Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 83,702 bioRxiv papers from 360,579 authors.
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in category ecology
3,586 results found. For more information, click each entry to expand.
2,364 downloads ecology
The Dirichlet-multinomial mixture model (DMM) and its extensions provide powerful new tools for interpreting the ecological dynamics underlying taxon abundance data. However, like many complex models, how effectively they capture the many features of empirical data is not well understood. In this work, we expand the DMM to an infinite mixture model (iDMM) and use posterior predictive distributions (PPDs) to explore the performance in three case studies, including two amplicon metagenomic time series. We avoid concentrating on fluctuations within individual taxa and instead focus on consortial-level dynamics, using straight-forward methods for visualizing this perspective. In each study, the iDMM appears to perform well in organizing the data as a framework for biological interpretation. Using the PPDs, we also observe several exceptions where the data appear to significantly depart from the model in ways that give useful ecological insight. We summarize the conclusions as a set of considerations for field researchers: problems with samples and taxa; relevant scales of ecological fluctuation; additional niches as outgroups; and possible violations of niche neutrality.
2,321 downloads ecology
Citizen science data are valuable for addressing a wide range of ecological research questions, and there has been a rapid increase in the scope and volume of data available. However, data from large-scale citizen science projects typically present a number of challenges that can inhibit robust ecological inferences. These challenges include: species bias, spatial bias, variation in effort, and variation in observer skill. To demonstrate key challenges in analysing citizen science data, we use the example of estimating species distributions with data from eBird, a large semi-structured citizen science project. We estimate three widely applied metrics for describing species distributions: encounter rate, occupancy probability, and relative abundance. For each method, we outline approaches for data processing and modelling that are suitable for using citizen science data for estimating species distributions. Model performance improved when data processing and analytical methods addressed the challenges arising from citizen science data. The largest gains in model performance were achieved with two key processes 1) the use of complete checklists rather than presence-only data, and 2) the use of covariates describing variation in effort and detectability for each checklist. Including these covariates accounted for heterogeneity in detectability and reporting, and resulted in substantial differences in predicted distributions. The data processing and analytical steps we outlined led to improved model performance across a range of sample sizes. When using citizen science data it is imperative to carefully consider the appropriate data processing and analytical procedures required to address the bias and variation. Here, we describe the consequences and utility of applying our suggested approach to semi-structured citizen science data to estimate species distributions. The methods we have outlined are also likely to improve other forms of inference and will enable researchers to conduct robust analyses and harness the vast ecological knowledge that exists within citizen science data.
2,312 downloads ecology
Data management and publication are core components of the research process. An emerging challenge that has received limited attention in biology is managing, working with, and providing access to data under continual active collection. "Evolving data" present unique challenges in quality assurance and control, data publication, archiving, and reproducibility. We developed a evolving data workflow for a long-term ecological study that addresses many of the challenges associated with managing this type of data. We do this by leveraging existing tools to: 1) perform quality assurance and control; 2) import, restructure, version, and archive data; 3) rapidly publish new data in ways that ensure appropriate credit to all contributors; and 4) automate most steps in the data pipeline to reduce the time and effort required by researchers. The workflow uses two tools from software development, version control and continuous integration, to create a modern data management system that automates the pipeline.
2,220 downloads ecology
Background Using metagenomics to determine animal diet offers a new and promising alternative to current methods. Here we show that rapid and inexpensive diet quantification is possible through metagenomic sequencing with the portable Oxford Nanopore Technologies (ONT) MinION. Using an amplification-free approach, we profiled the stomach contents from wild-caught rats. Results We conservatively identified diet items from over 50 taxonomic orders, ranging across nine phyla that include plants, vertebrates, invertebrates, and fungi. This highlights the wide range of taxa that can be identified using this simple approach. We calibrate the accuracy of this method by comparing the characteristics of reads matching the ground-truth host genome (rat) to those matching diet items, and show that at the family-level, false positive taxon assignments are approximately 97.5% accurate. We also suggest a way to mitigate for database biases in metagenomic approaches. Finally, we implement a constrained ordination analysis and show that we can identify the sampling location of an individual rat within tens of kilometres based on diet content alone. Conclusions This work establishes proof-of-principle for long-read metagenomic methods in quantitative diet analysis. We show that diet content can be quantified even with limited expertise, using a simple, amplification free workflow and a relatively inexpensive and accessible next generation sequencing method. Continued increases in the accuracy and throughput of ONT sequencing, along with improved genomic databases, suggests that a metagenomic approach to quantification of animal diets will become an important method in the future.
