Random versus targeted sampling for monitoring occurrence

Random versus targeted sampling for monitoring occurrence, community composition, and relative abundance of fishes in near-shore habitats of Lake Michigan

Report Number: 2017-019

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Smith, B. J., D. G. Simpkins, and T. R. Strakosh 2017. Random versus targeted sampling for monitoring occurrence, community composition, and relative abundance of fishes in near-shore habitats of Lake Michigan. Report #2017-019, USFWS-Green Bay Fish and Wildlife Conservation Office, New Franken, WI.

April 2017

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Contents Summary ....................................................................................................................................................... 1 Introduction ................................................................................................................................................... 2 Methods ........................................................................................................................................................ 3 Study area ................................................................................................................................................. 3 Sampling gears.......................................................................................................................................... 3 Sampling design ........................................................................................................................................ 5 Statistical analysis .................................................................................................................................... 5 Results ........................................................................................................................................................... 6 Discussion ..................................................................................................................................................... 7 Acknowledgements ....................................................................................................................................... 9 References ..................................................................................................................................................... 9 Tables .......................................................................................................................................................... 12 Figures ........................................................................................................................................................ 13

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Summary Choosing an appropriate sampling design is often among the first and most difficult decisions to make when designing a monitoring program. Some form of randomization is typically advised when collecting field data to ensure a representative sample. However, for detection of rare or spatially concentrated species, randomization may be inefficient and non-random, or targeted, sampling may be preferable. Here we demonstrate that choice of random or targeted sampling design has little to no influence on estimates of species richness, diversity, evenness or abundance for five gears used in the nearshore zone of Lake Michigan. Of the five gears tested, only boat electrofishing demonstrated consistently higher estimates of species richness, diversity, and evenness at sites targeted by biologists. For paired mini-fyke nets, paired fyke nets, micromesh gillnets, and large-mesh gillnets, biologists were unable to adequately target sites that yielded indices higher than those produced by a randomized sample. Of the five gears tested boat electrofishing requires the most skill and subjectivity, perhaps explaining the greater effectiveness of targeted sampling using this gear. Our results demonstrate that for most commonly used gears randomization yields results similar to, and more defensible than, targeted sampling. The tradeoff of using randomized sites is reduced flexibility in sampling, a problem when trying to survey rare species or heterogeneous habitats. We suggest that if targeted sampling is to be used, it must provide demonstrably higher target metrics than random sampling to be justifiable.

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Introduction Choice of sampling design is among the most challenging and debated aspects of developing fisheries research and monitoring programs. Sample design is driven primarily by study objectives and practical considerations. If the objective of a sampling program is to monitor the status of a population through time within a system, then a fixed sampling design is often justified (Smith et al. 2016). However, if the objective is to collect a spatially representative sample, then a randomized design is often implemented. Targeted sampling can be used in specific situations where biologists know when, where, and how to collect required fisheries data, but is more vulnerable to subjective bias in choosing sampling sites (Rozas and Minello 1997). Despite abundant literature discussing varying sampling designs there remains intense debate about when, where, and how to deploy sampling gears in a scientifically defensible manner (Krebs 1999). Trade-offs between statistical defensibility and practical considerations are central to the debate over sampling design. Sampling designs that incorporate randomization (i.e., probabilistic) are typically considered more statistically defensible than non-random (i.e., nonprobabilistic) sampling because all sites within a study area have an equal chance of being visited and the resulting sample is more representative (Krebs 1999). Most statistical tests assume the independence of samples; an assumption often violated using non-random sampling (Green 1979). However, there are biologically relevant reasons to perform non-random sampling. Often, fish and other aquatic organisms are concentrated in a few areas or habitats and scarce elsewhere (Grossman and Freeman 1987; Hansen et al. 2007; Croft and Chow-Fraser 2009). Targeted sampling is an example of a non-probabilistic method commonly used when the goal is to sample specific habitats suspected to provide more suitable conditions for target species than the surrounding environment. In particular, biologists often have a priori knowledge of specific fish communities or habitats that can inform when and where to sample in order to maximize sampling efficiency (Hubbard and Miranda 1986). Freshwater fishes are heterogeneously distributed in their environments making targeted sampling appealing for finding rare/invasive species (Minchin 2007; Trebitz et al. 2009).

