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USGS2017

Characterizing sources of uncertainty from global climate models and downscaling techniques

In recent years climate model experiments have been increasingly oriented towards providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here we present a method, based on a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. We apply the method to the Southeast U.S. using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios are typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast U.S. for precipitation and ~30% for extreme heat days (> 35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a sub-sample of all models are available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. We conclude with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered.

Journal of Applied Meteorology and Climatology↗

USGS2019

Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using Random Forest classifier on Google Earth Engine

Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide agricultural statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district) within and across nations, which in turn can be used to estimate agricultural productivity as well as degree of disturbance on food security from natural disasters and political conflict. However, existing cropland extent maps over large areas (e.g., Country, region, continent, world) are derived from coarse resolution imagery (250 m to 1 km pixels) and have many limitations such as missing fragmented and\or small farms with mixed signatures from different crop types and\or farming practices that can be, confused with other land cover. As a result, the coarse resolution maps have limited useflness in areas where fields are small (<1 ha), such as in Southeast Asia. Furthermore, coarse resolution cropland maps have known uncertainties in both geo-precision of cropland location as well as accuracies of the product. To overcome these limitations, this research was conducted using multi-date, multi-year 30-m Landsat time-series data for 3 years chosen from 2013 to 2016 for all Southeast and Northeast Asian Countries (SNACs), which included 7 refined agro-ecological zones (RAEZ) and 12 countries (Indonesia, Thailand, Myanmar, Vietnam, Malaysia, Philippines, Cambodia, Japan, North Korea, Laos, South Korea, and Brunei). The 30-m (1 pixel = 0.09 ha) data from Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper (ETM+) were used in the study. Ten Landsat bands were used in the analysis (blue, green, red, NIR, SWIR1, SWIR2, Thermal, NDVI, NDWI, LSWI) along with additional layers of standard deviation of these 10 bands across 1 year, and global digital elevation model (GDEM)-derived slope and elevation bands. To reduce the impact of clouds, the Landsat imagery was time-composited over four time-periods (Period 1: January- April, Period 2: May-August, and Period 3: September-December) over 3-years. Period 4 was the standard deviation of all 10 bands taken over all images acquired during the 2015 calendar year. These four period composites, totaling 42 band data-cube, were generated for each of the 7 RAEZs. The reference training data (N = 7849) generated for the 7 RAEZ using sub-meter to 5-m very high spatial resolution imagery (VHRI) helped generate the knowledge-base to separate croplands from non-croplands. This knowledge-base was used to code and run a pixel-based random forest (RF) supervised machine learning algorithm on the Google Earth Engine (GEE) cloud computing environment to separate croplands from non-croplands. The resulting cropland extent products were evaluated using an independent reference validation dataset (N = 1750) in each of the 7 RAEZs as well as for the entire SNAC area. For the entire SNAC area, the overall accuracy was 88.1% with a producer’s accuracy of 81.6% (errors of omissions = 18.4%) and user’s accuracy of 76.7% (errors of commissions = 23.3%). For each of the 7 RAEZs overall accuracies varied from 83.2 to 96.4%. Cropland areas calculated for the 12 countries were compared with country areas reported by the United Nations Food and Agriculture Organization and other national cropland statistics resulting in an R 2 value of 0.93. The cropland areas of provinces were compared with the province statistics that showed an R 2 = 0.95 for South Korea and R 2 = 0.94 for Thailand. The cropland products are made available on an interactive viewer at www.croplands.org and for download at National Aeronautics and Space Administration’s (NASA) Land Processes Distributed Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/node/1281 .

International Journal of Applied Earth Observation↗

USGS2024

Propensity score matching mitigates risk of faulty inferences in observational studies of effectiveness of restoration trials

Determining effectiveness of restoration treatments is an important requirement of adaptive management, but it can be non-trivial where only portions of large and heterogeneous landscapes of concern can be treated and sampled. Bias and non-randomness in the spatial deployment of treatment and thus sampling is nearly unavoidable in the data available for large-scale management trials, and the biophysical landscape characteristics underlying the bias are key but rare considerations in analyses of treatment effects. Treatment effects from large-scale management trials are typically estimated with multivariable regression (MVR) models. However, this method is unsuited to reliable estimations of treatment effects when treated and untreated areas differ in their underlying biophysical variability. An alternative to conventional regression is to use propensity score (PS) matching, which can limit the differences in confounding variables among treatment groups and assure the data collected or selected for analysis are more consistent with a randomized and unconfounded experiment. Thus, PS is expected to identify treatment effects more accurately. We used data from a large-scale monitoring effort of a megafire to evaluate the efficacy of PS matching in making inferences on treatment effects when treatments are applied non-randomly over a large heterogeneous area. We compared the resulting inference to both traditional MVR methods and to “naïve” methods that do not consider treatment allocation bias. Treatment effects varied between the different statistical methods for controlling selection bias and confounding biophysical factors. The PS-matched model revealed a weaker treatment effect of drill seeding and a greater effect of herbicide spraying on the cover of perennial bunchgrasses when compared to MVR or naïve modelled estimates. The inferences from the PS-matched model are considered more reliable because the treated and untreated plots are more similar in their underlying biophysical characteristics. Synthesis and applications . Failure to consider the non-random and selective deployment of restoration treatments by managers leads to faulty inference on their effectiveness. However, tools such as propensity-score matching can be used to remove the bias from analyses of the outcomes of management trials or to devise sampling plans that efficiently protect against the bias.

