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Environmental DNA, or eDNA—DNA shed from organisms and extracted from environmental samples—is an emerging survey technique that has the potential to transform biodiversity monitoring in freshwater ecosystems. We provide a brief overview of the primary methodological aspects of eDNA sampling that ecologists should consider before taking environmental samples in the field. We outline five key methodological considerations: (i) targeting single species vs multiple species; (ii) where and when to sample; (iii) how much water to collect; (iv) how many samples to take; and (v) recognising potential sources of false positives. The need to account for false negatives and false positives in eDNA surveys, and the power of species occupancy detection models in accounting for imperfect detection, is also discussed. [ABSTRACT FROM AUTHOR]

Smooth newts (Lissotriton vulgaris) established recently in Melbourne, Australia. Previously, the population's disease status was unknown. Samples from 34 adults and 78 larvae, collected 2011–16, were tested for two pathogens driving the global amphibian extinction crisis. The fungus Batrachochytrium dendrobatidis was identified (6.3% quantitative PCR positive); ranaviruses were not detected. [ABSTRACT FROM AUTHOR]

• Background of insect images can reveal microhabitat information of species. • Machine learning approaches benchmarked against manual algorithm for microhabitat classification. • Insect image backgrounds classified as natural or anthropogenic microhabitats. • Thorough experimentations with three insect species are performed. • Automated algorithms proved to be better approach with 97.4% accuracy. Habitat fragmentation under increased urbanisation, industrial agriculture and land clearing, are changing the way insects occupy habitat. Some species are highly adaptable and may occupy urbanised areas, utilising anthropogenic microhabitat-scale features. Other species are dependent on natural elements of their habitats, having to locate small regions of natural microhabitat within increasingly hostile landscapes. Consequently, humans are encountering insects in new settings. Identifying and analysing insects' use of natural and anthropogenic microhabitats is therefore important to assess their responses to a changing environment, for instance to improve pollination or manage invasive pests. But such studies are labour-intensive. Traditional studies of insect microhabitat use can now be supplemented by machine learning-based insect image analysis. Typically, research has focused on automatic insect classification, but valuable data appearing in image backgrounds has been ignored. In this research, we analysed the backgrounds of insect images available in the Atlas of Living Australia database to determine the microhabitats in which they were commonly photographed. We analysed the image backgrounds of three globally distributed insect species that are common across Australia: Drone flies (Eristalis tenax), European honey bees (Apis mellifera), and European wasps (Vespula germanica). Image backgrounds were classified broadly as either natural or anthropogenic using computer vision and machine learning tools benchmarked against a manual classification algorithm. Our automated image background classification achieved 97.4 % accuracy when compared against manual classification. Mis-classifications were scarce, usually less than 1 % , and primarily for backgrounds of wood and soil or bare ground. Our results indicate that drone flies and European honey bees were predominantly photographed against natural backgrounds (flies manual classifier 95 ± 3 % , automated classifier 94 % , bees 89 ± 2 % , 87 %), implying frequent observations by humans in natural microhabitat. European wasps were less frequently photographed against natural backgrounds (70 ± 6 % , 63 %). Within this data set, observations of the wasps in anthropogenic microhabitats were more common than for flies and bees. Our results are aligned with the expectation that the wasps are relatively well-suited to urban environments, and that European honey bees and drone flies utilise natural features of their environment. In general, although biases in data collected without formal protocols limits their application, our new automated approach for image background analysis can provide valuable data about insects' interactions with humans, our artefacts, and natural features of their environments. [ABSTRACT FROM AUTHOR]

Environmental DNA (eDNA) sampling—the detection of intra- or extra-cellular DNA in environmental samples—is a rapid and sensitive survey method for detecting aquatic species. Single-species detection methods (typically based on PCR or LAMP) have been shown to be more sensitive for detecting target species than multi-species detection methods, such as metabarcoding. However, previous studies have generally only compared these two eDNA detection approaches for a single target species and have used different methodological and statistical approaches. Here we present a comparison of single- and multi-species eDNA detection methods, drawing on two published case studies (one fish, one amphibian) and two new extensive datasets on a freshwater mammal (the platypus). To ensure consistent conclusions regarding the sensitivity of each eDNA method, we use the same hierarchical site occupancy-detection model for each dataset, incorporating uncertainty at the site, water sample, and technical replicate level. Overall, qPCR achieved higher detection probabilities than metabarcoding across species and datasets. However, differences in sensitivity between detection methods varied depending on methodological decisions concerning what constitutes a true positive detection (i.e., qPCR and metabarcoding thresholds). The decision as to which eDNA detection method to use should always be influenced by the study aims, but our results suggest that single-species detection methods based on qPCR may be preferable when the aim is to achieve a high detection probability for target species. • A hierarchical Bayesian approach to consistently compare eDNA detection methods. • Detection probability was compared between qPCR and metabarcoding. • qPCR was more sensitive compared to metabarcoding across 3 species and 4 study designs. • Methodological decisions such as thresholds impacted detection probability. • There are trade-offs between the need for multi-species information and sensitivity. [ABSTRACT FROM AUTHOR]

