Estimating local stream fish assemblage attributes: sampling effort and efficiency at two spatial scales

As part of a wider study to develop an ecosystem-health monitoring program for wadeable streams of south-eastern Queensland, Australia, comparisons were made regarding the accuracy, precision and relative efficiency of single-pass backpack electrofishing and multiple-pass electrofishing plus supplementary seine netting to quantify fish assemblage attributes at two spatial scales (within discrete mesohabitat units and within stream reaches consisting of multiple mesohabitat units). The results demonstrate that multiple-pass electrofishing plus seine netting provide more accurate and precise estimates of fish species richness, assemblage composition and species relative abundances in comparison to single-pass electrofishing alone, and that intensive sampling of three mesohabitat units (equivalent to a riffle–run–pool sequence) is a more efficient sampling strategy to estimate reach-scale assemblage attributes than less intensive sampling over larger spatial scales. This intensive sampling protocol was sufficiently sensitive that relatively small differences in assemblage attributes (<20%) could be detected with a high statistical power (1-β > 0.95) and that relatively few stream reaches (<4) need be sampled to accurately estimate assemblage attributes close to the true population means. The merits and potential drawbacks of the intensive sampling strategy are discussed, and it is deemed to be suitable for a range of monitoring and bioassessment objectives.

Download Full-text

Optimized Survey Design for Monitoring Protocols: A Case Study of Waterfowl Abundance

Journal of Fish and Wildlife Management ◽

10.3996/jfwm-20-037 ◽

2021 ◽

Author(s):

Alexander V Kumar ◽

Mindy B. Rice

Keyword(s):

Survey Design ◽

Spatial Scales ◽

Financial Burden ◽

Monitoring Program ◽

Census Count ◽

Sampling Effort ◽

Sampling Schemes ◽

National Monitoring ◽

Wildlife Refuges ◽

Monitoring Protocols

Nationwide monitoring programs are important tools that quantify the status and trends of natural resources providing important information for management and conservation decisions. These programs operate at large spatial scales with standardized protocols requiring wide-spread participation. However, resource limitations can reduce participation, which can then compromise the spatial replication needed for nationwide inference. The Integrated Waterbird Management and Monitoring program is an example of a national monitoring program that could benefit from a reduction in sampling effort to facilitate increased participation and ultimately broader inference. Therefore, we examined various sampling schemes to determine if it is possible to reduce the sampling effort while maintaining the statistical accuracy needed to support management. We found that instead of needing to census a National Wildlife Refuge, sampling effort could be reduced while accurately estimating waterfowl abundance to within 10% of the census count by surveying just 2/3 of all the sample units or 3/4 of the total survey area. Not only did this guideline apply to our five pilot National Wildlife Refuges, but it was also further validated by applying it to four additional National Wildlife Refuges. We hope that by applying this finding to other National Wildlife Refuges, we can increase participation in the program by reducing the logistical and financial burden of sampling.

Download Full-text

State-space analysis of power to detect regional brook trout population trends over time

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/cjfas-2018-0241 ◽

2019 ◽

Vol 76 (11) ◽

pp. 2145-2155 ◽

Cited By ~ 2

Author(s):

Kasey C. Pregler ◽

R. Daniel Hanks ◽

Evan S. Childress ◽

Nathaniel P. Hitt ◽

Daniel J. Hocking ◽

...

Keyword(s):

State Space ◽

Brook Trout ◽

Statistical Power ◽

Spatial Scales ◽

Temporal Trends ◽

Monitoring Program ◽

Trend Detection ◽

Data Set ◽

Trout Population ◽

Variable Sampling

Threats to aquatic biodiversity are expressed at broad spatial scales, but identifying regional trends in abundance is challenging owing to variable sampling designs and temporal and spatial variation in abundance. We compiled a regional data set of brook trout (Salvelinus fontinalis) counts across their southern range representing 326 sites from eight states between 1982 and 2014 and conducted a statistical power analysis using Bayesian state-space models to evaluate the ability to detect temporal trends by characterizing posterior distributions with three approaches. A combination of monitoring periods, number of sites and electrofishing passes, decline magnitude, and different revisit patterns were tested. Power increased with monitoring periods and decline magnitude. Trends in adults were better detected than young-of-the-year fish, which showed greater interannual variation in abundance. The addition of weather covariates to account for the temporal variation increased power only slightly. Single- and three-pass electrofishing methods were similar in power. Finally, power was higher for sampling designs with more frequent revisits over the duration of the monitoring program. Our results provide guidance for broad-scale monitoring designs for temporal trend detection.

