Biology subjects

Kusumoto, B.

Publications and source records attributed to Kusumoto, B..

6 recordsLinked to original sources

bioRxiv2023

Occurrence-based diversity estimation reveals macroecological and conservation knowledge gaps for global woody plants

Incomplete sampling of species geographic distributions has challenged biogeographers for many years to precisely quantify global-scale biodiversity patterns. After correcting for the spatial inequality of sample completeness, we generated a global species diversity map for woody angiosperms (82,974 species, 13,959,780 occurrence records). The estimated diversity demonstrated non-linear latitudinal and longitudinal patterns that were potentially related to region-specific biogeographic factors including current climate, paleoclimate, and topographical factors, while energy availability was the most important predictor at a global level. We identified the areas with potentially high species richness and rarity, but poorly explored, unprotected, and threatened by deforestation: they are distributed mostly at low latitudes across central South America, central Africa, subtropical China, and Indomalayan islands. These priority areas for botanical exploration would help to efficiently fill spatial knowledge gaps for better describing the status of biodiversity and improve the effectiveness of the protected area network for global woody plant conservation. TeaserBias-corrected diversity map based on occurrence records sheds new light on global macroecology and conservation of woody angiosperms.

ecology↗

bioRxiv2021

Area-based conservation planning in Japan: protected area network effectiveness to the post-2020 Global Biodiversity Framework

To reframe the imperfect review processes of nation-scale actions on area-based conservation through protected area (PA) networks, we first created novel infrastructure to visualize nation-level biodiversity information in Japan. We then assessed the performance of the existing PA network relative to land exploitation pressure and evaluated conservation effectiveness of PA expansion for the post-2020 Global Biodiversity Framework. The Zonation algorithm was used to spatially prioritize conservation areas to minimize biodiversity loss and the extinction risk for 8,500 Japanese vascular plant and vertebrate species under constraints of the existing PA network and land use. The spatial pattern of the identified priority areas, which were considered candidate areas for expansion of the current PA network, was influenced by land-use types according to the mask layers of non-PAs, and low-, middle-, and high-ranked PAs. The current PA network reduced the aggregate extinction risk of multiple species by 36.6%. Indeed, the percentage of built-up areas in the existing PAs was in general smaller than that in the areas surrounding PAs. Notably, high-ranked PAs fully restrained built-up pressure (0.037% per 10 years), whereas low-ranked PAs in the national park and wild-life protection areas did not (1.845% per 10 years). Conservation effects were predicted to substantially improve by expansion of high-ranked (legally strict) PAs into remote non-PAs without population/socio-economic activities, or expansion of medium-ranked PAs into agriculture forestry satoyama and urban areas. A 30% land conservation target was predicted to decrease extinction risk by 74.1% when PA expansion was implemented across remote areas, satoyama, and urban areas; moreover, PA connectivity almost doubled compared with the existing PA network. In contrast, a conventional scenario showed that placing national parks in state-owned and non-populated areas would reduce extinction risk by only 4.0%. The conservation prioritization analyses demonstrated an effectiveness of using a comprehensive conservation approach that reconciles land-sparing protection and land-sharing conservation in other effective area-based conservation measures (OECM) in satoyama and urban green spaces. Our results revealed that complementary inclusion of various PAs interventions related to their governance and land-use planning plays a critical role in effectively preventing biodiversity loss and makes it more feasible to achieve ambitious conservation targets.

ecology↗

bioRxiv2018

Macroscale estimates of species abundance reveal evolutionary drivers of biodiversity