Understanding the locations of potential invasion hotspots and the extent to which they overlap with biodiversity hotspots is crucial for prioritizing efforts to reduce the impacts of alien species on global biodiversity. Using ensembles of species distribution models based on climate, anthropogenic predictors, vegetation, and water resources, we predict global potential invasion hotspots for alien herpetofauna (reptiles and amphibians). On average, when subjected to current and future climate scenarios, potential richness of alien herpetofauna per grid cell (the minimum unit of our spatial variables for modeling and projecting) in biodiversity hotspots is nearly 1.4 times higher than in other regions. Furthermore, potential invasion hotspots are projected to occupy a large proportion of the total area within biodiversity hotspots. These results suggest that biodiversity hotspots are at greater risk from alien herpetofaunal invasions than are other regions. Our results provide key information for globally targeting early detection and rapid-response programs to help prevent or mitigate future impacts of alien herpetofauna on biodiversity. [ABSTRACT FROM AUTHOR]

Effective management of alien species requires detecting populations in the early stages of invasion. Environmental DNA (eDNA) sampling can detect aquatic species at relatively low densities, but few studies have directly compared detection probabilities of eDNA sampling with those of traditional sampling methods. We compare the ability of a traditional sampling technique (bottle trapping) and eDNA to detect a recently established invader, the smooth newt Lissotriton vulgaris vulgaris, at seven field sites in Melbourne, Australia. Over a four-month period, per-trap detection probabilities ranged from 0.01 to 0.26 among sites where L. v. vulgaris was detected, whereas per-sample eDNA estimates were much higher (0.29-1.0). Detection probabilities of both methods varied temporally (across days and months), but temporal variation appeared to be uncorrelated between methods. Only estimates of spatial variation were strongly correlated across the two sampling techniques. Environmental variables (water depth, rainfall, ambient temperature) were not clearly correlated with detection probabilities estimated via trapping, whereas eDNA detection probabilities were negatively correlated with water depth, possibly reflecting higher eDNA concentrations at lower water levels. Our findings demonstrate that eDNA sampling can be an order of magnitude more sensitive than traditional methods, and illustrate that traditional- and eDNA-based surveys can provide independent information on species distributions when occupancy surveys are conducted over short timescales. [ABSTRACT FROM AUTHOR]

iEcology is used to supplement traditional ecological data by sourcing large quantities of media from the internet. Images and their metadata are widely available online and can provide information on species occurrence, behaviour and visible traits. However, this data is inherently noisy and data quality varies significantly between sources. Many iEcology studies utilise data from a single source for simplicity and efficiency. Hence, a tool to compare the suitability of different media sources in addressing a particular research question is needed. We provide a simple, novel way to estimate the fraction of images within multiple unverified datasets that potentially depict a specified target fauna. Our method, the Sum of Tag Frequency Differences (STFD), uses any pretrained, general-purpose image classifier. One of the method's innovations is that it does not require training the classifier to recognise the target fauna. Instead, STFD analyses the frequency of the generic text-tags returned by a classifier for multiple datasets and compares them to the corresponding frequencies of an authoritative image dataset that depicts only the target organism. From this comparison, STFD allows us to deduce the fraction of images of the target in unverified datasets. To validate the STFD approach, we processed images from five sources: Flickr, iNaturalist, Instagram, Reddit and Twitter. For each media source, we conducted an STFD analysis of three fauna invasive to Australia: Cane toads (Rhinella marina), German wasps (Vespula germanica), and the higher-level colloquial taxonomic classification, "wild rabbits". We found the STFD provided an accurate assessment of image source relevance across all data sources and target organisms. This was demonstrated by the consistent, very strong correlation (toads r ≥0.97, wasps r ≥0.95, wild rabbits≥ 0.95) between STFD predictions, and the fraction of target images in a source dataset observed by a human expert. The STFD provides a low-cost, simple and accurate comparison of the relevance of online image sources to specific fauna for iEcology applications. It does not require expertise in machine learning or training neural-network species-specific classifiers. The method enables researchers to assess multiple image sources to select those warranting detailed investigation for the development of tools for web-scraping, citizen science campaigns, further monitoring or analysis. [ABSTRACT FROM AUTHOR]