Predicting the spring abundance distribution of red-legged partridge populations in agricultural regions using environmental models and an application for game management

Abstract

Abstract. Several series of available environmental (land use/land cover, agriculture, soil, climate) variables are used in exploratory models to test their use for successful prediction of red-legged partridge (Alectoris rufa L.) abundance in spring. A Geographic Information System and stepwise multiple regression analysis are used to show and predict distribution of this population parameter in an agricultural region of southern France. High spring abundance was observed to be distributed mainly in the central and north-western part of the study area. Two partial models, land use/land cover and agriculture, and a complete model with land use and temperature variables are the most significant and more accurate than any others. The complete model is the best model (lowest Akaike Information Criterion and highest Akaike weight). The potential abundance obtained from this best model shows communes with high Kilometric Abundance Indices (KAI), mainly located in the northwestern part of the region. Partridge abundance was unevenly or irregularly distributed across the study area, which is typical of wildlife species inhabiting complex and changing landscapes limited by various sources of human pressure, such as agriculture, urbanization and game management. A game tool is provided using potential spring abundance to plan the harvest quotas two months before opening the hunting season.

Main ecological gradients and landscape matrix affect wild rabbitOryctolagus cuniculus(Linnaeus, 1758) (Mammalia: Leporidae) abundance in a coastal region (South-East Spain)

“…It is, therefore, an area of great contrasts, which goes from the coastal zone to valleys and sheer areas over a relatively short distance, thus providing a rich landscape system (Peña et al 2007;Belda et al 2011;Zaragozí et al 2012). Furthermore, comparable studies have been conducted on game species in nearby regions, using similar methods (Arques 2000;Jiménez-García 2007;Peiro & Blanc 2011;Belda & Zaragozí et al 2012). In agricultural landscapes like ours, KAIs can be used, depending on the aims of study.…”

“…The data of the rabbit sightings detected during the census, in each of the land cover matrices and sampling periods, are presented using the kilometric abundance index (KAI) (Ferry & Frochot 1958;Peiro & Blanc 2011), expressed as individuals per linear kilometer. In this way, the mean KAI has been obtained by dividing the total number of individuals detected by the total number of transects undertaken in a particular matrix (natural, abandoned, dry and irrigated).…”