Download Full-text

Spatial Scan Statistics to Assess Sampling Strategy of Antimicrobial Resistance Monitoring Program

Foodborne Pathogens and Disease ◽

10.1089/fpd.2008.0132 ◽

2009 ◽

Vol 6 (1) ◽

pp. 15-21 ◽

Cited By ~ 4

Author(s):

A.R. Vieira ◽

H. Houe ◽

H.C. Wegener ◽

D.M.A. Lo Fo Wong ◽

R. Bødker ◽

...

Keyword(s):

Antimicrobial Resistance ◽

Monitoring Program ◽

Sampling Strategy ◽

Scan Statistics ◽

Spatial Scan Statistics ◽

Resistance Monitoring ◽

Spatial Scan

Download Full-text

Using confidence intervals to estimate the response of salmon populations (Oncorhynchus spp.) to experimental habitat alterations

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/f05-179 ◽

2005 ◽

Vol 62 (12) ◽

pp. 2716-2726 ◽

Cited By ~ 24

Author(s):

Michael J Bradford ◽

Josh Korman ◽

Paul S Higgins

Keyword(s):

Confidence Intervals ◽

Effect Size ◽

Null Hypothesis ◽

Statistical Power ◽

Habitat Restoration ◽

Coho Salmon ◽

Fish Habitat ◽

Monitoring Program ◽

Considerable Uncertainty ◽

Monitoring Programs

There is considerable uncertainty about the effectiveness of fish habitat restoration programs, and reliable monitoring programs are needed to evaluate them. Statistical power analysis based on traditional hypothesis tests are usually used for monitoring program design, but here we argue that effect size estimates and their associated confidence intervals are more informative because results can be compared with both the null hypothesis of no effect and effect sizes of interest, such as restoration goals. We used a stochastic simulation model to compare alternative monitoring strategies for a habitat alteration that would change the productivity and capacity of a coho salmon (Oncorhynchus kisutch) producing stream. Estimates of the effect size using a freshwater stockrecruit model were more precise than those from monitoring the abundance of either spawners or smolts. Less than ideal monitoring programs can produce ambiguous results, which are cases in which the confidence interval includes both the null hypothesis and the effect size of interest. Our model is a useful planning tool because it allows the evaluation of the utility of different types of monitoring data, which should stimulate discussion on how the results will ultimately inform decision-making.

Download Full-text

Synchrony of frond dynamics among patches of the clonal seaweed Mazzaella parksii (Rhodophyta) at local spatial scale

Journal of the Marine Biological Association of the United Kingdom ◽

10.1017/s0025315404010148h ◽

2004 ◽

Vol 84 (5) ◽

pp. 883-886 ◽

Cited By ~ 1

Author(s):

Ricardo Scrosati

Keyword(s):

Spatial Scale ◽

Seasonal Changes ◽

Pacific Coast ◽

Spatial Scales ◽

Sampling Effort ◽

Confidence Limits ◽

Seasonal Trends ◽

Intertidal Seaweed ◽

The Mean

This study investigated the synchrony of frond dynamics among patches of the intertidal seaweed Mazzaella parksii (=M. cornucopiae; Rhodophyta: Gigartinales) at local spatial scale. At Prasiola Point (Pacific coast of Canada), the mean synchrony of the seasonal changes in frond density among seven permanent, 100-cm2 quadrats was significant (mean Pearson's r=0·73, with 0·65–0·81 as 95% confidence limits) between 1993 and 1995. This indicates that predicting seasonal trends for non-monitored patches at local spatial scale can be done relatively well based on observations on a limited number of quadrats. The identification of the spatial scales at which seaweed populations covary synchronously will permit minimizing sampling effort while retaining the ability to make valid predictions for non-monitored sites.

