Forest edge creates small‐scale variation in reproductive rate of sika deer

Abstract

Habitat edges are considered to have an important role in determining the abundance of deer in forest landscapes, but to our knowledge there are few lines of evidence indicating that forest edge enhances the vital rate of deer. We examined pregnancy of female sika deer in Boso peninsula, central Japan, and explored how forest edges, food availability in forests, and local population density influence the pregnancy rate of sika deer. Local deer density was estimated by the number of fecal pellets, and food availability in forests was estimated by combining GIS data of vegetation distribution and the relationship between vegetation biomass and local deer density. Forest edge length was also determined by GIS data. Model selection was performed with multiple logistic regression analyses using the AIC to find the best model for accounting for the observed variation in pregnancy rates of the deer. Multiple logistic regression analysis showed that the length of forest edge had a positive effect on the pregnancy rate of females, whereas food availability in forests and local deer density had little effect. This forest edge effect was detected in a 100-200-m radius from deer captured locations, indicating that deer pregnancy is primarily determined by habitat quality within a 10-ha area. This result was confirmed by tracking females with GPS telemetry, which found that the core areas of the home range were less than 12 ha. The positive effect of edges and the lack of density dependence could be a result of high plant productivity in open environments that produces forages not depleted by high deer densities. Our results support the view that land management is the cause of the current problem of deer overabundance.

Roe and fallow deer: are they compatible neighbours?

“…These habitats, at the boundary between wood and open fields, are mostly covered by bushes, interspersed with pastures, providing food and cover to deer species. Ecotone areas are an optimal habitat for deer, determining high reproductive success (Miyashita et al 2008;McLoughlin et al 2007) and great densities (Wahlström and Kjellander 1995;Hemami et al 2005).…”

Habitat–performance relationships: finding the right metric at a given spatial scale

“…Adult white-footed mouse (Peromyscus leucopus) females have longer residence times in old fields, leading mice occupying this habitat type to have higher fitness (Halama & Dueser 1994) and higher adult survival in forests than in other habitats (Morris & Davidson 2000). The length of forest edge within the maternal home range mostly determined pregnancy rates of sika deer (Cervus nippon): all females with a home range including more than 80 m ha 21 of forest edge were pregnant, whereas only half of females with a home range including less than 50 m ha 21 were pregnant (Miyashita et al 2008). Besides the effects of resource-related differences of habitat quality on individual fitness, mortality risk from predation or human-related causes is increasingly assessed as a main driver of spatial variation in individual fitness in several vertebrate populations.…”

Distribution of the oriental weatherloach, Misgurnus anguillicaudatus, in paddy fields and its implications for conservation in Sado Island, Japan

“…The values of factors (6)-(8) were obtained using the GIS software ''ArcGIS'' (ESRI Japan, Tokyo). With regards to factor (6), the spatial extent at which the variables should be extracted was not clear, so we generated buffers of given radii (200, 400, 600, 800, 1,000, 1,200, 1,400 and 1,600 m) around each site and then calculated the proportion of paddy field area within the buffered area (e.g., Miyashita et al 2008). With regards to factor (7), for the same reason, the slope variables were obtained from the average slopes in 200, 400,…,1,600 m rasterized grid squares using the GIS software.…”

“…GIS tools have therefore taken on an increasingly significant role by providing the means to achieve the quantitative modeling of potential habitats for species (Johnson and Patil 2006). These tools enable the prediction of either the presence/absence or the abundance of individuals in a particular graphical area through the analysis of multi-scale variables from immediate surroundings to landscape features (Manel et al 2001;Sergio et al 2003Sergio et al , 2004Miyashita et al 2008;Zarri et al 2008;Menke et al 2009). This kind of habitat suitability modeling has multiple applications in conservation biology (Manel et al 2001).…”