2,162 downloads ecology
Inferring species interactions from co-occurrence data is one of the most controversial tasks in community ecology. One difficulty is that a single pairwise interaction can ripple through an ecological network and produce surprising indirect consequences. For example, the negative correlation between two competing species can be reversed in the presence of a third species that is capable of outcompeting both of them. Here, I apply models from statistical physics, called Markov networks or Markov random fields, that can predict the direct and indirect consequences of any possible species interaction matrix. Interactions in these models can also be estimated from observed co-occurrence rates via maximum likelihood, controlling for indirect effects. Using simulated landscapes with known pairwise interaction strengths, I evaluated Markov networks and six existing approaches. The Markov networks consistently outperformed other methods, correctly isolating direct interactions between species pairs even when indirect interactions or abiotic factors largely overpowered them. Two computationally efficient approximations, based on controlling for indirect effects with linear or generalized linear models, also performed well. Indirect effects reliably caused a common null modeling approach to produce incorrect inferences, however.
2,141 downloads ecology
Succinate dehydrogenase inhibitors (SDHIs) are now widely used worldwide as fungicides to limit the proliferation of molds in cereal crops, or to better preserve fruits, vegetables, and seeds from these molds, as well as to facilitate the lawn care for public spaces and golf courses. According to the companies that produce them, the SDHIs quite specifically inhibit the activity of the succinate dehydrogenase in the molds. We here establish that these inhibitors readily inhibit the earthworm and the human enzyme, raising a new concern on the danger of their large scale utilization in agriculture. This is all the more worrying as we know that the loss of function, partial or total, of the SDH activity caused by genetic variants causes severe human neurological diseases, or leads to the development of tumors and/or cancers.
2,137 downloads ecology
Mobile phones can be found almost everywhere across the globe, upholding a direct point-to-point connection between the device and the broadcast tower. The emission of radiofrequency electromagnetic radiation (RF-EMF) puts the surrounding environment inevitably into contact with this pollutant. We have therefore exposed honey bee queen larvae to the radiation of a common mobile phone device (GSM) during all stages of their pre-adult development including pupation. After 14 days of exposure, hatching of adult queens was assessed and mating success after further 11 days, respectively. Moreover, full colonies were established of five of the untreated and four of the treated queens to contrast population dynamics. We found that mobile phone radiation had significantly reduced the hatching ratio but not the mating success. If treated queens were successfully mated, colony development was not adversely affected. We provide evidence that RF-EMF only acts detrimental within the sensitivity of pupal development, once succeeded this point, no further impairment has manifested in adulthood. Our results are discussed against the background of long-lasting consequences for colony performance and the possible implication on periodic colony losses.
