The New Zealand mudsnail (NZMS) is a small-bodied gastropod that has successfully invaded waters across multiple continents. This species has the ability to reach extremely high densities in streams and exclude other aquatic macroinvertebrates which higher trophic levels rely on as a food source. While the effects of NZMS are well studied, early detection methods for this species are limited almost entirely to environmental DNA (eDNA) testing. While eDNA is a valuable tool for the early detection of this species, low density sampling protocols are also essential to verify positive eDNA detections and to determine precise distributions so that management may be implemented in these areas during an invasion. The goal of our study is to evaluate and compare the efficacy of various quadrat sampling protocols to detect NZMS at low densities, and to determine the densities below which detection may become uncertain using these protocols. We tested 10-, 20-, and 30-quadrat grids within 100 m stream reaches, using both random and strategic selection of quadrat sites, to assess each design’s performance in overall probability of detection. We found that a non-random strategic sampling design was significantly more effective at detection of NZMS than a random design. Additionally, we found that, across study streams with different snail densities, taking 14 quadrat Surber samples using non-random strategic site selection consistently led to capture probabilities over 99%, with one exception in the stream with the lowest densities. To account for heterogeneity in habitat and snail density, we recommend using 30 quadrats with non-random strategic site selection to maximize detection in systems with unknown presence. This study outlines a sampling protocol to verify the physical presence of NZMS that can be adapted into monitoring programs or to confirm presence of this species following a suspected introduction.

Bootstrapping, Invasive Species, Quadrats, Physical Detection, Surber Sampler

Ecosystem health and proper functioning is imperative to the continued persistence of aquatic organisms and is largely influenced by having essential trophic levels intact and interacting in a stable manner (Scott et al. 2012). Ecosystem processes can be affected by multiple stressors, including disturbance and the introduction of invasive species (Cardinale and Palmer 2002; Gallardo et al. 2016). Aquatic invasive species have become increasingly common throughout North America, including within the American Southwest, where they have caused ecosystem health to decline in numerous watersheds (Dukes and Mooney 2004; Thomaz et al. 2015). The New Zealand mudsnail (Potamopyrgus antipodarum; hereafter NZMS), a gastropod native to freshwater streams and lakes throughout New Zealand, is one such aquatic invasive species that has been introduced throughout the region and has the ability to cause negative effects to proper ecosystem functioning of many waterbodies (Geist et al. 2022; Alonso et al. 2023). Within its native range, NZMS is a host to multiple trematode parasites which render their hosts infertile. This heavily limits the reproductive rate of NZMS, keeping their populations in check (Hechinger 2012). With the absence of these parasites outside of their native range, NZMS have the ability to reproduce unchecked and reach extremely high densities in streams (Hechinger 2012; Geist et al. 2022). The NZMS has been documented to alter the structure of the native invertebrate communities in both laboratory and field studies (Larson and Black 2016; Rakauskas et al. 2017), which has led to bottom-up effects on higher trophic levels that rely upon these invertebrate communities as a prey base. The expansion of this species, which now encompasses numerous waters in five continents, can be attributed to life history and physical characteristics of the species. This includes an optimized reproductive strategy, small size and ability to attach themselves to surfaces that are moved between waters, as well as their operculum, which provides the snail a strong resistance to desiccation when removed from aquatic systems (Richards et al. 2004; Alonso and Castro-Díez 2008; Geist et al. 2022). Additionally, this species is of little nutritional value in fish diets, with individuals of NZMS closing their opercula upon consumption and commonly passing through the fish intestinal tract undigested and still alive (Vinson and Baker 2008; Bruce et al. 2009). Because they outcompete aquatic invertebrates and are largely inaccessible as a food source, NZMS operate as a trophic sink to higher trophic levels, drastically interrupting the food chain. The difficulty in suppressing this species following an introduction, coupled with its high potential for invasion between waterbodies makes them extremely difficult to manage (Richards et al. 2004; Geist et al. 2022). Given the largescale impact this species can have on the food chain and long-term viability of native aquatic communities, it is imperative to be able to detect NZMS effectively and efficiently in systems outside of their native range to mitigate the effects as quickly as possible.

