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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 ├ętudes 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.


Relationship between the relative density as estimated by our model and the raw number of detection in each small agricultural region (SAR).These variables are showed on a log scale (we focused on SAR for which both the estimated density and the raw number of detections was greater than 0).
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pone.0121689.g005: Relationship between the relative density as estimated by our model and the raw number of detection in each small agricultural region (SAR).These variables are showed on a log scale (we focused on SAR for which both the estimated density and the raw number of detections was greater than 0).

Mentions: Until now, the data collected under the SCSL program were only used by the decision makers and private consultancy companies to derive distribution maps of the species from the raw numbers of detected animals. However, as many authors previously have noted, failing to account for unequal sampling effort as well as unequal detection probabilities precludes any density estimation [18]. We therefore developed a model taking into account an unequal observational effort across regions and detection status (dead/living animal), as well as a variable detection probability among species and status, to estimate the relative density of 6 Mustelidae species in each small agricultural region in France. Accounting for this effort in our model led to substantial differences between the estimated densities and the raw numbers of detected animals (see Fig. 5).


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)

Relationship between the relative density as estimated by our model and the raw number of detection in each small agricultural region (SAR).These variables are showed on a log scale (we focused on SAR for which both the estimated density and the raw number of detections was greater than 0).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0121689.g005: Relationship between the relative density as estimated by our model and the raw number of detection in each small agricultural region (SAR).These variables are showed on a log scale (we focused on SAR for which both the estimated density and the raw number of detections was greater than 0).
Mentions: Until now, the data collected under the SCSL program were only used by the decision makers and private consultancy companies to derive distribution maps of the species from the raw numbers of detected animals. However, as many authors previously have noted, failing to account for unequal sampling effort as well as unequal detection probabilities precludes any density estimation [18]. We therefore developed a model taking into account an unequal observational effort across regions and detection status (dead/living animal), as well as a variable detection probability among species and status, to estimate the relative density of 6 Mustelidae species in each small agricultural region in France. Accounting for this effort in our model led to substantial differences between the estimated densities and the raw numbers of detected animals (see Fig. 5).

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 ├ętudes 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.