General Overview

Estimating the density and distribution of invasive populations is critical for management and control efforts, but can be a challenge in nascent infestations when densities are low. Statistically valid sampling designs that account for imperfect detection of individuals are needed to estimate densities across time and space reliably. Survey methods that yield reliable estimates allow managers to determine how invader biomass impacts ecosystem services and evaluate population trends and effectiveness of control measures.

Numerous methods have been developed to account for imperfect detection in population surveys using specialized data collection protocols and associated models to obtain unbiased estimates of population parameters (Lancia et al. 2005, Royle and Dorazio 2008, Kéry and Royle 2016). Here, we describe our efforts to implement survey designs that allow us to estimate detection probabilities of zebra mussels in recently infested lakes.

First field season (summer 0f 2017)

We focused our initial efforts on a broad class of methods that utilize distances between observations and observers to estimate and correct for imperfect detection (commonly referred to as “distance sampling”). We also emphasized line-transect sampling designs, which are appealing for several reasons:

  1. divers can quickly survey large contiguous areas (Smith 2008);
  2. methods for estimating and correcting for imperfect detection are well developed (Buckland et al. 2005, 2007);
  3. recent advances in spatial modeling can be used to estimate the distribution of mussels throughout a sampled area (Miller et al. 2013).

During the summer 2017, we explored 2 different distance sampling designs:

  1. Single-observer distance sampling (in Lake Sylvia in Stearns County, MN): our dive team sampled transects once and recorded the distance from the transect whenever they observed a cluster of mussels. This method requires an assumption that all mussels on the transect line are detected.

  2. Independent double-observer distance sampling (in Lake Burgan in Douglas County, MN): each transect was surveyed independently by 2 dive teams (our divers and 2 divers from the MN DNR). This design allowed us to relax the assumption of perfect detection of mussels on the transect line.

Results We estimated that divers detected between 5% and 41% of the mussels present in the surveyed area, depending on the specific diver and on whether the lake bottom was vegetated. We also found that a key assumption of conventional distance sampling (e.g., perfect detection on the transect line) was not met. Therefore, accurate density estimates required a double-observer approach. These results highlight the importance of accounting for detectability when comparing estimates over time or across lakes, particularly when different observers conduct surveys.

In our first field season, the 2 dive teams surveyed the same areas in Lake Burgan independenty. As a result, we had to make assumptions about which mussels were seen by both dive teams (this may not always be easy to infer due to errors in measuring distances along the transect where mussels occured). In our second field season, we decided to implement dependent double-observer surveys, in which the first dive team marked all mussels they saw and the second dive team looked for additional mussels that were not seen by the first dive team. This change helped to simplify the survey and analysis of the resulting data.

Second field season (summer of 2018)

We implemented 3 different survey techniques (dependent double-observer surveys with and without distance sampling, quadrat counts) in three lakes capturing a range of zebra mussel densities: Lake Florida in Kandiyohi County, Lake Burgan in Douglas County, and Little Birch Lake in Todd County.

We compared estimates of detection probabilities and zebra mussel density from data collecting during our second field season using thes 3 different survey methods. We found that estimates of detection probabilities were fairly similar in all three sampled lakes (Lake Burgan, Lake Florida, and Little Birch Lake), and the different survey methods all gave similar estimates of density. The estimated detection probability using double-observer surveys without distance sampling was 0.94, suggesting we may be able to achieve near perfect detection, provided we use 2 observers and survey a smaller width transect. However, we detected a pattern of slightly lower density estimates when using this approach (compared to double observer surveys with distance sampling and quadrat counts). Preliminary comparisons of the 3 survey methods suggest that double-observer surveys with distance sampling may be most efficient at low densities and quadrat or double-observer surveys (without distance data) may be more efficient when densities are high. Initial results are summarized in a poster presentation linked below:

Hawaii Conservation Conference

Literature Cited

Buckland, S.T., D.R. Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers, and L. Thomas. 2007. Advanced distance sampling. Oxford University Press, New York.

Buckland, S. T., D. R. Anderson, K. P. Burnham, and J. L. Laake. 2005. Distance sampling. John Wiley & Sons, New York.

K\(\mbox{e}'\)ry, M. and J. A. Royle. 2016. Applied hierarchical modeling in ecology: Analysis of distribution, abundance and species richness in R and BUGS: Volume 1: Prelude and Static Models. Academic Press, New York.

Lancia, R.A., W. L. Kendall, K.H. Pollock, K.H. and J.D. Nichols. 2005 Estimating the number of animals in wildlife populations. Techniques for Wildlife Investigations and Management, 6th edn (ed. C.E. Braun), pp. 106–133. The Wildlife Society, Bethesda, Maryland,USA.

Miller, D.L., M.L. Burt, E.A. Rexstad, and L. Thomas. 2013. Spatial models for distance sampling: recent developments and future directions. Methods in Ecology and Evolution 4:1001-2010.

Royle, J. A. and R. M. Dorazio. 2008. Hierarchical modeling and inference in ecology. Academic Press, New York.

Smith, D. R. 2007. Survey design for detecting rare freshwater mussels. Journal of the North American Benthological Society 25:701-711.