Environmental DNA (eDNA), which is DNA that has been shed into the environment by organisms, has been commonly used to detect various aquatic invasive species (AIS), including NZMS, in the critical early stages of invasion (Woodell et al. 2021). Compared to traditional methods of detection, eDNA offers advantages, such as low cost and the ability to detect presence of a target species at low densities without physically locating a species within a system where density is unknown (Woodell et al. 2021). Advancements in eDNA sampling methods, equipment, and assays have decreased sample processing times and have made it possible to obtain in situ results (Thomas et al. 2020). Despite the benefits of eDNA technology, the potential for false positives and false negatives remain prominent challenges in eDNA analyses (Bochove et al. 2020; Wood et al. 2021). Erroneous survey results can result in inappropriate management decisions for a particular stream. For example, false positives can be costly and pull finite resources away from where they are needed, while false negatives can delay detection and make it more difficult to respond to and control AIS species once they are established. Such errors can also be an issue in other methods of AIS detection. Presence of AIS is also sometimes reported by members of the public, and these reports are at times the first evidence of AIS in a particular waterbody. While these public reports can greatly assist in documenting the spread of AIS, particularly in waters that may not be monitored regularly, physical verification is still needed prior to implementing management actions (i.e., mobilizing staff, identifying treatment options, public postings, etc.) due to the potential for a false identification. Physical confirmation of NZMS presence addresses concerns related to false positives, and also provides instantaneous confirmation of presence, allowing for immediate action. Accurate records of occurrence following a suspected invasion are essential for managers to quickly define the severity of the problem and refocus management activities to suppress and/or control the species and minimize their spread within the ecosystem. Low species densities can complicate and impact both the spatial determination of their distribution in a stream system and the accuracy of their initial detection, particularly using eDNA. While eDNA remains a valuable tool in regard to detecting species introductions throughout aquatic ecosystems, physical verification following a positive eDNA detection or any other unsubstantiated report is necessary to verify presence before further management actions are implemented.

While the initial detection of an invasive species, such as via eDNA sampling, is crucial for directing rapid response efforts to control its spread, long-term management of NZMS requires the ability to confirm their presence via physical sampling. A sampling protocol that provides guidance on the sampling intensity and effort required to physically confirm presence will allow managers to better allocate survey resources and also have confidence in survey results. Thus, the goal of this study was to establish a standardized sampling method to physically and reliably detect NZMS in streams at low densities following a suspected introduction. To accomplish this, we tested multiple quadrat sampling designs for the purpose of physically detecting low-density populations of NZMS. Our specific objectives were: 1) to evaluate the number of quadrats needed within sampling designs to ensure detection at low densities in a stream setting, 2) to compare random versus non-random strategic site selection on detection, and 3) using the outcomes of Objective 1 and 2, to determine the lowest NZMS density that can be detected by these quadrat methods. Results from this study will provide managers with guidance on standardized methods for assessing the distribution of NZMS after positive eDNA detections or when physical surveys are necessary to verify the presence of the species.

Sampling for NZMS occurred from June to July, 2024. The distribution of substrate sizes across the three streams sampled varied slightly, with Canyon Creek having a higher percentage of large substratum (i.e., cobble and larger) while Tonto and Oak Creek had a higher proportion of fine sediments (Fig. 3). Mean sampled depth also differed between streams with Oak Creek being the deepest site and Tonto Creek being the shallowest (Fig. 3).

Figure 3. 

Distribution of substrate size (top) and mean sampled depth (bottom) of the three streams sampled for New Zealand mudsnails. Whiskers represent standard deviation.

Simulated scale residuals indicated no evidence of deviation from uniformity for our glmer model (p-value > 0.05) and no significant over dispersion was detected (alp-values > 0.05). Mean densities of NZMS observed across the three study streams were as follows: Oak Creek = 47.0 snails/m², Canyon Creek = 28.0 snails/m², and Tonto Creek = 8.2 snails/m² (Table 1). The proportion of NZMS captured also varied between sampling strategies (Table 1). Probability of detecting NZMS significantly differed between random site selection and non-random strategic site selection, with the latter resulting in significantly higher probability of detection (Z value = -6.38, p-value < 0.001; Table 2; Fig. 4). There was no statistical difference in detection probability by net across the 10-, 20-, or 30-quadrat groups in either the random or non-random strategic design (Z value = 0.22, p-value = 0.34; Table 2; Fig. 4), indicating that heterogeneity (i.e. density and distribution) was likely similar across sets of quadrats. Further, non-random strategic sampling was also significantly better at detecting NZMS in all streams sampled for this study when making pairwise comparisons using the function emmeans function (Oak Creek: Z value = -5.74, p-value <0.001; Canyon Creek: Z value = -2.12, p-value = 0.034; Tonto Creek: Z value = -0.96, p-value <0.001; Fig. 4). We found no difference in detection between taking 10-, 20-, or 30-quadrat samples in Oak and Canyon Creek (Oak Creek: Z value = 0.10, p-value = 0.920; Canyon Creek: Z value = 1.74, p-value = 0.082). However, taking 20 and 30 quadrat samples in Tonto Creek significantly increased detection probability relative to 10 samples (Z value = 0.07, p-value <0.001; Fig. 5), although this may be related to the higher prevalence of zero-density groups observed for the 10-quadrat groups in Tonto (Table 1). An additional sampling trip was taken to Tonto Creek in July of 2024 in an attempt to identify a lower density area of NZMS, relative to what was observed during sampling for this study, further down in the watershed to test the bounds of our sampling protocol. The results of this sampling yielded no individuals captured and all data collected during this sampling excursion was omitted from analysis.

