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Detection of a colonizing, aquatic, non-indigenous species


*Correspondence: Chad T. Harvey, Great Lakes Institute for Environmental Research, University of Windsor, Windsor, Ontario N9B 3P4, Canada. E-mail:


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.