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Predicting species establishment using absent species and functional neighborhoods

View Article: PubMed Central - PubMed

ABSTRACT

Species establishment within a community depends on their interactions with the local environment and resident community. Such environmental and biotic filtering is frequently inferred from functional trait and phylogenetic patterns within communities; these patterns may also predict which additional species can establish. However, differentiating between environmental and biotic filtering can be challenging, which may complicate establishment predictions. Creating a habitat‐specific species pool by identifying which absent species within the region can establish in the focal habitat allows us to isolate biotic filtering by modeling dissimilarity between the observed and biotically excluded species able to pass environmental filters. Similarly, modeling the dissimilarity between the habitat‐specific species pool and the environmentally excluded species within the region can isolate local environmental filters. Combined, these models identify potentially successful phenotypes and why certain phenotypes were unsuccessful. Here, we present a framework that uses the functional dissimilarity among these groups in logistic models to predict establishment of additional species. This approach can use multivariate trait distances and phylogenetic information, but is most powerful when using individual traits and their interactions. It also requires an appropriate distance‐based dissimilarity measure, yet the two most commonly used indices, nearest neighbor (one species) and mean pairwise (all species) distances, may inaccurately predict establishment. By iteratively increasing the number of species used to measure dissimilarity, a functional neighborhood can be chosen that maximizes the detection of underlying trait patterns. We tested this framework using two seed addition experiments in calcareous grasslands. Although the functional neighborhood size that best fits the community's trait structure depended on the type of filtering considered, selecting these functional neighborhood sizes allowed our framework to predict up to 50% of the variation in actual establishment from seed. These results indicate that the proposed framework may be a powerful tool for studying and predicting species establishment.

No MeSH data available.


An example of the effect of neighborhood size on establishment predictions when multiple mechanisms affect community assembly. Neighborhood sizes are shown as a proportion of the total community. Figures show the effect of different functional neighborhood sizes on establishment predictions for a single community, both without (left column; a,c,e,g,i) and with (right column; b,d,f,h,j) trait interactions included in the model. The neighborhood sizes shown range from nearest neighbor distances (one species; a, b) to mean pairwise distances (all species; i, j). Red areas denote areas with high predicted establishment and purple areas low establishment (see legend between panels c‐f). In all panels, black circles denote environmentally excluded species, gray circles biotically excluded species, and white circles species observed within the community
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ece32804-fig-0005: An example of the effect of neighborhood size on establishment predictions when multiple mechanisms affect community assembly. Neighborhood sizes are shown as a proportion of the total community. Figures show the effect of different functional neighborhood sizes on establishment predictions for a single community, both without (left column; a,c,e,g,i) and with (right column; b,d,f,h,j) trait interactions included in the model. The neighborhood sizes shown range from nearest neighbor distances (one species; a, b) to mean pairwise distances (all species; i, j). Red areas denote areas with high predicted establishment and purple areas low establishment (see legend between panels c‐f). In all panels, black circles denote environmentally excluded species, gray circles biotically excluded species, and white circles species observed within the community

Mentions: To illustrate how interactions among traits and different neighborhood sizes affect establishment predictions using an individual trait approach, we construct another hypothetical community. For this community, species possess two randomly generated traits. Both traits are affected by environmental filtering, but for biotic filtering one trait is affected by limiting similarity and the other by weak phenotype exclusion (see Appendix S1 for details). Consequently, species from the habitat‐specific species pool are clustered in both trait dimensions, and successful establishment should occur in zones within the functional space occupied by the habitat‐specific species pool. Trait interactions appear to have little effect on environmental filtering predictions. However, the use of nearest neighbor distances mistakenly predicted success for species without the traits required to pass the environmental filters (warmer colors in areas with no species from the habitat‐specific species pool in Figure 5a,b). As we increased neighborhood sizes, this became less of an issue as successful phenotypes became restricted to the functional space occupied by the habitat‐specific species pool (Figure 5).


