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The spatial distribution of Mustelidae in France.

Calenge C, Chadoeuf J, Giraud C, Huet S, Julliard R, Monestiez P, Piffady J, Pinaud D, Ruette S - PLoS ONE (2015)

Bottom Line: Because a large number of detected animals in a region could have been the result of a high sampling pressure there, we modeled the number of detected animals as a function of the sampling effort to allow for unbiased estimation of the species density.For living animals, we had no way to measure the sampling effort.We demonstrated that it was possible to use the whole dataset (dead and living animals) to estimate the following: (i) the relative density -- i.e., the density multiplied by an unknown constant -- of each species of interest across the different French agricultural regions, (ii) the sampling effort for living animals for each region, and (iii) the relative detection probability for various species of interest.

View Article: PubMed Central - PubMed

Affiliation: Office national de la chasse et de la faune sauvage, Direction des études et de la recherche, Saint Benoist, BP 20. 78612 Le Perray en Yvelines, France.

ABSTRACT
We estimated the spatial distribution of 6 Mustelidae species in France using the data collected by the French national hunting and wildlife agency under the "small carnivorous species logbooks" program. The 1500 national wildlife protection officers working for this agency spend 80% of their working time traveling in the spatial area in which they have authority. During their travels, they occasionally detect dead or living small and medium size carnivorous animals. Between 2002 and 2005, each car operated by this agency was equipped with a logbook in which officers recorded information about the detected animals (species, location, dead or alive, date). Thus, more than 30000 dead or living animals were detected during the study period. Because a large number of detected animals in a region could have been the result of a high sampling pressure there, we modeled the number of detected animals as a function of the sampling effort to allow for unbiased estimation of the species density. For dead animals -- mostly roadkill -- we supposed that the effort in a given region was proportional to the distance traveled by the officers. For living animals, we had no way to measure the sampling effort. We demonstrated that it was possible to use the whole dataset (dead and living animals) to estimate the following: (i) the relative density -- i.e., the density multiplied by an unknown constant -- of each species of interest across the different French agricultural regions, (ii) the sampling effort for living animals for each region, and (iii) the relative detection probability for various species of interest.

No MeSH data available.


Spatial distribution of the 6 Mustelidae species of interest as estimated by a purely spatially regularized model, with a high penalty parameter ν = 20 (darker areas correspond to higher densities).
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pone.0121689.g006: Spatial distribution of the 6 Mustelidae species of interest as estimated by a purely spatially regularized model, with a high penalty parameter ν = 20 (darker areas correspond to higher densities).

Mentions: We used a ridge-type regularization to improve the prediction of our model by accounting for a spatial and environmental structure in the species density. Silverman [35] noted the following about another statistical method relying on a smoothing parameter: “the process of examining several plots of the data, all smoothed by different amounts, may well give more insight into the data than merely considering a single automatically produced curve”. Although this is not the main aim of our study, this remark applies to our modeling approach as well. Indeed, we replaced the spatial and environmental proximity metrics used in our regularization by a purely spatial proximity, with πjm = 1 when SARs j and m are adjacent, and πjm = 0 otherwise. Setting the parameter ν to a very large value (ν = 20) allowed us to identify the large scale distribution patterns of the species over the study area (Fig. 6). Although these maps are characterized by a larger bias and for this reason should not be used for management, they can be useful for exploration purposes. These maps indeed illustrated more clearly the broad presence areas identified in the Fig. 3.


The spatial distribution of Mustelidae in France.

Calenge C, Chadoeuf J, Giraud C, Huet S, Julliard R, Monestiez P, Piffady J, Pinaud D, Ruette S - PLoS ONE (2015)

Spatial distribution of the 6 Mustelidae species of interest as estimated by a purely spatially regularized model, with a high penalty parameter ν = 20 (darker areas correspond to higher densities).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4374970&req=5

pone.0121689.g006: Spatial distribution of the 6 Mustelidae species of interest as estimated by a purely spatially regularized model, with a high penalty parameter ν = 20 (darker areas correspond to higher densities).
Mentions: We used a ridge-type regularization to improve the prediction of our model by accounting for a spatial and environmental structure in the species density. Silverman [35] noted the following about another statistical method relying on a smoothing parameter: “the process of examining several plots of the data, all smoothed by different amounts, may well give more insight into the data than merely considering a single automatically produced curve”. Although this is not the main aim of our study, this remark applies to our modeling approach as well. Indeed, we replaced the spatial and environmental proximity metrics used in our regularization by a purely spatial proximity, with πjm = 1 when SARs j and m are adjacent, and πjm = 0 otherwise. Setting the parameter ν to a very large value (ν = 20) allowed us to identify the large scale distribution patterns of the species over the study area (Fig. 6). Although these maps are characterized by a larger bias and for this reason should not be used for management, they can be useful for exploration purposes. These maps indeed illustrated more clearly the broad presence areas identified in the Fig. 3.

Bottom Line: Because a large number of detected animals in a region could have been the result of a high sampling pressure there, we modeled the number of detected animals as a function of the sampling effort to allow for unbiased estimation of the species density.For living animals, we had no way to measure the sampling effort.We demonstrated that it was possible to use the whole dataset (dead and living animals) to estimate the following: (i) the relative density -- i.e., the density multiplied by an unknown constant -- of each species of interest across the different French agricultural regions, (ii) the sampling effort for living animals for each region, and (iii) the relative detection probability for various species of interest.

View Article: PubMed Central - PubMed

Affiliation: Office national de la chasse et de la faune sauvage, Direction des études et de la recherche, Saint Benoist, BP 20. 78612 Le Perray en Yvelines, France.

ABSTRACT
We estimated the spatial distribution of 6 Mustelidae species in France using the data collected by the French national hunting and wildlife agency under the "small carnivorous species logbooks" program. The 1500 national wildlife protection officers working for this agency spend 80% of their working time traveling in the spatial area in which they have authority. During their travels, they occasionally detect dead or living small and medium size carnivorous animals. Between 2002 and 2005, each car operated by this agency was equipped with a logbook in which officers recorded information about the detected animals (species, location, dead or alive, date). Thus, more than 30000 dead or living animals were detected during the study period. Because a large number of detected animals in a region could have been the result of a high sampling pressure there, we modeled the number of detected animals as a function of the sampling effort to allow for unbiased estimation of the species density. For dead animals -- mostly roadkill -- we supposed that the effort in a given region was proportional to the distance traveled by the officers. For living animals, we had no way to measure the sampling effort. We demonstrated that it was possible to use the whole dataset (dead and living animals) to estimate the following: (i) the relative density -- i.e., the density multiplied by an unknown constant -- of each species of interest across the different French agricultural regions, (ii) the sampling effort for living animals for each region, and (iii) the relative detection probability for various species of interest.

No MeSH data available.