The effects of class size and class separation on fractional abundance estimation accuracy

2004 ◽  
Author(s):  
Charles L. Matson ◽  
S. Maile Giffin ◽  
Kris Hamada
Keyword(s):  
Class Size ◽  
Estimation Accuracy ◽  
Abundance Estimation ◽  
Class Separation ◽  
Fractional Abundance

scholarly journals Estimation accuracy of species abundance based on environmental DNA with relation to its production source, state, and assay methodology suggested by meta-analyses

2021 ◽  
Author(s):  
Toshiaki Jo ◽  
Hiroki Yamanaka
Keyword(s):  
Meta Analysis ◽  
Empirical Studies ◽  
Species Abundance ◽  
Environmental Dna ◽  
Efficient Estimation ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Abundance Estimation ◽  
Natural Environments ◽  
Promising Tool

Environmental DNA (eDNA) analysis is a promising tool for non-disruptive and cost-efficient estimation of species abundance. However, its practical applicability in natural environments is limited because it is unclear whether eDNA concentrations actually represent species abundance in the field. Although the importance of accounting for eDNA dynamics, such as transport and degradation, has been discussed, the influences of eDNA characteristics, including production source and state, and methodology, including collection and quantification strategy and abundance metrics, on the accuracy of eDNA-based abundance estimation were entirely overlooked. We conducted a meta-analysis using 56 previous eDNA literature and investigated the relationships between the accuracy (R2) of eDNA-based abundance estimation and eDNA characteristics and methodology. Our meta-regression analysis found that R2 values were significantly lower for crustaceans than fish, suggesting that less frequent eDNA production owing to their external morphology and physiology may impede accurate estimation of their abundance via eDNA. Moreover, R2 values were positively associated with filter pore size, indicating that selective collection of larger-sized eDNA, which is typically fresher, could improve the estimation accuracy of species abundance. Furthermore, R2 values were significantly lower for natural than laboratory conditions, while there was no difference in the estimation accuracy among natural environments. Our findings shed a new light on the importance of what characteristics of eDNA should be targeted for more accurate estimation of species abundance. Further empirical studies are required to validate our findings and fully elucidate the relationship between eDNA characteristics and eDNA-based abundance estimation.

scholarly journals Estimation accuracy of species abundance based on environmental DNA with relation to its production source and state suggested by meta-analyses

2021 ◽  
Author(s):  
Toshiaki Jo ◽  
Hiroki Yamanaka
Keyword(s):  
Empirical Studies ◽  
Species Abundance ◽  
Environmental Dna ◽  
Molecular State ◽  
Efficient Estimation ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Abundance Estimation ◽  
Natural Environments ◽  
Meta Analyses

Environmental DNA (eDNA) analysis is a promising tool for non-disruptive and cost-efficient estimation of species abundance. However, its practical applicability in natural environments is limited owing to a potential gap between eDNA concentration and species abundance in the field. Although the importance of accounting for eDNA dynamics, such as transport and degradation, has been discussed, the influence of eDNA characteristics, including production source and cellular/molecular state, on the accuracy of eDNA-based abundance estimation was entirely overlooked. We conducted meta-analyses using 44 of previous eDNA studies and investigated the relationships between the accuracy (R) of eDNA-based abundance estimation and eDNA characteristics. First, we found that estimated R values were significantly lower for crustaceans and mussels than fish. This finding suggests that less frequent eDNA production of these taxa owing to their external morphology and physiology may impede accurate estimation of their abundance via eDNA. Moreover, linear mixed modeling showed that, despite high variances, R values were positively correlated with filter pore size, indicating that selective collection of larger-sized eDNA, which is typically fresher, could improve the estimation accuracy of species abundance. Although our collected dataset was somewhat biased to the studies targeting specific taxa, our findings shed a new light on the importance of what characteristics of eDNA should be targeted for more accurate estimation of species abundance. Further empirical studies are required to validate our findings and fully elucidate the relationship between eDNA characteristics and eDNA-based abundance estimation.

scholarly journals Spatially explicit approach to population abundance estimation in field surveys

2017 ◽  
Author(s):  
Nao Takashina ◽  
Buntarou Kusumoto ◽  
Maria Beger ◽  
Suren Rathnayake ◽  
Hugh P. Possingham
Keyword(s):  
Point Processes ◽  
Survey Design ◽  
Ecological Factors ◽  
Population Estimation ◽  
Estimation Accuracy ◽  
Abundance Estimation ◽  
Spatially Explicit ◽  
Population Abundance ◽  
Field Surveys ◽  
Explicit Approach

AbstractThe 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.

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