Limits...
NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping.

Caubet Y, Richard FJ - Zookeys (2015)

Bottom Line: Animal grouping is a very complex process that occurs in many species, involving many individuals under the influence of different mechanisms.The software also includes statistical analysis and indexes to discriminate aggregates based on spatial localisation of individuals and their neighbours.After the description of the software, the indexes computed by the software are illustrated using both artificial patterns and case studies using the spatial distribution of woodlice.

View Article: PubMed Central - HTML - PubMed

Affiliation: Université de Poitiers - Faculté des Sciences, UMR CNRS 7267 EBI - "Écologie, Évolution, Symbiose", Bat. B8-B35; 6, rue Michel Brunet, TSA 51106, F-86073 POITIERS Cedex 9, France.

ABSTRACT
Animal grouping is a very complex process that occurs in many species, involving many individuals under the influence of different mechanisms. To investigate this process, we have created an image processing software, called NEIGHBOUR-IN, designed to analyse individuals' coordinates belonging to up to three different groups. The software also includes statistical analysis and indexes to discriminate aggregates based on spatial localisation of individuals and their neighbours. After the description of the software, the indexes computed by the software are illustrated using both artificial patterns and case studies using the spatial distribution of woodlice. The added strengths of this software and methods are also discussed.

No MeSH data available.


Aggregation heterogeneity in woodlice. Aggregation patterns of two groups of woodlice illustrating the Aggregation Heterogenity Index (AHI) and the Spatial Mixed Index (SMI). PD: Porcelliodilatatus, PS: Porcellioscaber, CC: Cylisticusconvexus. Values of indexes: PD-PD: AHI=0.93 & SMI=0.80; PD-PS: AHI=0.67 & SMI=0.60; PD-CC: AHI=0.63 & SMI=0.33.
© Copyright Policy - creative-commons-attribution
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4525043&req=5

Figure 3: Aggregation heterogeneity in woodlice. Aggregation patterns of two groups of woodlice illustrating the Aggregation Heterogenity Index (AHI) and the Spatial Mixed Index (SMI). PD: Porcelliodilatatus, PS: Porcellioscaber, CC: Cylisticusconvexus. Values of indexes: PD-PD: AHI=0.93 & SMI=0.80; PD-PS: AHI=0.67 & SMI=0.60; PD-CC: AHI=0.63 & SMI=0.33.

Mentions: First, we compared three gregarious species (groups): Porcelliodilatatus (PD), Porcellioscaber (PS) and Cylisticusconvexus (CC). Three different combinations of two groups of eight individuals (“objects”) are placed in a squared arena (width 12.3 cm) divided into 64 cells. After one hour, a snapshot is taken (Fig. 3) and the distribution of the individuals is analysed with NEIGHBOUR-IN. We used indexes in order to characterise our aggregates according to group characteristics. In a second step, we added the species Armadillidiumvulgare (AV) to PD and PS. This species presents a more scattered aggregation pattern (Hassall et al. 2010). A snapshot is taken after one hour and analysed. The indexes are presented in Table 2 and the outputs concerning spatial distribution in Fig. 4 (“Surfaces” output display).


NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping.

Caubet Y, Richard FJ - Zookeys (2015)

Aggregation heterogeneity in woodlice. Aggregation patterns of two groups of woodlice illustrating the Aggregation Heterogenity Index (AHI) and the Spatial Mixed Index (SMI). PD: Porcelliodilatatus, PS: Porcellioscaber, CC: Cylisticusconvexus. Values of indexes: PD-PD: AHI=0.93 & SMI=0.80; PD-PS: AHI=0.67 & SMI=0.60; PD-CC: AHI=0.63 & SMI=0.33.
© Copyright Policy - creative-commons-attribution
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4525043&req=5

Figure 3: Aggregation heterogeneity in woodlice. Aggregation patterns of two groups of woodlice illustrating the Aggregation Heterogenity Index (AHI) and the Spatial Mixed Index (SMI). PD: Porcelliodilatatus, PS: Porcellioscaber, CC: Cylisticusconvexus. Values of indexes: PD-PD: AHI=0.93 & SMI=0.80; PD-PS: AHI=0.67 & SMI=0.60; PD-CC: AHI=0.63 & SMI=0.33.
Mentions: First, we compared three gregarious species (groups): Porcelliodilatatus (PD), Porcellioscaber (PS) and Cylisticusconvexus (CC). Three different combinations of two groups of eight individuals (“objects”) are placed in a squared arena (width 12.3 cm) divided into 64 cells. After one hour, a snapshot is taken (Fig. 3) and the distribution of the individuals is analysed with NEIGHBOUR-IN. We used indexes in order to characterise our aggregates according to group characteristics. In a second step, we added the species Armadillidiumvulgare (AV) to PD and PS. This species presents a more scattered aggregation pattern (Hassall et al. 2010). A snapshot is taken after one hour and analysed. The indexes are presented in Table 2 and the outputs concerning spatial distribution in Fig. 4 (“Surfaces” output display).

Bottom Line: Animal grouping is a very complex process that occurs in many species, involving many individuals under the influence of different mechanisms.The software also includes statistical analysis and indexes to discriminate aggregates based on spatial localisation of individuals and their neighbours.After the description of the software, the indexes computed by the software are illustrated using both artificial patterns and case studies using the spatial distribution of woodlice.

View Article: PubMed Central - HTML - PubMed

Affiliation: Université de Poitiers - Faculté des Sciences, UMR CNRS 7267 EBI - "Écologie, Évolution, Symbiose", Bat. B8-B35; 6, rue Michel Brunet, TSA 51106, F-86073 POITIERS Cedex 9, France.

ABSTRACT
Animal grouping is a very complex process that occurs in many species, involving many individuals under the influence of different mechanisms. To investigate this process, we have created an image processing software, called NEIGHBOUR-IN, designed to analyse individuals' coordinates belonging to up to three different groups. The software also includes statistical analysis and indexes to discriminate aggregates based on spatial localisation of individuals and their neighbours. After the description of the software, the indexes computed by the software are illustrated using both artificial patterns and case studies using the spatial distribution of woodlice. The added strengths of this software and methods are also discussed.

No MeSH data available.