Predicting species establishment using absent species and functional neighborhoods
An example of the effect of neighborhood size on establishment predictions when multiple mechanisms affect community assembly. Neighborhood sizes are shown as a proportion of the total community. Figures show the effect of different functional neighborhood sizes on establishment predictions for a single community, both without (left column; a,c,e,g,i) and with (right column; b,d,f,h,j) trait interactions included in the model. The neighborhood sizes shown range from nearest neighbor distances (one species; a, b) to mean pairwise distances (all species; i, j). Red areas denote areas with high predicted establishment and purple areas low establishment (see legend between panels c‐f). In all panels, black circles denote environmentally excluded species, gray circles biotically excluded species, and white circles species observed within the community
© Copyright Policy - creativeCommonsBy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC5383500&req=5

ece32804-fig-0005: An example of the effect of neighborhood size on establishment predictions when multiple mechanisms affect community assembly. Neighborhood sizes are shown as a proportion of the total community. Figures show the effect of different functional neighborhood sizes on establishment predictions for a single community, both without (left column; a,c,e,g,i) and with (right column; b,d,f,h,j) trait interactions included in the model. The neighborhood sizes shown range from nearest neighbor distances (one species; a, b) to mean pairwise distances (all species; i, j). Red areas denote areas with high predicted establishment and purple areas low establishment (see legend between panels c‐f). In all panels, black circles denote environmentally excluded species, gray circles biotically excluded species, and white circles species observed within the community
Mentions: To illustrate how interactions among traits and different neighborhood sizes affect establishment predictions using an individual trait approach, we construct another hypothetical community. For this community, species possess two randomly generated traits. Both traits are affected by environmental filtering, but for biotic filtering one trait is affected by limiting similarity and the other by weak phenotype exclusion (see Appendix S1 for details). Consequently, species from the habitat‐specific species pool are clustered in both trait dimensions, and successful establishment should occur in zones within the functional space occupied by the habitat‐specific species pool. Trait interactions appear to have little effect on environmental filtering predictions. However, the use of nearest neighbor distances mistakenly predicted success for species without the traits required to pass the environmental filters (warmer colors in areas with no species from the habitat‐specific species pool in Figure 5a,b). As we increased neighborhood sizes, this became less of an issue as successful phenotypes became restricted to the functional space occupied by the habitat‐specific species pool (Figure 5).

View Article: PubMed Central - PubMed

ABSTRACT

Species establishment within a community depends on their interactions with the local environment and resident community. Such environmental and biotic filtering is frequently inferred from functional trait and phylogenetic patterns within communities; these patterns may also predict which additional species can establish. However, differentiating between environmental and biotic filtering can be challenging, which may complicate establishment predictions. Creating a habitat‐specific species pool by identifying which absent species within the region can establish in the focal habitat allows us to isolate biotic filtering by modeling dissimilarity between the observed and biotically excluded species able to pass environmental filters. Similarly, modeling the dissimilarity between the habitat‐specific species pool and the environmentally excluded species within the region can isolate local environmental filters. Combined, these models identify potentially successful phenotypes and why certain phenotypes were unsuccessful. Here, we present a framework that uses the functional dissimilarity among these groups in logistic models to predict establishment of additional species. This approach can use multivariate trait distances and phylogenetic information, but is most powerful when using individual traits and their interactions. It also requires an appropriate distance‐based dissimilarity measure, yet the two most commonly used indices, nearest neighbor (one species) and mean pairwise (all species) distances, may inaccurately predict establishment. By iteratively increasing the number of species used to measure dissimilarity, a functional neighborhood can be chosen that maximizes the detection of underlying trait patterns. We tested this framework using two seed addition experiments in calcareous grasslands. Although the functional neighborhood size that best fits the community's trait structure depended on the type of filtering considered, selecting these functional neighborhood sizes allowed our framework to predict up to 50% of the variation in actual establishment from seed. These results indicate that the proposed framework may be a powerful tool for studying and predicting species establishment.

No MeSH data available.