2,128 downloads ecology
Effective ecosystem conservation and resource management require quantitative monitoring of biodiversity, including accurate descriptions of species composition and temporal variations of species abundance. Therefore, quantitative monitoring of biodiversity has been performed for many ecosystems, but it is often time- and effort-consuming and costly. Recent studies have shown that environmental DNA (eDNA), which is released to the environment from macro-organisms living in a habitat, contains information about species identity and abundance. Thus, analyzing eDNA would be a promising approach for more efficient biodiversity monitoring. In the present study, we added internal standard DNAs (i.e., known amounts of short DNA fragments from fish species that have never been observed in a sampling area) to eDNA samples, which were collected weekly from a coastal marine ecosystem in Maizuru-Bay, Kyoto, Japan (from April 2015 to March 2016), and performed metabarcoding analysis using Illumina MiSeq to simultaneously identify fish species and quantify fish eDNA copy numbers. A correction equation was obtained for each sample using the relationship between the number of sequence reads and the added amount of the standard DNAs, and this equation was used to estimate the copy numbers from the sequence reads of non-standard fish eDNA. The calculated copy numbers showed significant positive correlation with those determined by quantitative PCR, suggesting that eDNA metabarcoding with standard DNA enabled useful quantification of eDNA. Furthermore, for samples that show a high level of PCR inhibition, our method might allow more accurate quantification than qPCR because the correction equations generated using internal standard DNAs would include the effect of PCR inhibition. A single run of Illumina MiSeq produced > 70 quantitative fish eDNA time series in our study, showing that our method could contribute to more efficient and quantitative monitoring of biodiversity.
2,104 downloads ecology
Global climate is changing rapidly and is accompanied by large-scale fragmentation and destruction of habitats. Since dispersal is the first line of defense for mobile organisms to cope with such adversities in their environment, it is important to understand the causes and consequences of evolution of dispersal. Although dispersal is a complex phenomenon involving multiple dispersal-traits like propensity (tendency to leave the natal patch) and ability (to travel long distances), the relationship between these traits is not always straight-forward, it is not clear whether these traits can evolve simultaneously or not, and how their interactions affect the overall dispersal profile. To investigate these issues, we subjected four large (N~2500) outbred populations of Drosophila melanogaster to artificial selection for increased dispersal, in a setup that mimicked increasing habitat fragmentation over 33 generations. The propensity and ability of the selected populations were significantly greater than the non-selected controls and the difference persisted even in the absence of proximate drivers for dispersal. The dispersal kernel evolved to have significantly greater standard deviation and reduced values of skew and kurtosis, which ultimately translated into the evolution of a greater frequency of long-distance dispersers (LDDs). We also found that although sex-biased dispersal exists in Drosophila melanogaster, its expression can vary depending on which dispersal component is being measured and the environmental condition under which dispersal takes place. Interestingly though, there was no difference between the two sexes in terms of dispersal evolution. We discuss possible reasons for why some of our results do not agree with previous laboratory and field studies. The rapid evolution of multiple components of dispersal and the kernel, expressed even in the absence of stress, indicates that dispersal evolution cannot be ignored while investigating eco-evolutionary phenomena like speed of range expansion, disease spread, evolution of invasive species and destabilization of metapopulation dynamics.
2,079 downloads ecology
Determining the host-parasitoid interactions and parasitism rates for invasive species entering novel environments is an important first step in assessing potential routes for biocontrol and integrated pest management. Conventional insect rearing techniques followed by taxonomic identification are widely used to obtain such data, but this can be time consuming and prone to biases. Here we present a Next Generation Sequencing approach for use in ecological studies which allows for individual level metadata tracking of large numbers of invertebrate samples through the use of hierarchically organised molecular identification tags. We demonstrate its utility using a sample data set examining both species identity and levels of parasitism in late larval stages of the Oak Processionary Moth (Thaumetopoea processionea - Linn. 1758), an invasive species recently established in the UK. Overall we find that there are two main species exploiting the late larval stages of Oak Processionary Moth in the UK with the main parasitoid (Carcelia iliaca - Ratzeburg, 1840) parasitising 45.7% of caterpillars, while a rare secondary parasitoid (Compsilura conccinata - Meigen, 1824) was also detected in 0.4% of caterpillars. Using this approach on all life stages of the Oak Processionary Moth may demonstrate additional parasitoid diversity. We discuss the wider potential of nested tagging DNA-metabarcoding for constructing large, highly-resolved species interaction networks.