Journal of Applied Ecology↗

USGS2014

Identifying variably saturated water-flow patterns in a steep hillslope under intermittent heavy rainfall

The objective of this paper is to identify water-flow patterns in part of an active landslide, through the use of numerical simulations and data obtained during a field study. The approaches adopted include measuring rainfall events and pore-pressure responses in both saturated and unsaturated soils at the site. To account for soil variability, the Richards equation is solved within deterministic and stochastic frameworks. The deterministic simulations considered average water-retention data, adjusted retention data to account for stones or cobbles, retention functions for a heterogeneous pore structure, and continuous retention functions for preferential flow. The stochastic simulations applied the Monte Carlo approach which considers statistical distribution and autocorrelation of the saturated conductivity and its cross correlation with the retention function. Although none of the models is capable of accurately predicting field measurements, appreciable improvement in accuracy was attained using stochastic, preferential flow, and heterogeneous pore-structure models. For the current study, continuum-flow models provide reasonable accuracy for practical purposes, although they are expected to be less accurate than multi-domain preferential flow models.

Hydrogeology Journal↗

USGS1994

Statistical Techniques for Assessing water‐quality effects of BMPs

Little has been published on the effectiveness of various management practices in small rural lakes and streams at the watershed scale. In this study, statistical techniques were used to test for changes in water‐quality data from watersheds where best management practices (BMPs) were implemented. Reductions in data variability due to climate and seasonality were accomplished through the use of regression methods. This study discusses the merits of using storm‐mass‐transport data as a means of improving the ability to detect BMP effects on stream‐water quality. Statistical techniques were applied to suspended‐sediment records from three rural watersheds in Illinois for the period 1981–84. None of the techniques identified changes in suspended sediment, primarily because of the small degree of BMP implementation and because of potential errors introduced through the estimation of storm‐mass transport. A Monte Carlo sensitivity analysis was used to determine the level of discrete change that could be detected for each watershed. In all cases, the use of regressions improved the ability to detect trends. Read More: http://ascelibrary.org/doi/abs/10.1061/(ASCE)0733-9437(1994)120:2(334)

Journal of Irrigation and Drainage Engineering↗

USGS2016

A goodness-of-fit test for occupancy models with correlated within-season revisits

Occupancy modeling is important for exploring species distribution patterns and for conservation monitoring. Within this framework, explicit attention is given to species detection probabilities estimated from replicate surveys to sample units. A central assumption is that replicate surveys are independent Bernoulli trials, but this assumption becomes untenable when ecologists serially deploy remote cameras and acoustic recording devices over days and weeks to survey rare and elusive animals. Proposed solutions involve modifying the detection-level component of the model (e.g., first-order Markov covariate). Evaluating whether a model sufficiently accounts for correlation is imperative, but clear guidance for practitioners is lacking. Currently, an omnibus goodnessof- fit test using a chi-square discrepancy measure on unique detection histories is available for occupancy models (MacKenzie and Bailey, Journal of Agricultural, Biological, and Environmental Statistics, 9, 2004, 300; hereafter, MacKenzie– Bailey test). We propose a join count summary measure adapted from spatial statistics to directly assess correlation after fitting a model. We motivate our work with a dataset of multinight bat call recordings from a pilot study for the North American Bat Monitoring Program. We found in simulations that our join count test was more reliable than the MacKenzie–Bailey test for detecting inadequacy of a model that assumed independence, particularly when serial correlation was low to moderate. A model that included a Markov-structured detection-level covariate produced unbiased occupancy estimates except in the presence of strong serial correlation and a revisit design consisting only of temporal replicates. When applied to two common bat species, our approach illustrates that sophisticated models do not guarantee adequate fit to real data, underscoring the importance of model assessment. Our join count test provides a widely applicable goodness-of-fit test and specifically evaluates occupancy model lack of fit related to correlation among detections within a sample unit. Our diagnostic tool is available for practitioners that serially deploy survey equipment as a way to achieve cost savings.

Ecology and Evolution↗

USGS2007

Lithology-derived structure classification from the joint interpretation of magnetotelluric and seismic models

Magnetotelluric and seismic methods provide complementary information about the resistivity and velocity structure of the subsurface on similar scales and resolutions. No global relation, however, exists between these parameters, and correlations are often valid for only a limited target area. Independently derived inverse models from these methods can be combined using a classification approach to map geologic structure. The method employed is based solely on the statistical correlation of physical properties in a joint parameter space and is independent of theoretical or empirical relations linking electrical and seismic parameters. Regions of high correlation (classes) between resistivity and velocity can in turn be mapped back and re-examined in depth section. The spatial distribution of these classes, and the boundaries between them, provide structural information not evident in the individual models. This method is applied to a 10 km long profile crossing the Dead Sea Transform in Jordan. Several prominent classes are identified with specific lithologies in accordance with local geology. An abrupt change in lithology across the fault, together with vertical uplift of the basement suggest the fault is sub-vertical within the upper crust. ?? 2007 The Authors Journal compilation ?? 2007 RAS.

