What is the goal of fish habitat restoration efforts and partnerships? To create more/better fish habitat To enhance/recover/restore fish populations
How do we know if we met our goal? We need to monitor
Adaptive Management DOI Technical Guide Example Models: More Large Woody Debris = More Brook Trout Lower Sediment Load = More Brook Trout
Issues of spatial scale Defining the population of interest Depends on the goal of the habitat restoration workDepends on the goal of the habitat restoration work Reach population vs. stream population vs. range wide populationReach population vs. stream population vs. range wide population Often a mismatch between the population of interest and spatial scale of habitat restoration and monitoring Can lead to erroneous conclusions about the effectiveness of habitat restoration
300 m Brook trout abundance A 0 = 15 A 1 = 30 100% increase !!!!!!
300 m 6000 m If abundance throughout the rest of the steam stays the same, this is only a 5% increase.
Issues of spatial scale Life history information can inform decisions on spatial scale of monitoring Home range – Does monitoring encompass the home range of individuals in the population? Schooling behavior – Is the species of interest patchily distributed? Migration – Does the species of interest migrate from one habitat to another while completing its life cycle?
A good example of monitoring at the population scale Liermann, M. and P. Roni. 2008. More sites or more years? Optimal study design for monitoring fish response to watershed restoration. North American Journal of Fisheries Management 28: 935-943. “…the only way to assess the population level effects of watershed scale restoration is to monitor at the population level.” Monitored salmon smolt migration from small streams with and without habitat restoration. Employed knowledge of life history information (migration) Replicated experiment with controls
Issues of temporal scale How does the duration of monitoring compare to the life history of the species of interest? Generation Time – amount of time it takes one cohort to grow up and replace another; can be calculated from a life table or a Leslie matrix YOYAge1Age2Age3Age4+ YOY0.0037.5056.2597.50150.00 Age10.060.000.000.000.00 Age20.000.100.000.000.00 Age30.000.000.100.000.00 Age4+0.000.000.000.100.01 Population growth rate(λ) = 1.6, generation time = 2.14
Issues of temporal scale If habitat restoration has a population level effect, we would not expect to begin seeing any real change until the expected generation time is reached Likely longer due to variation in other uncontrollable factors (e.g. rainfall, flow, temperature, predation etc.) Length of monitoring can greatly influence conclusions that are drawn
Sweka, J.A. and K.J. Hartman. 2006. Effects of large woody debris addition on stream habitat and brook trout populations in Appalachian streams. Hydrobiologia 559: 363-378.
Types of Monitoring Designs Before-After Design Time Abundance Assumes everything but the treatment remained constant through time Best if there is many years of pre- and post- data Simply compare pre- and post- mean abundance
Types of Monitoring Designs Pre/Post Pairs Time Abundance Allows assessment of site-to-site variation Temporal scale may not be long enough Ignores any regional trends in abundance that may exist
Types of Monitoring Designs Before-After-Control-Impact Design (BACI) Time Abundance Has an independent control site Best if there are several years of pre- and post- data Interested in the difference between treatment and control Embraces natural variation Control site Treatment site
Types of Monitoring Designs Before-After-Control-Impact Design (BACI) Time Abundance Control can have higher or lower abundance than the treatment site Control site Treatment site
Types of Monitoring Designs Before-After-Control-Impact Design (BACI) Time Abundance Can have several treatment and control sites Control sites Treatment sites
Types of Monitoring Designs Real World Case Time Abundance Control site Treatment site Limited funding, 1 year pre-, couple years post- Add a control – separate natural variation from treatment variation Continue monitoring treatment and control – look for treatment x time interaction
Power and Sample Size Power – the probability of correctly rejecting the null hypothesis of no change (no difference) when some specified alternative is correct How much Power do you need? Depends on the consequencesDepends on the consequences Law of diminishing returns – rate of increase in power decreases with increasing sample sizeLaw of diminishing returns – rate of increase in power decreases with increasing sample size Increasing power can be costlyIncreasing power can be costly
Power and Sample Size How many samples should I take to detect a difference? Choice of alpha (chance of falsely rejecting H o )Choice of alpha (chance of falsely rejecting H o ) Whether the test will be one- or two-tailedWhether the test will be one- or two-tailed Value of the alternative H a (desired difference to detect)Value of the alternative H a (desired difference to detect) Choice of designChoice of design Some assumption about the behavior of the variation in the data (e.g variance proportional to mean)Some assumption about the behavior of the variation in the data (e.g variance proportional to mean) Estimate of the variation (standard deveiation or variance)Estimate of the variation (standard deveiation or variance)
Power and Sample Size Guidelines for choosing an effect size (Gerow 2007) Small Effect – the smallest difference that elicits your interest Large Effect – the smallest difference that you would definitely not want to fail to detect Medium Effect - the average of small and large effects
Power and Sample Size Gerow, K. G. 2007. Power and sample size estimation techniques for fisheries management: Assessment and a new computational tool. North American Journal of Fisheries Management 27: 397 – 404. Gerrodette, T. 1987. A power analysis for detecting trends. Ecology 68: 1364-1372.
Conclusions Effective monitoring starts with a clearly defined population of interest and goals Incorporate the life history, home range, and behavior of the target species Have a control and avoid psuedoreplication View monitoring as an experiment (hypothesis testing)View monitoring as an experiment (hypothesis testing) Use power analysis to inform study design Funding timelines don’t match biological timelines Additional partnershipsAdditional partnerships Creative ways to extend fundingCreative ways to extend funding Work with funding sources for changeWork with funding sources for change