2,075 downloads ecology
Significance: Foraged leafy greens are consumed around the globe, including in urban areas, and may play a larger role when food is scarce or expensive. It is thus important to assess the safety and nutritional value of wild greens foraged in urban environments. Methods: Field observations, soil tests, and nutritional and toxicology tests on plant tissue were conducted for three sites, each roughly 9 square blocks, in disadvantaged neighborhoods in the East San Francisco Bay Area in 2014--2015. The sites included mixed-use areas and areas with high vehicle traffic. Results: Edible wild greens were abundant, even during record droughts. Soil at some survey sites had elevated concentrations of lead and cadmium, but tissue tests suggest that rinsed greens of the tested species are safe to eat. Daily consumption of standard servings comprise less than the EPA reference doses of lead, cadmium, and other heavy metals. Pesticides, glyphosate, and PCBs were below detection limits. The nutrient density of 6 abundant species compared favorably to that of the most nutritious domesticated leafy greens. Conclusions: Wild edible greens harvested in industrial, mixed-use, and high-traffic urban areas in the San Francisco East Bay area are abundant and highly nutritious. Even grown in soils with elevated levels of heavy metals, tested species were safe to eat after rinsing in tap water. This does not mean that all edible greens growing in contaminated soil are safe to eat--tests on more species, in more locations, and over a broader range of soil chemistry are needed to determine what is generally safe and what is not. But it does suggest that wild greens could contribute to nutrition, food security, and sustainability in urban ecosystems. Current laws, regulations, and public-health guidance that forbid or discourage foraging on public lands, including urban areas, should be revisited.
2,074 downloads ecology
The study of ecological networks is severely limited by (i) the difficulty to access data, (ii) the lack of a standardized way to link meta-data with interactions, and (iii) the disparity of formats in which ecological networks themselves are represented. To overcome these limitations, we conceived a data specification for ecological networks. We implemented a database respecting this standard, and released a R package ( `rmangal`) allowing users to programmatically access, curate, and deposit data on ecological interactions. In this article, we show how these tools, in conjunctions with other frameworks for the programmatic manipulation of open ecological data, streamlines the analysis process, and improves eplicability and reproducibility of ecological networks studies.
2,067 downloads ecology
Climate change during the last five decades has impacted significantly on natural ecosystems and the rate of current climate change is of great concern among conservation biologists. Species Distribution Models (SDMs) have been used widely to project changes in species’ bioclimatic envelopes under future climate scenarios. Here, we aimed to advance this technique by assessing future changes in the bioclimatic envelopes of an entire mammalian Order, the Lagomorpha, using a novel framework for model validation based jointly on subjective expert evaluation and objective model evaluation statistics. SDMs were built using climatic, topographical and habitat variables for all 87 species under past and current climate scenarios. Expert evaluation and Kappa values were used to validate past and current distribution models and only those deemed ‘modellable’ through our framework were projected under future climate scenarios (58 species). We then used phylogenetically-controlled regressions to test whether species traits were correlated with predicted responses to climate change. Climate change will impact more than two-thirds of the Lagomorpha, with leporids (rabbits, hares and jackrabbits) likely to undertake poleward shifts with little overall change in range extent, whilst pikas are likely to show extreme shifts to higher altitudes associated with marked range declines, including the likely extinction of Kozlov’s Pika (Ochotona koslowi). Smaller-bodied species were more likely to exhibit range contractions and elevational increases, but showing little poleward movement, and fecund species were more likely to shift latitudinally and elevationally. Our results suggest that species traits may be important indicators of future climate change and we believe multi-species approaches, as demonstrated here, are likely to lead to more effective mitigation measures and conservation management.
