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Temporal coexistence mechanisms contribute to the latitudinal gradient in forest diversity

Abstract

The tropical forests of Borneo and Amazonia may each contain more tree species diversity in half a square kilometre than do all the temperate forests of Europe, North America, and Asia combined1. Biologists have long been fascinated by this disparity, using it to investigate potential drivers of biodiversity2. Latitudinal variation in many of these drivers is expected to create geographic differences in ecological2,3,4 and evolutionary processes4,5, and evidence increasingly shows that tropical ecosystems have higher rates of diversification, clade origination, and clade dispersal5,6. However, there is currently no evidence to link gradients in ecological processes within communities at a local scale directly to the geographic gradient in biodiversity. Here, we show geographic variation in the storage effect, an ecological mechanism that reduces the potential for competitive exclusion more strongly in the tropics than it does in temperate and boreal zones, decreasing the ratio of interspecific-to-intraspecific competition by 0.25% for each degree of latitude that an ecosystem is located closer to the Equator. Additionally, we find evidence that latitudinal variation in climate underpins these differences; longer growing seasons in the tropics reduce constraints on the seasonal timing of reproduction, permitting lower recruitment synchrony between species and thereby enhancing niche partitioning through the storage effect. Our results demonstrate that the strength of the storage effect, and therefore its impact on diversity within communities, varies latitudinally in association with climate. This finding highlights the importance of biotic interactions in shaping geographic diversity patterns, and emphasizes the need to understand the mechanisms underpinning ecological processes in greater detail than has previously been appreciated.

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Figure 1: The median values of pairwise competition coefficients AijAji are correlated with latitude.
Figure 2: Relationship between within-year and among-year synchrony and latitude.

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Acknowledgements

We thank J. M. Levine, M. G. Turner, D. M. Waller, D. J. Mladenoff and J. Zhu for comments on the manuscript, and S.-H. Wu of the Taiwan Forestry Research Institute for plant identification. We acknowledge the following funding sources, which have been essential in the ongoing collection of long-term forest data (in alphabetical order): Andrew M. Mellon Foundation, Center for Tropical Forest Science, Environment Research and Technology Development Fund of the Japan Ministry of the Environment, JSPS KAKENHI, National Key Research and Development Program of China, National Natural Science Foundation of China, National Science Foundation of the United States (DDIG, IGERT, LTER, LTREB), Natural Environment Research Council of the UK, Natural History Museum of London, Smithsonian Tropical Research Institute, Taiwan Forestry Bureau, Taiwan Forestry Research Institute, Taiwan Ministry of Science and Technology, USDA Forest Service.

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Authors

Contributions

J.U. analysed data and wrote the paper. C.-H.C.–Y., Y.-Y.C., J.S.C., C.F., N.C.G., Z.H., J.J., Y.L., M.R.M., T.M., T.N., I.S., R.V., Y.W., J.K.Z., and S.J.W. have established, maintained, and collected data from long-term demography plots. S.J.W. and A.R.I. contributed equally to paper conception and development. All authors discussed results and commented on the manuscript.

Corresponding author

Correspondence to Jacob Usinowicz.

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Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks R. Bagchi, S. McMahon, G. Mittelbach and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Figure 1 Frequency distributions of AijAji for each forest community, calculated with three different approaches (columns 1–3).

The first column is calculated for the full-community datasets (variable number of years and species between sets), the second column for the six-year jack-knifed, full-community datasets (variable number of species), and the third column is for six-year jack-knifed, six most-sampled species datasets. See Extended Data Table 1 for sample sizes (number of species per forest).

Extended Data Figure 2 The median values of pairwise competition coefficients AijAji are correlated with latitude for three different methods of calculation.

Each combination of symbols and fitted lines corresponds to an approach for calculating AijAji, including standard error bars. Solid line and solid circles: all years in each dataset (slope = 0.0018, R2 = 0.63, P = 0.004); dashed line and ‘X’s: repeated jack-knifing of six-year subsets of the full dataset (slope = 0.0025, R2 = 0.83, P < 0.001); dotted line and open squares: jack-knifing six-year subsets containing only the top six seedling-producing species in each forest (slope = 0.0031, R2 = 0.42, P = 0.03). See Extended Data Table 1 for sample sizes (number of species per forest).

Extended Data Figure 3 The median values of pairwise competition coefficients AijAji are correlated with growing season for three different methods of calculation.

The metric of growing season is the log of the sum of coefficients of variation (CV) for monthly solar insolation, minimum temperatures, and maximum temperatures, log(CVI + CVX + CVN) (Methods, Extended Data Table 2). Lower values correspond to a longer growing season. Symbols and fitted lines represent different methods of calculating AijAji, including standard error bars. Solid line and solid circles: all years in each dataset (slope = 0.027, R2 = 0.57, P = 0.007); dashed line and ‘X’s: repeated jack-knifing of six-year subsets of the full set (slope = 0.041, R2 = 0.87, P < 0.001); dotted line and open squares: jack-knifing six-year subsets containing only the top six seedling producing species in each forest (slope = 0.055, R2 = 0.55, P = 0.009). See Extended Data Table 1 for sample sizes (number of species per forest).

Extended Data Figure 4 The correlation between within-year pairwise synchrony and among-year synchrony was positive for all ten forests.

Within-year and among-year synchronies are calculated as the within-year and among-year portions of the correlation (see Fig. 2, Methods). The correlation between these two scales of synchrony was also calculated using the Pearson’s correlation, given as r in the lower right corner of each panel. The median correlation across all forests was 0.28, and it ranged from 0.05 (BCI) to 0.80 (Bonanza Creek). See Extended Data Table 1 for sample sizes (number of species per forest).

Extended Data Table 1 A summary of the selected forests and available data
Extended Data Table 2 Statistical models using CVs of four different climate variables to predict community-wide averages of within-year interspecific synchrony, among-year interspecific synchrony, and the metric AijAji

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Usinowicz, J., Chang-Yang, CH., Chen, YY. et al. Temporal coexistence mechanisms contribute to the latitudinal gradient in forest diversity. Nature 550, 105–108 (2017). https://doi.org/10.1038/nature24038

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