CIDANE: Comprehensive Isoform Discovery and Abundance Estimation

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.

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Movements of humpback whales between Kauai and Hawaii:implications for population structure and abundance estimation in the Hawaiian Islands

Marine Ecology Progress Series ◽

10.3354/meps175013 ◽

1998 ◽

Vol 175 ◽

pp. 13-22 ◽

Cited By ~ 25

Author(s):

S Cerchio ◽

CM Gabriele ◽

TF Norris ◽

LM Herman

Keyword(s):

Population Structure ◽

Hawaiian Islands ◽

Humpback Whales ◽

Abundance Estimation

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Recruitment and abundance estimation of glass eel genus Anguilla in three large estuaries on the coast of Sukabumi West Java

E3S Web of Conferences ◽

10.1051/e3sconf/202132201037 ◽

2021 ◽

Vol 322 ◽

pp. 01037

Author(s):

Triyanto ◽

Gadis Sri Haryani ◽

Mohammad Mukhlis Kamal ◽

Iwan Ridwansyah ◽

Fauzan Ali ◽

...

Keyword(s):

Indian Ocean ◽

Rainy Season ◽

Dominant Species ◽

Water Discharge ◽

Abundance Estimation ◽

West Java ◽

Glass Eel ◽

The Indian Ocean ◽

Glass Eels

The rivers on the Sukabumi Coast flow into the Indian Ocean. Three major rivers, namely the Cimandiri River, Cikaso River, and Cibuni River, are sources of glass eel fishing. This study aims to determine the recruitment and estimate of glass eel abundance for future glass eel management. The study was conducted from November 2020-April 2021. Estimation of glass eel abundance was carried out using a fyke net. The glass eel calculation is determined based on the number of glass eels caught, the water discharge entering the fyke net, the water discharge in the estuary, and other variables. The results showed that glass eel recruitment began at the beginning of the rainy season in November 2020 and lasted at the end of the study in April 2021. Anguilla bicolor bicolor is a dominant species of glass eel found in the three river estuaries. The estimated abundance of glass eel was approximately 2,583,438-13,556,650 ind./year or 326,24-1,812 kg/year. The abundance of glass eels at the estuary of Cimandiri River was higher than that of the estuary of Cibuni River and the estuary of Cikaso River.

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Variant abundance estimation for SARS-CoV-2 in wastewater using RNA-Seq quantification

10.1101/2021.08.31.21262938 ◽

2021 ◽

Author(s):

Jasmijn A. Baaijens ◽

Alessandro Zulli ◽

Isabel M. Ott ◽

Mary E. Petrone ◽

Tara Alpert ◽

...

Keyword(s):

Temporal Dynamics ◽

The United States ◽

Population Surveillance ◽

High Variability ◽

Clinical Samples ◽

Abundance Estimation ◽

Computational Techniques ◽

Rna Seq ◽

Clinical Sequencing ◽

Wastewater Samples

Effectively monitoring the spread of SARS-CoV-2 variants is essential to efforts to counter the ongoing pandemic. Wastewater monitoring of SARS-CoV-2 RNA has proven an effective and efficient technique to approximate COVID-19 case rates in the population. Predicting variant abundances from wastewater, however, is technically challenging. Here we show that by sequencing SARS-CoV-2 RNA in wastewater and applying computational techniques initially used for RNA-Seq quantification, we can estimate the abundance of variants in wastewater samples. We show by sequencing samples from wastewater and clinical isolates in Connecticut U.S.A. between January and April 2021 that the temporal dynamics of variant strains broadly correspond. We further show that this technique can be used with other wastewater sequencing techniques by expanding to samples taken across the United States in a similar timeframe. We find high variability in signal among individual samples, and limited ability to detect the presence of variants with clinical frequencies <10%; nevertheless, the overall trends match what we observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in variant prevalence in situations where clinical sequencing is unavailable or impractical.

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Estimating brown bear abundance and harvest rate on the southern Alaska Peninsula

PLoS ONE ◽

10.1371/journal.pone.0245367 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0245367

Author(s):

Earl F. Becker ◽

David W. Crowley

Keyword(s):

Sampling Method ◽

Distance Sampling ◽

Brown Bear ◽

Aerial Survey ◽

Abundance Estimation ◽

Game Management ◽

Geographic Scale ◽

Detection Function ◽

Harvest Rate ◽

Harvest Data

Abundance estimation of hunted brown bear populations should occur on the same geographic scale as harvest data analyses for estimation of harvest rate. Estimated harvest rates are an important statistic for managing hunted bear populations. In Alaska, harvest data is collected over large geographic units, called Game Management Units (GMUs) and sub-GMUs. These sub GMUs often exceed 10,000 km2. In the spring of 2002, we conducted an aerial survey of GMU 9D (12,600 km2) and GMU 10 (4,070 km2) using distance sampling with mark-resight data. We used a mark-resight distance sampling method with a two-piece normal detection function to estimate brown bear abundance as 1,682.9 (SE = 174.29) and 316.9 (SE = 48.25) for GMU 9D and GMU 10, respectively. We used reported hunter harvest to estimate harvest rates of 4.35% (SE = 0.45%) and 3.06% (SE = 0.47%) for GMU 9D and GMU 10, respectively. Management objective for these units support sustained, high quality hunting opportunity which harvest data indicate are met with an annual harvest rate of approximately 5–6% or less.

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