Detection of a colonizing, aquatic, non‐indigenous species

Abstract

Detecting the presence of rare species has interested ecologists and conservation biologists for many years. A particularly daunting application of this problem pertains to the detection of non‐indigenous species (NIS) as they colonize new ecosystems. Ethical issues prevent experimental additions of NIS to most natural systems to explore the relationship between sampling intensity and the detection probability of a colonizing NIS. Here we examine this question using a recently introduced water flea, Cercopagis pengoi, in Lake Ontario. The species has biphasic population development, with sexually‐produced ‘spring morphs’ developing prior to parthenogenetically‐produced ‘typical’ morphs. Thus, this biphasic morphology allows distinction between new colonists (spring morphs) from subsequent generations. We repeatedly sampled Hamilton Harbour, Lake Ontario for the presence of both spring and typical morphs. Probability of detection was positively related to both the number of samples taken and animal density in the lake; however, even highly intensive sampling (100 samples) failed to detect the species in early spring when densities were very low. Spatial variation was greatest when densities of Cercopagis were intermediate to low. Sub‐sampling, which increased space between adjacent samples, significantly decreased the number of samples required to reach greater, calculated detection probabilities on these dates. Typical sampling protocols for zooplankton have a low probability (< 0.2) of detecting the species unless population density is high. Results of this study suggest that early detection of colonizing, aquatic NIS may be optimized through use of a risk‐based sampling design, combined with high sampling intensity in areas deemed most vulnerable to invasion, rather than less intensive sampling at a wider array of sites.

Are zooplankton invasions in constructed waters facilitated by simple communities?

“…However, sampling a series of random sites has a much greater chance of coring a patch of sediment containing a high number of viable eggs, or simply attaining a representative sample of species. Secondly, it has been suggested (Harvey et al, 2009) that by sampling at a number of widely spread sites there is a greater probability of obtaining organisms that may only be present in small numbers, such as newly introduced non-indigenous species. Harvey et al (2009) also found that whilst the number of samples collected is important for detecting low density species, it was the spatial arrangement that had the greatest influence on the success of detection.…”

Early detection of invasive species in marine ecosystems using high-throughput sequencing: technical challenges and possible solutions

“…The high detection sensitivity has largely increased the detection probability; for example, when more than one larva of a highly invasive species, golden mussel Limnoperna fortunei, occurred in plankton samples, the detection probability was 100 % (Zhan et al 2014a). In contrast, typical sampling protocols have a low probability (<0.2) of detecting the species unless population density is high (Harvey et al 2009). Using environmental samples, Zaiko et al (2015) identified four NIS from plankton samples collected from southeastern Baltic Sea coastal zones based on HTS metabarcoding; we found 24 NIS in a comprehensive survey on 16 major Canadian ports, 11 of which were detected in previously unreported locations (unpublished data).…”

“…Such detection challenges become more obvious in some particular ecosystems such as marine and coastal ones where organisms may be small, geographically constrained, and hidden beneath the water surface (McDonald 2004;Jerde et al 2011). Traditional methods, which rely on capture using different sampling instruments and then identification via assessment of morphological and/or anatomical characteristics, have been empirically proven as low-resolution or low-throughput means for the detection of newly introduced NIS (e.g., <20 % based on bulk samples; Harvey et al 2009). Thus, the comprehensive identification of rare taxa is time-consuming and laborious in marine and coastal ecosystems.…”

Combining citizen science, bioclimatic envelope models and observed habitat preferences to determine the distribution of an inconspicuous, recently detected introduced bee (Halictus smaragdulus Vachal Hymenoptera: Halictidae) in Australia

“…Determining the distribution of newly introduced species is problematic, as it is difficult to detect species that are only present in low densities (Harvey et al 2009). …”

“…To ensure early detection of introduced species, and to estimate the invaded range, it is important to develop efficient sampling strategies (Harvey et al 2009;Hauser and McCarthy 2009).…”