ORCID Profile
0000-0002-5091-3575
Current Organisation
Kyushu University
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Cold Spring Harbor Laboratory
Date: 12-05-2017
DOI: 10.1101/137158
Abstract: 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 in idual distributions (mapping unit), and the survey area within each mapping unit (s ling unit). We developed an ecological survey method for mapping in idual distributions by applying spatially-explicit stochastic models. We used spatial point processes to describe in idual spatial placements using either random or clustering processes. We then designed ecological surveys with different spatial scales and in idual detectability. We found that the choice of mapping unit affected the presence mapped fraction, and the fraction of the total in iduals 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 in iduals situated in the presence detected units. The developed theory may facilitate management decision-making and inform the design of monitoring and data gathering.
Publisher: Springer Science and Business Media LLC
Date: 23-06-2018
Publisher: Elsevier BV
Date: 09-2018
DOI: 10.1016/J.JTBI.2018.05.013
Abstract: 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 in idual distribution patterns (e.g. random/clustered s ling and in idual distribution). We demonstrate that clustered s ling decreases the estimation precision when in iduals form clusters, while s ling designs do not affect the estimation accuracy when in iduals are distributed randomly. Regardless of in idual distribution, the estimation precision becomes higher with increasing total population abundance and the s led fraction. These insights provide theoretical bases for efficient field survey designs in information deficiency situations.
Publisher: Wiley
Date: 03-2021
DOI: 10.1111/JVS.13017
Publisher: Springer Science and Business Media LLC
Date: 11-12-2018
Publisher: Cold Spring Harbor Laboratory
Date: 26-04-2017
DOI: 10.1101/131037
Abstract: 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 s ling given required accuracy. We examine a spatially-explicit approach to population estimation using spatial point processes, enabling us to explicitly and consistently discuss various s ling designs. We find that the accuracy of abundance estimation varies with both ecological factors and survey design. Although the spatial scale of s ling does not affect estimation accuracy when the underlying in idual distribution is random, it decreases with the s led unit size if in iduals 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.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Buntarou Kusumoto.