. We assessed habitat selection by these predators during summer using within-home range (3rd order) resource selection functions (RSFs) in multiple study areas throughout western Montana, and tested how generalizable RSF predictions were by applying them to out-of-sample telemetry data from separate study areas. Selection for vegetation cover-types varied substantially among wolves in different study areas. Nonetheless, our predictions of 3rd order selection by wolves were highly generalizable across different study areas. Wolves consistently selected simple topography where ungulate prey may be more susceptible to their cursorial hunting mode. Topographic features may serve as better proxies of predation risk by wolves than vegetation cover-types. Predictions of mountain lion distribution were less generalizable. Use of rugged terrain by mountain lions varied across ecosystem-types, likely because mountain lions targeted the habitats of different prey species in each study area. Our findings suggest that features that facilitate the hunting mode of a predator (i.e. simple topography for cursorial predators and hiding cover for stalking predators) may be more generalizable predictors of their habitat selection than features associated with local prey densities.

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Ecological correlates of the distribution of territorial Svalbard rock ptarmigan (Lagopus muta hyperborea)

Canadian Journal of Zoology ◽

10.1139/z06-197 ◽

2007 ◽

Vol 85 (1) ◽

pp. 122-132 ◽

Cited By ~ 13

Author(s):

Å.Ø. Pedersen ◽

J.U. Jepsen ◽

N.G. Yoccoz ◽

E. Fuglei

Keyword(s):

Vegetation Cover ◽

Resource Selection ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Habitat Type ◽

Model Performance ◽

Rock Ptarmigan ◽

Habitat Models ◽

Logistic Regression Models ◽

Lagopus Muta

Predictive habitat models have become important research and management tools for monitoring the spatial distribution and abundance of wildlife species. In this paper we develop and evaluate statistical habitat models for presence of territorial Svalbard rock ptarmigan ( Lagopus muta hyperborea Sundevall, 1845) cocks in spring and apply the best model to assess ptarmigan habitat selection in a larger extrapolated region. Terrain variables were extracted at detailed (10 m digital elevation model (DEM)) and coarse (50 m DEM) scales to compare model performance. Sets of candidate environmental variables related to terrain and vegetation cover were developed and explanatory variables were calculated at increasing distances from the count site to well above the typical size of ptarmigan territory. We used ecological niche factor analysis to describe the difference between used and available sites. Survey sites used by cocks were characterized by a restricted range of altitude, a high degree of terrain heterogeneity, and dense vegetation cover compared with overall site availability in the survey region. We then used model selection criteria (AICc) to find the most parsimonious logistic regression models estimating habitat resource selection functions for cocks. Detailed terrain variables were better predictors than coarse terrain variables. The normalized difference vegetation index (NDVI) was a good predictor of presence of territorial cocks, but not as good as the most preferred habitat type. Owing to limited availability of high-quality vegetation maps, the best model containing NDVI and 10 m DEM variables was used for extrapolation of male ptarmigan habitat. Our results show that it is possible to obtain a model with a high ability to rank habitats using a low number of map-derived variables. Such rankings can then be used to improve field sampling designs and are therefore a useful tool for management and conservation of ptarmigan and wildlife in Arctic and alpine areas.

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