Download Full-text

Minimum time required to detect population trends: the need for long-term monitoring programs

10.7287/peerj.preprints.3168v2 ◽

2017 ◽

Author(s):

Easton R White

Keyword(s):

Time Series ◽

Statistical Power ◽

Time Series Data ◽

Conservation Status ◽

Monitoring Program ◽

Minimum Time ◽

Series Data ◽

Series Length ◽

Time Required

Long-term time series are necessary to better understand population dynamics, assess species' conservation status, and make management decisions. However, population data are often expensive, requiring a lot of time and resources. When is a population time series long enough to address a question of interest? We determine the minimum time series length required to detect significant increases or decreases in population abundance. To address this question, we use simulation methods and examine 878 populations of vertebrate species. Here we show that 15-20 years of continuous monitoring are required in order to achieve a high level of statistical power. For both simulations and the time series data, the minimum time required depends on trend strength, population variability, and temporal autocorrelation. These results point to the importance of sampling populations over long periods of time. We argue that statistical power needs to be considered in monitoring program design and evaluation. Time series less than 15-20 years are likely underpowered and potentially misleading.

Download Full-text

Transects, quadrats, or points? What is the best combination to get a precise estimation of a coral community?

10.1101/832790 ◽

2019 ◽

Author(s):

Luis M. Montilla ◽

Emy Miyazawa ◽

Alfredo Ascanio ◽

María López-Hernández ◽

Gloria Mariño-Briceño ◽

...

Keyword(s):

Community Structure ◽

Coral Reef ◽

Effect Size ◽

Standard Error ◽

Scleractinian Coral ◽

Statistical Power ◽

Coral Community ◽

Sampling Effort ◽

Precise Estimation

ABSTRACTThe characteristics of coral reef sampling and monitoring are highly variable, with numbers of units and sampling effort varying from one study to another. Numerous works have been carried out to determine an appropriate effect size through statistical power, however, always from a univariate perspective. In this work, we used the pseudo multivariate dissimilarity-based standard error (MultSE) approach to assess the precision of sampling scleractinian coral assemblages in reefs of Venezuela between 2017 and 2018 when using different combinations of number of transects, quadrats and points. For this, the MultSE of 36 sites previously sampled was estimated, using four 30m-transects with 15 photo-quadrats each and 25 random points per quadrat. We obtained that the MultSE was highly variable between sites and is not correlated with the univariate standard error nor with the richness of species. Then, a subset of sites was re-annotated using 100 uniformly distributed points, which allowed the simulation of different numbers of transects per site, quadrats per transect and points per quadrat using resampling techniques. The magnitude of the MultSE stabilized by adding more transects, however, adding more quadrats or points does not improve the estimate. For this case study, the error was reduced by half when using 10 transects, 10 quadrats per transect and 25 points per quadrat. We recommend the use of MultSE in reef monitoring programs, in particular when conducting pilot surveys to optimize the estimation of the community structure.

Download Full-text

Growth rings across the Tree of Life: demographic insights from biogenic time series data

10.1093/oso/9780198838609.003.0004 ◽

2021 ◽

pp. 77-96

Author(s):

Margaret E. K. Evans ◽

Bryan A. Black ◽

Donald A. Falk ◽

Courtney L. Giebink ◽

Emily L. Schultz

Keyword(s):

Time Series ◽

Time Series Data ◽

Spatial Scales ◽

Ocean Dynamics ◽

Individual Growth ◽

Series Data ◽

Rate Variation ◽

Sampling Effort ◽

Growth Rings ◽

Demographic Processes

Biogenic time series data can be generated in a single sampling effort, offering an appealing alternative to the slow process of revisiting or recapturing individuals to measure demographic rates. Annual growth rings formed by trees and in the ear bones of fish (i.e. otoliths) are prime examples of such biogenic time series. They offer insight into not only the process of growth but also birth, death, movement, and evolution, sometimes at uniquely deep temporal and large spatial scales, well beyond 5–30 years of data collected in localised study areas. This chapter first reviews the fundamentals of how tree-ring and otolith time series data are developed and analysed (i.e. dendrochronology and sclerochronology), then surveys growth rings in other organisms, along with microstructural or microcompositional variation in growth rings, and other records of demographic processes. It highlights the answers to demographic questions revealed by these time series data, such as the influence of environmental (atmospheric or ocean) conditions, competition, and disturbances on demographic processes, and the genetic versus plastic basis of individual growth and traits that influence growth. Lastly, it considers how spatial networks of biogenic, annually resolved time series data can offer insights into the importance of macrosystem atmospheric and ocean dynamics on multispecies, trophic dynamics. The authors encourage demographers to integrate the complementary information contained in biogenic time series data into population models to better understand the drivers of vital rate variation and predict the impacts of global change.

Download Full-text