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Top ‐ down network analysis characterizes hidden termite – termite interactions

View Article: PubMed Central - PubMed

ABSTRACT

The analysis of ecological networks is generally bottom‐up, where networks are established by observing interactions between individuals. Emergent network properties have been indicated to reflect the dominant mode of interactions in communities that might be mutualistic (e.g., pollination) or antagonistic (e.g., host–parasitoid communities). Many ecological communities, however, comprise species interactions that are difficult to observe directly. Here, we propose that a comparison of the emergent properties from detail‐rich reference communities with known modes of interaction can inform our understanding of detail‐sparse focal communities. With this top‐down approach, we consider patterns of coexistence between termite species that live as guests in mounds built by other host termite species as a case in point. Termite societies are extremely sensitive to perturbations, which precludes determining the nature of their interactions through direct observations. We perform a literature review to construct two networks representing termite mound cohabitation in a Brazilian savanna and in the tropical forest of Cameroon. We contrast the properties of these cohabitation networks with a total of 197 geographically diverse mutualistic plant–pollinator and antagonistic host–parasitoid networks. We analyze network properties for the networks, perform a principal components analysis (PCA), and compute the Mahalanobis distance of the termite networks to the cloud of mutualistic and antagonistic networks to assess the extent to which the termite networks overlap with the properties of the reference networks. Both termite networks overlap more closely with the mutualistic plant–pollinator communities than the antagonistic host–parasitoid communities, although the Brazilian community overlap with mutualistic communities is stronger. The analysis raises the hypothesis that termite–termite cohabitation networks may be overall mutualistic. More broadly, this work provides support for the argument that cryptic communities may be analyzed via comparison to well‐characterized communities.

No MeSH data available.


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A principal component projection of community properties shown in Figure 2. Mutualistic plant–pollinator communities are shown with open red circles, and antagonistic host–parasitoid communities are shown with black crosses. The Cameroon community is shown with a downward green triangle, and the Brazilian community is shown with an upward green triangle. The component contributions for axis 1 are as follows: size—28%, clustering—23%, redundancy—18%, connectance—13%, modularity—12%, degree correlation—7%, asymmetry—0%; for axis 2 are as follows: asymmetry—50%, degree correlation—27%, connectance—13%, redundancy—7%, modularity—3%, and <1% for size and clustering.
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ece32313-fig-0003: A principal component projection of community properties shown in Figure 2. Mutualistic plant–pollinator communities are shown with open red circles, and antagonistic host–parasitoid communities are shown with black crosses. The Cameroon community is shown with a downward green triangle, and the Brazilian community is shown with an upward green triangle. The component contributions for axis 1 are as follows: size—28%, clustering—23%, redundancy—18%, connectance—13%, modularity—12%, degree correlation—7%, asymmetry—0%; for axis 2 are as follows: asymmetry—50%, degree correlation—27%, connectance—13%, redundancy—7%, modularity—3%, and <1% for size and clustering.

Mentions: We study these relationships in a more holistic sense by means of a principal components analysis (Fig. 3) coupled with a statistical analysis of the termite networks' property distribution relative to those of the reference communities. Both termite communities align more closely with the mutualistic reference communities than the antagonistic reference communities, though we note that the Cameroon community also overlaps with the host–parasitoid communities. However, the Mahalanobis distances (generalized Z‐score) are generally larger than 2, indicating that the properties of both termite communities diverge from the properties of the reference mutualistic communities. While a measure‐by‐measure comparison of community properties can be insightful, an aggregate approach (such as a principal components analysis coupled with appropriate statistical analyses) provides a more robust view of the manner in which these properties covary, and thereby facilitates greater understanding than univariate analysis.


Top ‐ down network analysis characterizes hidden termite – termite interactions
A principal component projection of community properties shown in Figure 2. Mutualistic plant–pollinator communities are shown with open red circles, and antagonistic host–parasitoid communities are shown with black crosses. The Cameroon community is shown with a downward green triangle, and the Brazilian community is shown with an upward green triangle. The component contributions for axis 1 are as follows: size—28%, clustering—23%, redundancy—18%, connectance—13%, modularity—12%, degree correlation—7%, asymmetry—0%; for axis 2 are as follows: asymmetry—50%, degree correlation—27%, connectance—13%, redundancy—7%, modularity—3%, and <1% for size and clustering.
© Copyright Policy - creativeCommonsBy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC5016641&req=5

ece32313-fig-0003: A principal component projection of community properties shown in Figure 2. Mutualistic plant–pollinator communities are shown with open red circles, and antagonistic host–parasitoid communities are shown with black crosses. The Cameroon community is shown with a downward green triangle, and the Brazilian community is shown with an upward green triangle. The component contributions for axis 1 are as follows: size—28%, clustering—23%, redundancy—18%, connectance—13%, modularity—12%, degree correlation—7%, asymmetry—0%; for axis 2 are as follows: asymmetry—50%, degree correlation—27%, connectance—13%, redundancy—7%, modularity—3%, and <1% for size and clustering.
Mentions: We study these relationships in a more holistic sense by means of a principal components analysis (Fig. 3) coupled with a statistical analysis of the termite networks' property distribution relative to those of the reference communities. Both termite communities align more closely with the mutualistic reference communities than the antagonistic reference communities, though we note that the Cameroon community also overlaps with the host–parasitoid communities. However, the Mahalanobis distances (generalized Z‐score) are generally larger than 2, indicating that the properties of both termite communities diverge from the properties of the reference mutualistic communities. While a measure‐by‐measure comparison of community properties can be insightful, an aggregate approach (such as a principal components analysis coupled with appropriate statistical analyses) provides a more robust view of the manner in which these properties covary, and thereby facilitates greater understanding than univariate analysis.

View Article: PubMed Central - PubMed

ABSTRACT

The analysis of ecological networks is generally bottom&#8208;up, where networks are established by observing interactions between individuals. Emergent network properties have been indicated to reflect the dominant mode of interactions in communities that might be mutualistic (e.g., pollination) or antagonistic (e.g., host&ndash;parasitoid communities). Many ecological communities, however, comprise species interactions that are difficult to observe directly. Here, we propose that a comparison of the emergent properties from detail&#8208;rich reference communities with known modes of interaction can inform our understanding of detail&#8208;sparse focal communities. With this top&#8208;down approach, we consider patterns of coexistence between termite species that live as guests in mounds built by other host termite species as a case in point. Termite societies are extremely sensitive to perturbations, which precludes determining the nature of their interactions through direct observations. We perform a literature review to construct two networks representing termite mound cohabitation in a Brazilian savanna and in the tropical forest of Cameroon. We contrast the properties of these cohabitation networks with a total of 197 geographically diverse mutualistic plant&ndash;pollinator and antagonistic host&ndash;parasitoid networks. We analyze network properties for the networks, perform a principal components analysis (PCA), and compute the Mahalanobis distance of the termite networks to the cloud of mutualistic and antagonistic networks to assess the extent to which the termite networks overlap with the properties of the reference networks. Both termite networks overlap more closely with the mutualistic plant&ndash;pollinator communities than the antagonistic host&ndash;parasitoid communities, although the Brazilian community overlap with mutualistic communities is stronger. The analysis raises the hypothesis that termite&ndash;termite cohabitation networks may be overall mutualistic. More broadly, this work provides support for the argument that cryptic communities may be analyzed via comparison to well&#8208;characterized communities.

No MeSH data available.


Related in: MedlinePlus