2,057 downloads ecology
Pollen is the main protein and lipid source for honey bees (Apis mellifera), and nutritionally impoverished landscapes pose a threat to colony development. To determine colony nutritional demands, we analyzed a yearly cycle of bee-collected pollen from colonies in the field and compared it to colony worker production and honey bee body composition, for the first time in social insects. We monitored monthly bee production in ten colonies at each of seven sites throughout Israel, and trapped pollen bi-monthly in five additional colonies at each of four of these sites. Pollen mixtures from each sampling date and site were analyzed for weight, total protein, total fatty acids (FAs), and FA composition. Compared to more temperate climates, the eastern Mediterranean allows a relatively high yearly colony growth of ca. 300,000 to 400,000 bees. Colonies at higher elevation above sea level showed lower growth rates. Queen egg-laying rate did not seem to limit growth, as peaks in capped brood areas showed that queens lay a prolific 2,000 eggs a day on average, with up to 3,300 eggs in individual cases. Pollen uptake varied significantly among sites and seasons, with an overall annual mean total 16.8 kg per colony, containing 7.14 kg protein and 677 g fat. Overall mean pollen protein content was high (39.8%), and mean total FA content was 3.8%. Production cost, as expressed by the amount of nutrient used per bee, was least variable for linoleic acid and protein, suggesting these as the best descriptive variables for total number of bees produced. Linolenic acid levels in pollen during the autumn were relatively low, and supplementing colonies with this essential FA may mitigate potential nutritional deficiency. The essentiality of linoleic and linolenic acids was consistent with these FAs' tendency to be present at higher levels in collected pollen than in the expected nutrients in bee bodies, demonstrating a well-developed adjustment between pollinator nutritional demands and the nutritional value of food offered by pollinated plants.
2,053 downloads ecology
Popular frameworks for studying habitat selection include resource-selection functions (RSFs) and step-selection functions (SSFs) estimated using logistic and conditional logistic regression, respectively. Both frameworks compare environmental covariates associated with locations animals visit with environmental covariates at a set of locations assumed available to the animal. Conceptually, random coefficients could be used to accommodate inter-individual heterogeneity with either approach, but straightforward and efficient one-step procedures for fitting SSFs with random coefficients are currently lacking. We take advantage of the fact that the conditional logistic regression model (i.e., the SSF) is likelihood-equivalent to a Poisson model with stratum-specific intercepts. By interpreting the intercepts as a random effect with a large (fixed) variance, inference becomes feasible with standard Bayesian techniques, but also with frequentist methods that allow one to fix the variance of a random effect. We compare this approach to other commonly applied alternatives, including random intercept-only models, and to a two-step algorithm for fitting mixed-effects models. We also reinforce the need to weight available points when fitting RSFs, since models fit using "infinitely weighted logistic regression" have been shown to be equivalent to an inhomogeneous Poisson process (IPP). We generalize this result to "infinitely weighted Poisson regression", which converges to the same underlying IPP distribution. Using data from Eurasian otters (Lutra lutra) and mountain goats (Oreamnos americanus), we illustrate that our models lead to valid and feasible inference. In addition, we conduct a simulation study to demonstrate the importance of including random slopes when estimating individual- and population-level habitat-selection parameters. By providing coded examples using integrated nested Laplace approximations (INLA) and Template Model Builder (TMB) for Bayesian and frequentist analysis via the R packages R-INLA and glmmTMB, we hope to make efficient estimation of RSFs and SSFs with random effects accessible to anyone in the field. SSFs with individual-specific coefficients are particularly attractive since they can provide insights into movement and habitat-selection processes at fine-spatial and temporal scales, but these models had previously been very challenging to fit.
2,052 downloads ecology
DNA sampled from the environment (eDNA) is becoming a game changer for uncovering biodiversity patterns. By combining a conceptual model and empirical data, we test whether eDNA transported in river networks can be used as an integrative way to assess eukaryotic biodiversity for broad spatial scales and across the land-water interface. Using an eDNA metabarcode approach we detected 296 families of eukaryotes, spanning 19 phyla across the catchment of a river. We show for a subset of these families that eDNA samples overcome spatial autocorrelation biases associated with classical community assessments by integrating biodiversity information over space. Additionally, we demonstrate that many terrestrial species can be detected; thus revealing that eDNA in river-water also incorporates biodiversity information across terrestrial and aquatic biomes. Environmental DNA transported in river networks offers a novel and spatially integrated way to assess total biodiversity for whole landscapes and will transform biodiversity data acquisition in ecology.