Evolutionary processes underpin the biodiversity on the planet. Theories advocate that the form of the species abundance distribution (SAD), presented by the number of individuals for each species within an ecological community, is intimately linked to speciation modes such as point mutation and random fission. This prediction has rarely been, however, verified empirically; the fact that species abundance data can be obtained only from local communities critically limits our ability to infer the role of macroevolution in shaping ecological patterns. Here, we developed a novel statistical model to estimate macroscale SADs, the hidden macroecological property, by integrating spatially replicated multispecies detection-nondetection observations and the data on species geographic distributions. We determined abundance of 1,248 woody plant species at a 10 km grid square resolution over East Asian islands across subtropical to temperate biomes, which produced a metacommunity (i.e. species pool) SAD in four insular ecoregions along with its absolute size. The metacommunity SADs indicated lognormal-like distributions, which were well explained by the unified neutral theory of biodiversity and biogeography (UNTB) with protracted speciation, a mode of speciation intermediate between point mutation and random fission. Furthermore, the analyses yielded an estimate of speciation rate in each region that highlighted the importance of geographic characteristics in macroevolutionary processes and predicted the average species lifetime that was congruent with previous estimates. The estimation of macroscale SADs plays a remarkable role in revealing evolutionary diversification of regional species pools.

ecology↗

bioRxiv2018

A geometric approach to scaling individual distributions to macroecological patterns

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, using theory of point processes. The geometric approach provides a theoretical framework to derive SAR, EAR, and RSA from species range distributions and the pattern of individual distribution patterns therein. From this model, various well-documented macroecological patterns are recovered, including the tri-phasic SAR on a log-log plot with its asymptotic slope, and various RSAs (e.g., Fisher {boxtimes} s logseries and the Poisson lognormal distribution). Moreover, this approach can provide new insights such as a single equation describing the RSA at an arbitrary spatial scale, and explicit forms of the EAR with its asymptotic slope. The theory, which links spatial distributions of individuals and species with macroecological patterns, is ambiguous with regards to the mechanism(s) responsible for the statistical properties of individual distributions and species range sizes. However, our approach can be connected to mechanistic models that make such predictions about lower-level patterns and be used to scale them up to aggregate patterns, and therefore is applicable to many ecological questions. We demonstrate an application of the geometric model to scaling issue of beta diversity.

ecology↗

bioRxiv2017

A Theory For Ecological Survey Methods To Map Individual Distributions

Spatially-explicit approaches are widely recommended for ecosystem management. The quality of the data, such as presence/absence or habitat maps, affects the management actions recommended, and is, therefore, key to management success. However, available data are often biased and incomplete. Previous studies have advanced ways to resolve data bias and missing data, but questions remain about how we design ecological surveys to develop a dataset through field surveys. Ecological surveys may have multiple spatial scales, including the spatial extent of the target ecosystem (observation window), the resolution for mapping individual distributions (mapping unit), and the survey area within each mapping unit (sampling unit). We developed an ecological survey method for mapping individual distributions by applying spatially-explicit stochastic models. We used spatial point processes to describe individual spatial placements using either random or clustering processes. We then designed ecological surveys with different spatial scales and individual detectability. We found that the choice of mapping unit affected the presence mapped fraction, and the fraction of the total individuals covered by the presence mapped patches. Tradeoffs were found between these quantities and the map resolution, associated with equivalent asymptotic behaviors for both metrics at sufficiently small and large mapping unit scales. Our approach enabled consideration of the effect of multiple spatial scales in surveys, and estimation of the survey outcomes such as the presence mapped fraction and the number of individuals situated in the presence detected units. The developed theory may facilitate management decision-making and inform the design of monitoring and data gathering.

ecology↗

bioRxiv2017

Spatially Explicit Approach To Population Abundance Estimation In Field Surveys

The abundance of species is a fundamental consideration in ecology and conservation biology. Although broad models have been proposed to estimate the population abundance using existing data, available data is often limited. With no information available, a population estimation will rely on time consuming field surveys. Typically, time is a critical constraint in conservation and often management decisions must be made quickly under the data limited situation. Depending on time and budgetary constraints, the required accuracy of field survey changes significantly. Hence, it is desirable to set up an effective survey design to minimize time and effort of sampling given required accuracy. We examine a spatially-explicit approach to population estimation using spatial point processes, enabling us to explicitly and consistently discuss various sampling designs. We find that the accuracy of abundance estimation varies with both ecological factors and survey design. Although the spatial scale of sampling does not affect estimation accuracy when the underlying individual distribution is random, it decreases with the sampled unit size if individuals tend to form clusters. These results are derived analytically and checked numerically. Obtained insights provide a benchmark to predict the quality of population estimation, and improve survey designs for ecological studies and conservation.

ecology↗