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The Aquatic Invasive Species (AIS) program at the Green Bay Fish and Wildlife Conservation Office of the U.S. Fish and Wildlife Service is tasked with monitoring for new invasive species in Lake Michigan and its tributaries. Current sampling efforts focus on locations suspected to be vulnerable to invasion by new invasive species. Our goal is to characterize fish communities with an emphasis on detection of new invasive species that occur at low abundance during initial invasion stages (Vander Zanden et al. 2010). Historically, we used targeted sampling with a suite of traditional fish sampling gears at each location. We suspected that targeted sampling for some gears, particularly electrofishing, would improve our ability to characterize fish communities and detect invasive species but would be less effective for other gears. The objective of this study was to compare abundance, species richness, diversity and evenness of five gears used to monitor for invasive species using targeted and random sampling designs in high-risk areas for new invasive species. To justify targeted sampling we needed to observe higher estimates of measured population metrics than those produced by sampling randomized sites.

Methods Study area Our three study locations in Lake Michigan included lower Green Bay, Milwaukee Harbor, and Burns Harbor (Figure 1). Lower Green Bay, hereafter referred to as Green Bay, is the southernmost portion of the embayment of Lake Michigan. This area contains the mouth of the Fox River, and is correspondingly shallow, eutrophic, and highly productive. Milwaukee Harbor includes the confluence of three rivers (i.e., Milwaukee, Kinnikinic, and Menomonee rivers), the harbor, and immediate outer harbor area in downtown Milwaukee. Burns Harbor is on the south shore of Lake Michigan and is characterized by a large industrial port, the PortageBurns waterway, and adjacent protected sand dune habitats. Sampling gears The five gears we used were boat electrofishing (nighttime), paired fyke nets, paired mini-fyke nets, large-mesh gillnets, and micromesh experimental gillnets. Electrofishing was conducted during nighttime hours (i.e., 30 min after sunset to 30 min before sunrise) with either 3

a 6.1 m or 5.5 m long Kann 2-boom electrofishing boat equipped with an Infinity shock box (Midwest Lake Electrofishing Systems). Settings for pulsed DC electrofishing were standardized with duty cycle set at 30%, pulse rate at 60 pulses per second, 118-325 volts, 1037.1 amps, and power of approximately 5,000-5,500 watts. Units of effort for electrofishing were approximately 10 min of pedal time per shocking run. Electrofishing was performed with two netters in littoral areas ( 0.50 is a large effect. Micromesh and large-mesh gillnet data was only collected in Green Bay during September and there were no random effects to incorporate so t-tests were used to compare sampling designs for all response variables. Cohen’s d was used to calculate effect 5

sizes of significant differences for t-tests and interpretation followed Cohen (1988) whereby scores of 0.2-0.49 were small effects, 0.5-0.79 were medium effects, and > 0.8 were large effects. Species accumulation curves were calculated for each sampling design and gear combination across locations to compare efficiency in characterizing fish communities. Data were subjected to 10,000 unique permutations and 95% confidence intervals were computed. Plots of mean (± SE) values of response variables for each sample design were constructed to help visualize differences between gears and sample designs. All calculations were performed in R 3.1.2 (R Foundation for Statistical Computing, 2014).

Results Targeted and random sampling designs produced similar estimates of population metrics for all gears except boat electrofishing (Table 1). Targeted electrofishing was the only gear that yielded significantly higher values of species richness (Χ² = 11.227, df = 1, P < 0.001), diversity (Χ² = 15.996, df = 1, P < 0.001), and evenness (Χ² = 7.782, df = 1, P = 0.005) but abundances were similar to samples collected at random points (Χ² = 1.250, df = 1, P = 0.264). Large effect sizes were observed for species richness (Φ = 0.602; 0.281-0.802), diversity (Φ = 0.718; 0.4310.873), and evenness (Φ = 0.501; 0.162-0.938) between targeted and random sample designs for electrofishing. Bar plots of species richness, abundance, Shannon’s diversity, and Shannon’s evenness by gear type and sample design further corroborate ratio hypothesis tests by showing nearly identical values of measured response variables for paired fyke nets, large-mesh gillnets, micromesh gillnets, and paired mini-fyke nets regardless of sample design (Figure 2). Not all gears were equally effective. Of the gear-sample design combinations used, targeted electrofishing yielded the highest estimates of species richness, diversity, and evenness (Figure 2). Mini-fyke nets yielded the highest abundance, regardless of sample design. Largemesh gill nets were the least effective sampling gear used because they collected the fewest species, fewest individuals, and lowest diversity of any gear. Micromesh gillnets and paired fyke nets were nearly equally effective, though these comparisons do not account for differences in species composition, which differed between gears (Figure 2).