2,041 downloads ecology
Matteo Dainese, Emily A. Martin, Marcelo A. Aizen, Matthias Albrecht, Ignasi Bartomeus, Riccardo Bommarco, Luisa G. Carvalheiro, Rebecca Chaplin-Kramer, Vesna Gagic, Lucas A. Garibaldi, Jaboury Ghazoul, Heather Grab, Mattias Jonsson, Daniel S. Karp, Christina M. Kennedy, David Kleijn, Claire Kremen, Douglas A. Landis, Deborah K. Letourneau, Lorenzo Marini, Katja Poveda, Romina Rader, Henrik G. Smith, Teja Tscharntke, Georg K.S. Andersson, Isabelle Badenhausser, Svenja Baensch, Antonio Diego M. Bezerra, Felix J.J.A. Bianchi, Virginie Boreux, Vincent Bretagnolle, Berta Caballero-Lopez, Pablo Cavigliasso, Aleksandar Ćetković, Natacha P. Chacoff, Alice Classen, Sarah Cusser, Felipe D. da Silva e Silva, G. Arjen de Groot, Jan H. Dudenhöffer, Johan Ekroos, Thijs Fijen, Pierre Franck, Breno M. Freitas, Michael P.D. Garratt, Claudio Gratton, Juliana Hipólito, Andrea Holzschuh, Lauren Hunt, Aaron L. Iverson, Shalene Jha, Tamar Keasar, Tania N. Kim, Miriam Kishinevsky, Björn K. Klatt, Alexandra-Maria Klein, Kristin M. Krewenka, Smitha Krishnan, Ashley E. Larsen, Claire Lavigne, Heidi Liere, Bea Maas, Rachel E. Mallinger, Eliana Martinez Pachon, Alejandra Martínez-Salinas, Timothy D. Meehan, Matthew G.E. Mitchell, Gonzalo A.R. Molina, Maike Nesper, Lovisa Nilsson, Megan E. O’Rourke, Marcell K. Peters, Milan Plećaš, Simon G. Potts, Davi de L. Ramos, Jay A. Rosenheim, Maj Rundlöf, Adrien Rusch, Agustín Sáez, Jeroen Scheper, Matthias Schleuning, Julia Schmack, Amber R. Sciligo, Colleen Seymour, Dara A. Stanley, Rebecca Stewart, Jane C. Stout, Louis Sutter, Mayura B. Takada, Hisatomo Taki, Giovanni Tamburini, Matthias Tschumi, Blandina F. Viana, Catrin Westphal, Bryony K. Willcox, Stephen D. Wratten, Akira Yoshioka, Carlos Zaragoza-Trello, Wei Zhang, Yi Zou, Ingolf Steffan-Dewenter
Human land use threatens global biodiversity and compromises multiple ecosystem functions critical to food production. Whether crop yield-related ecosystem services can be maintained by few abundant species or rely on high richness remains unclear. Using a global database from 89 crop systems, we partition the relative importance of abundance and species richness for pollination, biological pest control and final yields in the context of on-going land-use change. Pollinator and enemy richness directly supported ecosystem services independent of abundance. Up to 50% of the negative effects of landscape simplification on ecosystem services was due to richness losses of service-providing organisms, with negative consequences for crop yields. Maintaining the biodiversity of ecosystem service providers is therefore vital to sustain the flow of key agroecosystem benefits to society.
