Climate reconstruction analysis using coexistence likelihood estimation (CRACLE): a method for the estimation of climate using vegetation.

UNLABELLED: • PREMISE OF THE STUDY: Plant distributions have long been understood to be correlated with the environmental conditions to which species are adapted. Climate is one of the major components driving species distributions. Therefore, it is expected that the plants coexisting in a community are reflective of the local environment, particularly climate.• METHODS: Presented here is a method for the estimation of climate from local plant species coexistence data. The method, Climate Reconstruction Analysis using Coexistence Likelihood Estimation (CRACLE), is a likelihood-based method that employs specimen collection data at a global scale for the inference of species climate tolerance. CRACLE calculates the maximum joint likelihood of coexistence given individual species climate tolerance characterization to estimate the expected climate.• KEY RESULTS: Plant distribution data for more than 4000 species were used to show that this method accurately infers expected climate profiles for 165 sites with diverse climatic conditions. Estimates differ from the WorldClim global climate model by less than 1.5°C on average for mean annual temperature and less than ∼250 mm for mean annual precipitation. This is a significant improvement upon other plant-based climate-proxy methods.• CONCLUSIONS: CRACLE validates long hypothesized interactions between climate and local associations of plant species. Furthermore, CRACLE successfully estimates climate that is consistent with the widely used WorldClim model and therefore may be applied to the quantitative estimation of paleoclimate in future studies.

Climate niche modeling in the perennial Glycine (Leguminosae) allopolyploid complex.

PREMISE OF STUDY: Polyploid plants, when compared with diploids, show similar molecular, morphological, physiological, and ecological tendencies across unrelated groups, but the degree to which these form "rules" of polyploid evolution are unclear. The Glycine (Leguminosae) allopolyploid complex affords the opportunity to test whether polyploidy in similar genetic backgrounds produces similar effects on geographical range or climatic space. METHODS: We used information on locality presence of four closely related Glycine allopolyploid species and their diploid progenitors to build models of the potentially available Australian ranges based on climate using Maxent3.3.3k. Principal coordinate analysis was used to characterize the multidimensional climate space occupied by each species. KEY RESULTS: Each of the four Glycine allopolyploids showed intermediacy in potential geographical space and in ecological space, relative to its diploid progenitors. The four allopolyploids did not have consistently larger ranges than their progenitors, though all four occupied a portion of climate niche space not available to its progenitors. The polyploids also differed in their exploitation of potentially available geographical range. Australian ranges and environmental space did not correlate with greater colonizing ability in these polyploids. CONCLUSIONS: The four Glycine allopolyploids do not show many common range- or climate-related features, other than intermediacy. Thus, despite their similar genetic and evolutionary backgrounds, polyploidy has not produced convergent ecological effects.

cRacle: R tools for estimating climate from vegetation.

PREMISE: The Climate Reconstruction Analysis using Coexistence Likelihood Estimation (CRACLE) method utilizes a robust set of modeling tools for estimating climate and paleoclimate from vegetation using large repositories of biodiversity data and open access R software. METHODS: Here, we implement a new R package for the estimation of climate from extant and fossil vegetation. The 'cRacle' package implements functions for data access, aggregation, and modeling to estimate climate from plant community compositions. 'cRacle' is modular and includes many best-practice features. RESULTS: Performance tests using modern vegetation survey data from North and South America shows that CRACLE outperforms alternative methods. CRACLE estimates of mean annual temperature are usually within 1°C of the actual values when optimal model parameters are used. Generalized boosted regression (GBR) model correction improves CRACLE estimates by reducing bias. DISCUSSION: CRACLE provides accurate estimates of climate based on the composition of modern plant communities. Non-parametric CRACLE modeling coupled with GBR model correction produces the most accurate results to date. The 'cRacle' R package streamlines the estimation of climate from plant community data, which will make this modeling more accessible to a wider range of users.

Algorithms and strategies in short-read shotgun metagenomic reconstruction of plant communities.

PREMISE OF THE STUDY: DNA may be preserved for thousands of years in very cold or dry environments, and plant tissue fragments and pollen trapped in soils and shallow aquatic sediments are well suited for the molecular characterization of past floras. However, one obstacle in this area of study is the limiting bias in the bioinformatic classification of short fragments of degraded DNA from the large, complex genomes of plants. METHODS: To establish one possible baseline protocol for the rapid classification of short-read shotgun metagenomic data for reconstructing plant communities, the read classification programs Kraken, Centrifuge, and MegaBLAST were tested on simulated and ancient data with classification against a reference database targeting plants. RESULTS: Performance tests on simulated data suggest that Kraken and Centrifuge outperform MegaBLAST. Kraken tends to be the most conservative approach with high precision, whereas Centrifuge has higher sensitivity. Reanalysis of 13,000 years of ancient sedimentary DNA from North America characterizes potential post-glacial vegetation succession. DISCUSSION: Classification method choice has an impact on performance and any downstream interpretation of results. The reanalysis of ancient DNA from glacial lake sediments yielded vegetation histories that varied depending on method, potentially changing paleoecological conclusions drawn from molecular evidence.