Inferring Species Interactions in Tropical Forests

Inferring species interactions in tropical forests Igor Volkova,b, Jayanth R. Banavara,1, Stephen P. Hubbellc,d, and Amos Maritane,1 aDepartment of Physics, 104 Davey Laboratory, Pennsylvania State University, University Park, PA 16802; bDepartment of Biology, Muller Laboratory, c Pennsylvania State University, University Park, PA 16802; Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095; dThe Smithsonian Tropical Research Institute, P.O. Box 0843–03092, Balboa, Ancón Panama, Republic of Panama; eDipartimento di Fisica “G. Galilei,” Università di Padova, Consorzio Nazionale Interuniversitario per le Scienze Fisiche della Materia, Istituto Nazionale di Fisica Nucleare, Via Marzolo 8, 35131 Padua, Italy Edited by James H. Brown, University of New Mexico, Albuquerque, NM, and approved May 29, 2009 (received for review March 25, 2009) We present 2 distinct and independent approaches to deduce the compared with the intraspecific ones. It is noteworthy that the effective interaction strengths between species and apply it to the results of both approaches are nearly identical. 20 most abundant species in the long-term 50-ha plot on Barro Consider a steady-state community (8) composed of S coexist- Colorado Island, Panama. The first approach employs the princi- ing species for each of which we have precise spatial information ple of maximum entropy, and the second uses a stochastic birth– on the location of every individual. Our goal is to use these data death model. Both approaches yield very similar answers and show to infer the effective interaction strengths between the species. that the collective effects of the pairwise interspecific interaction In order to deduce various correlations from such data, we divide strengths are weak compared with the intraspecific interactions. the community into equally sized quadrats large enough to contain Our approaches can be applied to other ecological communities many individuals yet small enough to have a sufficient number of in steady state to evaluate the extent to which interactions need quadrats to facilitate statistical averaging. to be incorporated into theoretical explanations for their structure The analysis focuses on the probability distribution P(n), where and dynamics. n = (n1, n2, ..., nS), and ni is the abundance of the ith species within a quadrat. Averaging over these quadrats, one can deter- ecology | maximum entropy principle | stochastic mine mean species abundances and higher-order correlations such as pair and triplet correlations of species abundances. The principle of maximum entropy provides a simple method to infer ECOLOGY ow strong are the interactions among species in ecologi- the probability distribution in an unbiased manner using these H cal communities, and what impact do they have on com- measured averages and correlation functions as an input. For munity structure and composition? This is perhaps the most a noninteracting system, this probability distribution would fac- fundamental problem in community ecology for understanding torize into independent single-species probability distributions. patterns of coexistence and the distribution and abundance of Any deviation from a simple product of independent probability species, with important implications for conserving biodiversity. distributions is a measure of the interactions among species. A long-standing debate persists in the literature over the extent Operationally, the procedure is simple and straightforward (see to which communities are niche-assembled (1–4) versus dispersal- Materials and Methods): One obtains the covariance matrix Σ assembled (5, 6). Niche theory starts from the premise that niche from the data and inverts it to obtain the effective interaction differences among species are essential to species coexistence (7). −1 matrix, M ≡−Σ , i.e., the component of this matrix Mij may be In contrast, neutral theory (8), based on the simplifying assump- associated with the interaction strength between species i and j. tion that all species within a trophic level are demographically ∝ [ − Indeed, the multivariate probability P(n) exp 1/2 i,j Mij(ni equivalent, along with the hypothesis of noninteracting species, n )(n −n )], where the averages n are obtained by averag- yields analytic predictions (9–11) that fit field data on static and i j j i ing the abundance data in the individual quadrats. M serves as a dynamic patterns of species abundance remarkably well. How- ij coupling constant between species. Note that there is no simple ever, patterns consistent with neutrality do not necessarily imply relationship in this many-body system between the covariance of underlying neutral processes (10, 12, 13). abundances for a pair of species and their interaction. For