Figure 4. 

Estimated probability of detection by individual sampling point in each stream, with whiskers representing 95% confidence intervals, of New Zealand mudsnails across all streams for random (panel A) and non-random strategic (panel B) sampling designs. Probabilities of detection represent the likelihood of capturing a NZMS in a single Surber sample derived from the bootstrapped glm models.

Figure 5. 

Sequential probability of detecting NZMS in 10-, 20-, 30-quadrat groups across all streams. Probabilities were calculated with bootstrapped glm models to predict sequential probabilities. Horizontal lines indicated 99% probability of detection.

Non-random strategic samples exceeded 99% sequential probability of detection in one pass in all streams and quadrat groups with the exception of one instance, using a 10-quadrat group in our lowest density stream (Table 2). By comparison, random site selection only exceeded a 95% sequential detection threshold in six of nine groupings (Table 2; Fig. 5). Using non-random strategic sampling, our models suggest that NZMS would be detected with 95% likelihood after the 10^th sequential quadrat and with 99% likelihood after the 14^th sequential quadrat at all streams sampled for this study with the exception of 10-quadrats in Tonto Creek, where the highest sequential probability of detection estimate was 65% (Fig. 5).

Our results suggest that the use of a non-random strategic sampling design provides a higher likelihood of detecting NZMS than the use of random samples in our protocols (Figs 4, 5). To account for patchy distribution affecting detection, while using non-random site selection, we attempted to distribute these samples throughout the entirety of the reach instead of limiting all samples to one zone within the reach that appeared to have optimal habitat. The results of this study highlight the importance of proper site selection within a stream when attempting to physically verify the presence of NZMS after a suspected invasion. Random sites are commonly used in routine monitoring because they allow for samples in a variation of habitat and reduce bias in observations (McClelland and Sass 2012). While these aspects are important when conducting representative monitoring of a species (i.e., annual monitoring), our results suggest that a non-random strategic design is more effective when attempting initial physical detection of NZMS invading a novel ecosystem, particularly when they are in low densities or still rare (Croft and Chow-Fraser 2009).

Non-random strategic sampling was able to exceed 99% sequential probability of detection with the 10-, 20-, and 30-quadrat group treatments in all streams sampled for this study with the exception of one iteration, 10-quadrat groups at our lowest density site. These results suggest that conducting quadrat utilizing a non-random strategic sampling design was effective at detection of NZMS even at low density, assuming a sufficient number of samples are taken. Where non-random strategic sampling was implemented in sampling, we were able to reach 99% sequential probability of detection within 14 Surber samples. We were only able to achieve a 65% sequential probability of detection in our 10-quadrat group at Tonto Creek, and it is unlikely we would have reached 99% after 14 samples based on the model. We observed a significant difference between 10-quadrat groups relative to 20- and 30-quadrat groups in Tonto Creek which was likely driven by having quadrat groups that contained zero NZMS in Tonto Creek. Although 14 quadrats were sufficient in detecting NZMS in all other sampling scenarios in this study, we recommend the use of a 30-quadrat sampling effort using the non-random strategic site selection protocols to ensure maximizing the detection of this species. This is a conservative number of quadrats based on our results; however, it will account for variability in sites (e.g., water depth, substrate size, and the amount of suitable NZMS habitat) and heterogeneity in distribution where these protocols may need to be implemented. It is possible that 30 quadrats is sufficient for detection of NZMS at densities lower than those observed during this study, although this could not be established from our results and further testing would be needed. If no NZMS are detected in a reach, the results of our study suggest that they are absent (with >99% likelihood) from that area, or that their densities are lower than those observed during this study (i.e., mean densities <8.2 snails/m²). The risk for false negatives will always exist; however, it is more likely in these cases that the selected reach falls outside of the current distribution of NZMS in a stream, rather than NZMS being present within the reach and not detected. For this reason, we also recommend sampling the next upstream and downstream reaches at a minimum near the positive eDNA detection site because at the early stages of the invasion these species may have a patchy distribution throughout the stream, although there may need to be more of a focus on sampling points upstream of the initial detection due to the dynamics of how eDNA travels in lotic systems (Petrovskaya et al. 2017). Although this will be a good starting point for managers in physically detecting NZMS, the travel distances of eDNA in lotic systems also heavily varies depending on many factors which managers should consider when sampling for this species following eDNA detection (Deiner and Altermatt 2014; Harrison et al. 2019).

