Publications by authors named "Buntarou Kusumoto"

5 Publications

  • Page 1 of 1

Multiple drivers of the COVID-19 spread: The roles of climate, international mobility, and region-specific conditions.

PLoS One 2020 23;15(9):e0239385. Epub 2020 Sep 23.

Faculty of Science, University of the Ryukyus, Okinawa, Japan.

Following its initial appearance in December 2019, coronavirus disease 2019 (COVID-19) quickly spread around the globe. Here, we evaluated the role of climate (temperature and precipitation), region-specific COVID-19 susceptibility (BCG vaccination factors, malaria incidence, and percentage of the population aged over 65 years), and human mobility (relative amounts of international visitors) in shaping the geographical patterns of COVID-19 case numbers across 1,020 countries/regions, and examined the sequential shift that occurred from December 2019 to June 30, 2020 in multiple drivers of the cumulative number of COVID-19 cases. Our regression model adequately explains the cumulative COVID-19 case numbers (per 1 million population). As the COVID-19 spread progressed, the explanatory power (R2) of the model increased, reaching > 70% in April 2020. Climate, host mobility, and host susceptibility to COVID-19 largely explained the variance among COVID-19 case numbers across locations; the relative importance of host mobility and that of host susceptibility to COVID-19 were both greater than that of climate. Notably, the relative importance of these factors changed over time; the number of days from outbreak onset drove COVID-19 spread in the early stage, then human mobility accelerated the pandemic, and lastly climate (temperature) propelled the phase following disease expansion. Our findings demonstrate that the COVID-19 pandemic is deterministically driven by climate suitability, cross-border human mobility, and region-specific COVID-19 susceptibility. The identification of these multiple drivers of the COVID-19 outbreak trajectory, based on mapping the spread of COVID-19, will contribute to a better understanding of the COVID-19 disease transmission risk and inform long-term preventative measures against this disease.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0239385PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7510993PMC
October 2020

Past and future decline of tropical pelagic biodiversity.

Proc Natl Acad Sci U S A 2020 06 26;117(23):12891-12896. Epub 2020 May 26.

Faculty of Science, University of the Ryukyus, 903-0213 Okinawa, Japan.

A major research question concerning global pelagic biodiversity remains unanswered: when did the apparent tropical biodiversity depression (i.e., bimodality of latitudinal diversity gradient [LDG]) begin? The bimodal LDG may be a consequence of recent ocean warming or of deep-time evolutionary speciation and extinction processes. Using rich fossil datasets of planktonic foraminifers, we show here that a unimodal (or only weakly bimodal) diversity gradient, with a plateau in the tropics, occurred during the last ice age and has since then developed into a bimodal gradient through species distribution shifts driven by postglacial ocean warming. The bimodal LDG likely emerged before the Anthropocene and industrialization, and perhaps ∼15,000 y ago, indicating a strong environmental control of tropical diversity even before the start of anthropogenic warming. However, our model projections suggest that future anthropogenic warming further diminishes tropical pelagic diversity to a level not seen in millions of years.
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http://dx.doi.org/10.1073/pnas.1916923117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293716PMC
June 2020

Integrating multiple sources of ecological data to unveil macroscale species abundance.

Nat Commun 2020 04 3;11(1):1695. Epub 2020 Apr 3.

Faculty of Science, University of the Ryukyus, 1 Senbaru, Nishihara, Okinawa, 903-0213, Japan.

The pattern of species abundance, represented by the number of individuals per species within an ecological community, is one of the fundamental characteristics of biodiversity. However, despite their obvious significance in ecology and biogeography, there is still no clear understanding of these patterns at large spatial scales. Here, we develop a hierarchical modelling approach to estimate macroscale patterns of species abundance. Using this approach, estimates of absolute abundance of 1248 woody plant species at a 10-km-grid-square resolution over East Asian islands across subtropical to temperate biomes are obtained. We provide two examples of the basic and applied use of the estimated species abundance for (1) inference of macroevolutionary processes underpinning regional biodiversity patterns and (2) quantitative community-wide assessment of a national red list. These results highlight the potential of the elucidation of macroscale species abundance that has thus far been an inaccessible but critical property of biodiversity.
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http://dx.doi.org/10.1038/s41467-020-15407-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7125090PMC
April 2020

A geometric approach to scaling individual distributions to macroecological patterns.

J Theor Biol 2019 01 16;461:170-188. Epub 2018 Oct 16.

Biodiversity and Biocomplexity Unit, Okinawa Institute of Science and Technology Graduate University, Onna-son, Okinawa 904-0495, Japan.

Understanding macroecological patterns across scales is a central goal of ecology and a key need for conservation biology. Much research has focused on quantifying and understanding macroecological patterns such as the species-area relationship (SAR), the endemic-area relationship (EAR) and relative species abundance curve (RSA). Understanding how these aggregate patterns emerge from underlying spatial pattern at individual level, and how they relate to each other, has both basic and applied relevance. To address this challenge, we develop a novel spatially explicit geometric framework to understand multiple macroecological patterns, including the SAR, EAR, RSA, and their relationships. First, we provide a general theory that can be used to derive the asymptotic slopes of the SAR and EAR, and demonstrates the dependency of RSAs on the shape of the sampling region. Second, assuming specific shapes of the sampling region, species geographic ranges, and individual distribution patterns therein based on theory of stochastic point processes, we demonstrate various well-documented macroecological patterns can be recovered, including the tri-phasic SAR and various RSAs (e.g., Fisher's logseries and the Poisson lognormal distribution). We also demonstrate that a single equation unifies RSAs across scales, and provide a new prediction of the EAR. Finally, to demonstrate the applicability of the proposed model to ecological questions, we provide how beta diversity changes with spatial extent and its grain over multiple scales. Emergent macroecological patterns are often attributed to ecological and evolutionary mechanisms, but our geometric approach still can recover many previously observed patterns based on simple assumptions about species geographic ranges and the spatial distribution of individuals, emphasizing the importance of geometric considerations in macroecological studies.
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http://dx.doi.org/10.1016/j.jtbi.2018.10.030DOI Listing
January 2019

Spatially explicit approach to estimation of total population abundance in field surveys.

J Theor Biol 2018 09 19;453:88-95. Epub 2018 May 19.

ARC Centre of Excellence for Environmental Decisions, School of Biological Sciences, The University of Queensland, St Lucia, QLD 4072, Australia; The Nature Conservancy, 4245 North Fairfax Drive Suite 100, Arlington, VA 22203-1606, USA.

Population abundance is fundamental in ecology and conservation biology, and provides essential information for predicting population dynamics and implementing conservation actions. While a range of approaches have been proposed to estimate population abundance based on existing data, data deficiency is ubiquitous. When information is deficient, a population estimation will rely on labor intensive field surveys. Typically, time is one of the critical constraints in conservation, and management decisions must often be made quickly under a data deficient situation. Hence, it is important to acquire a theoretical justification for survey methods to meet a required estimation precision. There is no such theory available in a spatially explicit context, while spatial considerations are critical to any field survey. Here, we develop a spatially explicit theory for population estimation that allows us to examine the estimation precision under different survey designs and individual distribution patterns (e.g. random/clustered sampling and individual distribution). We demonstrate that clustered sampling decreases the estimation precision when individuals form clusters, while sampling designs do not affect the estimation accuracy when individuals are distributed randomly. Regardless of individual distribution, the estimation precision becomes higher with increasing total population abundance and the sampled fraction. These insights provide theoretical bases for efficient field survey designs in information deficiency situations.
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http://dx.doi.org/10.1016/j.jtbi.2018.05.013DOI Listing
September 2018