exam- Wepresent 2 independent and complementary methods for esti- ple, 2 species could have a weak interaction and yet a very strong mating interaction strengths and apply it to the tropical tree com- correlation because they are both strongly interacting with a third munity on Barro Colorado Island, Panama (BCI). The effective species. Similar emergent behavior occurs on a much more com- interactions between species are emergent quantities that arise plex scale when one considers multiple species and the influence from the multiple interactions among the species and between of the environment. the species and the environment. A negative interaction (M < 0) is akin to “competition” The principle of maximum entropy, which can be used to derive ij between species, namely, the probability of the ith species hav- equilibrium statistical mechanics (14–19) and has proved pow- ing a given abundance decreases with the increase of abundance erful in a variety of disciplines (20–26), is a powerful inference of the jth species. Likewise, positive interaction coefficients pro- technique that can provide a measure of the effective inter- mote “mutualism” or similar patterns of environmental covari- actions while faithfully encoding all available information and ation between the species. The self-interaction components M being unbiased with respect to missing information. Note that this ii are necessarily negative—otherwise the normalization constraint, approach follows the classic ecological prescription of analyzing P(n) = 1, cannot be satisfied. Note that the interactions the statics of the forest composition (27) reflected in the covari- n deduced by using the maximum-entropy method provide a mea- ances of species abundances [the maximum-entropy approach sure of the probability of occurrence of a given snapshot of species that we present here goes beyond using only the covariances of abundances. The maximum-entropy method, however, does not species abundances and allows one to incorporate many-body interactions—see supporting information (SI) Text]. In contrast, the stochastic birth–death model provides a microscopic descrip- tion of the dynamical evolution of the community in the vicinity of Author contributions: I.V., J.R.B., S.P.H., and A.M. designed research, performed research, its present state. We study the interaction strengths among the top contributed new reagents/analytic tools, analyzed data, and wrote the paper. 20 species that make up >60% of all stems in the 50-ha plot. This The authors declare no conflict of interest. dual analysis demonstrates that the collective effects of interspe- 1To whom correspondence may be addressed. E-mail: [email protected] or cific interactions among the common BCI tree species are weak [email protected]. www.pnas.org / cgi / doi / 10.1073 / pnas.0903244106 Early Edition 1of6 Downloaded by guest on September 24, 2021 Table 1. Intraspecific and interspecific covariances in abundance and interaction strengths Comparison Mean covariance SE of mean covariance Mean interaction SE of mean interaction No. of comparisons Intraspecific 42.94 17.54 −0.062 0.014 20 Interspecific 0.63 0.32 0.0010 0.00051 190 Understory–Understory 1.97 0.93 0.00022 0.00065 45 Understory–Tree 0.11 0.41 0.0011 0.00079 100 Tree–Tree 0.42 0.29 0.0016 0.0011 45 Data were derived by using the method of stochastic dynamics for the 20 most abundant BCI species and for understory–understory, understory–tree, and tree–tree species pairs, not including intraspecific covariances and interactions. The distinction between tree and understory designations comes from the height of the adult plant being greater than or less than 10 m, respectively. The last column denotes the number of total number of species pairs in each category. SE, standard error. provide information pertaining to the mechanism or strength of effects of all variables (environmental as well as other species) not the direct interaction between individuals of a pair of species. considered explicitly. Many BCI tree species are rare to very rare, so we restrict The 20 most abundant BCI species include 10 main canopy and the analysis to interactions among a subset of the most abundant subcanopy species and 10 understory species, reflecting the mix of species. The new effective interactions among the subset of species species growth forms in the BCI forest. The analysis incorporates not only represent their direct interactions but also those mediated interactions among these species at all stages of ontogeny from by the species not being considered. One can show (see SI Text) small saplings of 1-cm dbh (diameter at the breast height) to larger that the interaction matrix in this case is given by inverting a trun- individuals. Note that canopy

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