Controlling the spread of aquatic invasive species is crucial for the continued health and functioning of ecosystems (Pejchar and Mooney 2009). Management approaches for controlling invasions are typically tailored to the species involved and the stage at which the invasion is detected (i.e., the Invasion Curve; Victorian Government 2010). Regardless of the target species, early detection is crucial for invasive species management because it potentially increases the probability of a possible eradication and the success of expansion control measures (Anderson 2005). Therefore, with the uncertainty surrounding eDNA detections, we recommend physical sampling for detections following positive eDNA results for validation as the first management action on the waterbody in questions. Physical verification following a positive eDNA detection or a public report of NZMS can be conducted relatively rapidly and allows confirmation prior to other management actions being implemented. The physical sampling protocols outlined in this study can also be used by managers to pinpoint invaded areas and establish the extent or distribution of NZMS.

We attempted to identify areas of low NZMS density for sampling, although they were unsuccessful and this represents a limitation of our study. One additional reach was sampled in Tonto Creek using our protocols in an attempt to test them on extremely low densities; however, these efforts did not yield capture of NZMS and we suspect they were absent from this reach. While the densities of NZMS observed in this study may seem high relative to other organisms (i.e., fish species) present in Arizona aquatic systems, this species has been documented to achieve densities of over 500,000 snails/m² (Geist et al. 2022). Additionally, past experiments utilizing this species have considered densities of 120 snails/m² as their low-density treatments (Hall et al. 2006; Lysene and Koetsier 2008). While our sampling framework could work effectively at lower densities of NZMS, these areas could not be identified within our study streams and further testing is needed. Another potential limitation of our results is related to stream depth. Oak Creek was deeper than both Tonto and Canyon Creek. During the sampling of Oak Creek, it became evident that areas with depths exceeding approximately one meter were ineffectively sampled with the Surber sampler, which in turn reduced the amount of the area within the stream that was sampled as quadrat sites needed to be moved to the nearest accessible site. Although NZMS were still effectively detected within Oak Creek using our protocols, larger rivers with elevated mean depths may require a combination of our quadrat protocols with different sampling techniques (i.e. the use of D nets, drift nets, taking additional samples in peripheral of stream, or other benthic samplers) or with a higher number of quadrats than those described in this research. Thus, further research is still needed to identify if this sampling strategy is effective in lower density areas as well as to describe methods that should be utilized in larger systems.

Physical verification of a species following a suspected invasion of a species is imperative when attempting to implement management for suppression or eradication. Here we have provided results from our testing of a sampling approach that was designed to provide a systematic, standardized method to physically verify the presence of NZMS when in low densities. We recommend a non-random strategic sampling effort utilizing 30-sample quadrat groups per 100-meter stream reach following a positive eDNA detection or public report of NZMS in a stream, nearest to the location of initial report. Specifically, non-random strategic sample sites should target preferential habitats for NZMS: areas with low water velocity, intermediate depth (while still being able to take Surber samples effectively), and small to intermediate substrate sizes. Further, we recommend spreading the thirty samples throughout the 100-meter sampling reach. Our models suggest implementing this recommended protocol would allow for >99% probability of detection in streams that met the same density and habitat parameters as those sampled over the course of this study. If the desire is only to confirm the presence of NZMS, sampling could conclude following the first quadrat where snails were found. If no snails are found within the first reach after 30 quadrats, we recommend sampling the 100 meter reaches immediately upstream and downstream of where the positive eDNA detection was made using this same protocol. This physical sampling protocol can be expanded beyond the verification of positive eDNA samples or public reports, by providing supplemental monitoring activities in streams or in areas where the presence of NZMS is unknown. In these cases, all 30 quadrats should be completed within each reach of interest to best ensure detection and to quantify density and distribution should they be found. Ultimately, managers will need to decide which and how many reaches to sample based on their available resources and management needs. These protocols were designed specifically for detection of NZMS; however, they could be adapted for detection of other benthic AIS that are difficult to observe either due to their small size or other cryptic attributes. The protocols outlined above will greatly assist in physical detection of NZMS following a suspected invasion in Arizona streams and should assist in rapid response and long-term management of this species.

Funding for this project was provided by the Arizona Game and Fish Department through the Federal Aid in Sport Fish Restoration Act administered by the U.S. Fish and Wildlife Service. We would specifically like to thank: C. Wellman, J. McDonald, A. Martinez, K. Dukette E. Morckel, J. Sorenson, E. Rubin, J. Jones, G. Daniell, J. Cordova, K. Marshall, as well as other AZGFD staff for project support and/or field assistance. Lastly, we also would like to thank S. Hedden (AZGFD) and A. Cameron (AZGFD) for assistance in our modelling approaches and two anonymous reviewers for providing valuable comments and suggestions that greatly improved this manuscript.