2,026 downloads ecology
Recent assessments of progress towards global conservation targets have revealed a paucity of indicators suitable for assessing the changing state of ecosystems. Moreover, land managers and planners are often unable to gain timely access to maps they need to support their routine decision-making. This deficiency is partly due to a lack of suitable data on ecosystem change, driven mostly by the considerable technical expertise needed to make ecosystem maps from remote sensing data. We have developed a free and open-access online remote sensing and environmental modelling application, REMAP (the remote ecosystem monitoring and assessment pipeline; https://remap-app.org) that enables volunteers, managers, and scientists with little or no experience in remote sensing to develop high-resolution classified maps of land cover and land use change over time. REMAP utilizes the geospatial data storage and analysis capacity of the Google Earth Engine, and requires only spatially resolved training data that define map classes of interest (e.g., ecosystem types). The training data, which can be uploaded or annotated interactively within REMAP, are used in a random forest classification of up to 13 publicly available predictor datasets to assign all pixels in a focal region to map classes. Predictor datasets available in REMAP represent topographic (e.g. slope, elevation), spectral (Landsat Archive image composites) and climatic variables (precipitation, temperature) that can inform on the distribution of ecosystems and land cover classes. The ability of REMAP to develop and export high-quality classified maps in a very short (<10 minute) time frame represents a considerable advance towards globally accessible and free application of remote sensing technology. By enabling access to data and simplifying remote sensing classifications, REMAP can catalyse the monitoring of land use and change to support environmental conservation, including developing inventories of biodiversity, identifying hotspots of ecosystem diversity, ecosystem-based spatial conservation planning, mapping ecosystem loss at local scales, and supporting environmental education initiatives.
1,996 downloads ecology
This study introduces statistical boosting for capture-mark-recapture (CMR) models. It is a shrinkage estimator that constrains the complexity of a CMR model in order to promote automatic variable-selection and avoid over-fitting. I discuss the philosophical similarities between boosting and AIC model-selection, and show through simulations that a boosted Cormack-Jolly-Seber model often out-performs AICc methods, in terms of estimating survival and abundance, yet yields qualitatively similar estimates. This new boosted CMR framework is highly extensible and could provide a rich, unified framework for addressing many topics in CMR, such as non-linear effects (splines and CART-like trees), individual-heterogeneity, and spatial components.
1,972 downloads ecology
1. Detecting aquatic macroorganisms with environmental DNA (eDNA) is a new survey method with broad applicability. However, the origin, state, and fate of aqueous macrobial eDNA - which collectively determine how well eDNA can serve as a proxy for directly observing organisms and how eDNA should be captured, purified, and assayed - are poorly understood. 2. The size of aquatic particles provides clues about their origin, state, and fate. We used sequential filtration size fractionation to measure, for the first time, the particle size distribution (PSD) of macrobial eDNA, specifically Common Carp (hereafter referred to as Carp) eDNA. We compared it to the PSDs of total eDNA (from all organisms) and suspended particle matter (SPM). We quantified Carp mitochondrial eDNA using a custom qPCR assay, total eDNA with fluorometry, and SPM with gravimetric analysis. 3. In a lake and a pond, we found Carp eDNA in particles from >180 to <0.2 µm, but it was most abundant from 1-10 µm. Total eDNA was most abundant below 0.2 µm and SPM was most abundant above 100 µm. SPM was ≤0.1% total eDNA, and total eDNA was ≤0.0004% Carp eDNA. 0.2 µm filtration maximized Carp eDNA capture (85%±6%) while minimizing total (i.e., non-target) eDNA capture (48%±3%), but filter clogging limited this pore size to a volume <250 mL. To mitigate this limitation we estimated a continuous PSD model for Carp eDNA and derived an equation for calculating isoclines of pore size and water volume that yield equivalent amounts of Carp eDNA. 4. Our results suggest that aqueous macrobial eDNA predominantly exists inside mitochondria or cells, and that settling plays an important role in its fate. For optimal eDNA capture, we recommend 0.2 µm filtration or a combination of larger pore size and water volume that exceeds the 0.2 µm isocline. In situ filtration of large volumes could maximize detection probability when surveying large habitats for rare organisms. Our method for eDNA particle size analysis enables future research to compare the PSDs of eDNA from other organisms and environments, and to easily apply them for ecological monitoring.
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