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Species accumulation curves showed similar patterns to those observed in bar-plots but added management value by demonstrating the amount of effort required to adequately sample using each gear (Figure 3). The most notable differences were for night electrofishing where targeted sampling produced a higher asymptote and greater rate of species accumulation than random sampling. Results for large-mesh gillnets differed as well, but these differences were likely due to low sample size and low species richness for this gear. Species accumulation curves for targeted and random paired mini-fyke nets, paired fyke nets, and micromesh gillnets show near complete overlap.

Discussion In all instances except boat electrofishing there was no measurable difference between targeted and random sampling designs. There are several possible explanations for this observation. For some gears this may be a result of low sample size. Large-mesh gill nets had only eleven replicates for each sample design making detection of significant differences difficult, as demonstrated by Isermann (2003) and Smith et al. (2016). For some gears, particularly gillnets, targeting sites is difficult because the benthic environment can be obscured to the biologist deploying the gear and often the only information available to help pick sites is bathymetry. Making this task more difficult is the homogenous benthic habitats found at many sample sites, namely sand or silt bottom with limited features to target. For gears where finding targeted sites is difficult, randomized sampling is preferable. Targeted sampling appears most efficient for gears where habitat features can be easily observed. Targeted electrofishing more efficiently encountered species than random sampling, indicating that biologists were able to consistently identify diversity hotspots within each study area. Electrofishing was hypothesized to be more suited to targeted sampling because shoreline habitat features (e.g., piers, large woody debris, and weed beds) are easily observed from the water allowing them to be targeted. Randomly generated electrofishing points frequently fell far from ideal habitats (e.g., macrophyte beds, complex shoreline) resulting in poor species accumulation. Electrofishing was the most subjective gear used and relies on the skill of the driver to identify spots to electrofish and ability of netters to capture stunned fish. For the 7

remaining four gears there was no difference between random and targeted sampling designs likely due to difficulty in identifying good sites to target within our study areas. For gears where no difference was detected between sampling designs, there are two ways to interpret the results. One interpretation is that if targeted sampling was equally effective as randomized sampling, and because targeted sampling allows more flexibility in choosing sites, then targeted sampling is preferred. The alternative interpretation is that randomized sampling is more statistically defensible, especially when extrapolating estimates of diversity over a defined spatial area. Therefore, if targeted sampling is not demonstrably more efficient than randomized sampling, then randomized sampling is preferred. If a spatially representative sample is desired then randomized sampling should be adopted (Krebs 1999). In a similar study Smith et al. (2016) found no difference in measured population metrics between fixed and random sampling designs in small glacial lakes and suggested that a fixed sample design was more appropriate for monitoring game fish populations. A potential criticism of using targeted sampling for detection of new potential AIS is that biologists, unfamiliar with habitat preferences of new species, may be unable to adequately target hotspot areas for sampling. New invaders may occupy habitats that differ from those of the established fish community – species that are effectively targeted using current gears and sample designs. Though this possibility exists, our sample design incorporates multiple complementary gears and sample designs that are spatially segregated within sample locations to encounter as many habitats as possible and over a large spatial scale. The intensive nature of our sampling protocol provides the best chance of encountering new AIS and targeted sampling can be incorporated. Sample designs are driven by study questions (Hansen et al. 2007); therefore, we recommend that sampling efforts should be flexible by accommodating elements of both random and targeted sampling for AIS depending on gear type. Current monitoring strategies for Asian Carps follow these methodologies (Asian Carp Regional Coordinating Committee 2016). Targeted sampling should be used for electrofishing assuming that effort is spread throughout the study area. One way to ensure thorough coverage using targeted sampling is to divide a study area into zones that all need to be sampled, but allow targeted sampling within each zone. Some form of randomized or systematic sampling should be used for benthic sampling gears. Tailoring appropriate sample designs to gears will make AIS monitoring efforts more effective 8

by maximizing species richness and diversity in samples making it more likely that new AIS will be detected.

Acknowledgements This project was made possible by the hard work and dedication of field personnel from the Aquatic Invasive Species Early Detection and Monitoring Program at the Green Bay Fish and Wildlife Conservation Office. Funding for this project was provided in part by the Great Lakes Restoration Initiative. The findings and conclusions in this article are those of the author(s) and do not necessarily represent the views of the U.S. Fish and Wildlife Service.

References Asian Carp Regional Coordinating Committee. 2016. Asian Carp Action Plan for Fiscal Year 2017. 260 pp.

Cohen, J. 1988. Statistical power analysis for the behavioral sciences, second ed. Hillsdale, NJ; Lawrence Erlbaum.

Croft, M. V. and P. Chow-Fraser. 2009. Non-random sampling and its role in habitat conservation: a comparison of three wetland macrophyte sampling protocols. Biodiversity Conservation 18:2283-2306.

Green, R. H. 1979. Sampling design and statistical methods for environmental biologists. John Wiley & Sons.

Grossman, G. D., and M. C. Freeman. 1987. Microhabitat use in a stream fish assemblage. Journal of Zoology 212(1):151-176. 9

Hansen, M. J., T. D. Beard, and D. B. Hayes. 2007. Sampling and experimental design. Pages 51-120 in C. S. Guy and M. L. Brown, editors. Analysis and interpretation of freshwater fisheries data. American Fisheries Society, Bethesda, MD.

Hubbard, W. D., and L. E. Miranda. 1986. Competence of non-random electrofishing sampling in assessment of structural indices. Proceedings of the Annual Conference of the Southeastern Association of Fish and Wildlife Agencies 40:79–84.

Isermann, D. A. 2003. Population dynamics and management of Yellow Perch populations in South Dakota glacial lakes. Dissertation. South Dakota State University, Brookings, South Dakota, USA.

Krebs, C. J. 1999. Ecological methodology, Second edition. Benjamin/Cummings, Menlo Park, California, USA.

Minchin, D. 2007. Rapid coastal survey for targeted alien species associated with floating pontoons in Ireland. Aquatic Invasions 2(1):63-70.

Rozas, L. P. and T. J. Minello. 1997. Estimating densities of small fishes and decapod crustaceans in shallow estuarine habitats: a review of sampling design with focus on gear selection. Estuaries 20:199-213.

Smith, B. J., N. S. Kruger, N. S. Voss, and B. G. Blackwell. 2016. Fixed versus random sampling designs in small South Dakota glacial lakes. The Prairie Naturalist 47:30-39.

Trebitz, A. S., J. R. Kelly, J. C. Hoffman, G. S. Peterson, and C. W. West. 2009. Exploiting habitat and gear patterns for efficient detection of rare and non-native benthos and fish in Great Lakes coastal ecosystems. Aquatic Invasions 4(4):651-667.

Vander Zanden, M. J, G. J. A Hansen, S. N. Higgins, and M. S. Kornis. 2010. A pound of 10

prevention, plus a pound of cure: early detection and eradication of invasive species in the Laurentian Great Lakes. Journal of Great Lakes Research 36(1):199-205.

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Tables Table 1. Results of population metric comparisons between random and targeted sampling designs for five gears used to sample the near-shore zone of Lake Michigan during 2015. Linear mixed effect models were used for most gear types to test the influence of sampling design (fixed effect) while incorporating random factors (i.e., location, electrofishing boat). Chi-square statistics were generated from likelihood ratio tests. Micromesh and large-mesh gillnet data was only collected at one location (i.e., Green Bay) allowing us to use t-tests. Sample sizes are reported for targeted (T) and random (R) samples. Gear Electrofishing

Sample size T R Population metric 17 14 Total abundance Species richness Diversity (Shannon's H') Evenness (Shannon's J)

Test statistic Χ² = 1.250 Χ² = 11.227 Χ² = 15.996 Χ² = 7.782

df 1 1 1 1

P 0.264 < 0.001 < 0.001 0.005

Large-mesh gillnet

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Total abundance Species richness Diversity (Shannon's H') Evenness (Shannon's J)

t = -0.643 t = 0.992 t = 1.402 t = 1.259

24 24 23 23

0.526 0.331 0.174 0.221

Micromesh gill net

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t = -0.539 t = -0.165 t = 0.924 t = 0.777

19 16 20 20

0.596 0.871 0.367 0.446

Paired fyke net

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Χ² = 0.218 Χ² = 0.139 Χ² = 0.210 Χ² = 1.009

1 1 1 1

0.641 0.709 0.646 0.315

Paired mini-fyke net

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Χ² = 0.013 Χ² = 0 Χ² = 0.025 Χ² = 0.002

1 1 1 1

0.911 0.999 0.874 0.966

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Figures

Figure 1. Locations of Green Bay, Milwaukee, and Burns Harbor study areas within Lake Michigan.

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Figure 2. Estimates of mean (± SE) species richness, abundance, and Shannon’s diversity and evenness for random and targeted sampling designs using five standard sampling gears. Sampling was performed in Green Bay, Milwaukee, and Burns Harbors during 2015.

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Figure 3. Species accumulation curves (solid lines) for five gears used to sample the near-shore zone of Lake Michigan with random (red) and targeted (blue) sampling designs during 2015 shown with 95% confidence intervals (dashed lines). Site data was added in a random order and subjected to 10,000 unique permutations.

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