Geophysical Journal International↗

USGS2010

Effects of precommercial thinning on snowshoe hares in Maine

Snowshoe hares (Lepus americanus) are an important prey species and a dominant herbivore across much of their North American range, and researchers have questioned the influences of forestry practices that alter habitat for hares and the potential community-level effects on carnivores. We examined the effects of precommercial thinning (PCT) from 1 to 11 years posttreatment on snowshoe hares. In the commercial forests of northern Maine, USA, we counted and cleared hare pellets twice a year during 2001 and 2002 on >46 km of pellet transects across 30 regenerating conifer stands (17 treated with PCT) previously treated with an aerial application of herbicide. We compared densities of snowshoe hare pellets among 3 development classes with (1 yr after thinning, 6 yr after thinning, and 11 yr after thinning) and without thinning (stands with a similar history of clearcut and herbicide treatment but no thinning). During both years, densities of hares were lower in stands treated with PCT than in similar unthinned stands across the 3 development classes and during both leaf-off and leaf-on seasons (P < 0.001). Within both thinned and unthinned stands, hare density was greatest in stands in the 1-year development class when compared to the 6-year and 11-year development classes, but a statistical difference (P = 0.048) among classes was evident only during leaf-off seasons. Precommercial thinning was associated with densities of snowshoe hares that were approximately half of those in similar unthinned stands up to at least 11 years posttreatment; however, thinned stands may retain densities of hares greater than stands managed using other forest harvesting regimes. Our results apply to core portions of stands with crop trees spaced at 1.8-2.4-m intervals following complete overstory removal and herbicide treatment. We advocate caution when applying our results to other thinning regimes or across broader spatial scales.

Journal of Wildlife Management↗

USGS2018

A database of natural monthly streamflow estimates from 1950 to 2015 for the conterminous United States

Quantifying and understanding the natural streamflow regime, defined as expected streamflow that would occur in the absence of anthropogenic modification to the hydrologic system, is critically important for the development of management strategies aimed at protecting aquatic ecosystems. Water balance models have been applied frequently to estimate natural flows, but are limited in the number of predictor variables that can be included. Here, a statistical machine learning technique — random forest modeling — was applied to estimate natural flows at a monthly time‐step from 1950 to 2015 for >2.5 million stream reaches in the conterminous United States (U.S.) using 200 potential predictor variables. We describe the development and documentation of this dataset and assess model performance. Model fit statistics (mean Nash–Sutcliffe efficiency = 0.85; observed/expected ratio = 0.94) indicate good correspondence between predicted and observed flows at nearly 2,000 streamgages. As an example application of the dataset, the observed streamflow record at a site prior to and after the construction of an upstream reservoir was compared with estimated natural flows to demonstrate the magnitude of seasonal depletions in streamflow due to the reservoir. This dataset can be applied to quantify natural and anthropogenic processes contributing to streamflow depletion or augmentation, and assess associated ecological effects.

Journal of the American Water Resources Associatio↗

USGS2001

U.S. Geological Survey programs and investigations related to soil and water conservation

The U.S. Geological Survey has a rich tradition of collecting hydrologic data, especially for fluxes of water and suspended sediment, that provide a foundation for studies of soil and water conservation. Applied and basic research has included investigations of the effects of land use on rangelands, croplands, and forests; hazards mapping; derivation of flood and drought frequency, and other statistics related to streamflow and reservoir storage; development and application of models of rainfall-runoff relations, chemical quality, and sediment movement; and studies of the interactive processes of overland and channel flow with vegetation. Networks of streamgaging stations and (or) sampling sites within numerous drainage basins are yielding information that extends databases and enhances the ability to use those data for interpretive studies.

International Journal of Sediment Research↗

USGS2022

Quantifying the relationship between prey density, livestock and illegal killing of leopards

Many large mammalian carnivores are facing population declines due to illegal killing (e.g., shooting) and habitat modification (e.g., livestock farming). Illegal killing occurs cryptically and hence is difficult to detect. However, reducing illegal killing requires a solid understanding of its magnitude and underlying drivers, while accounting for the imperfect detection of illegal killing events. Despite the importance of illegal killing of large carnivores in comparison with other causes of mortality, its relationship with potential drivers such as livestock density and wild prey abundance is rarely described. Using ranger-collected data (2007-2019) of leopard killing events and data on covariates (livestock density, wild prey abundance, road length, protected area size, elevation) across Iran, we applied a single-visit N-mixture model to jointly model variation in detection probability and expected annualized number of leopard killing events. Over the study period, we estimated 428 leopard mortalities (95% CI 184–1014), which was 45% larger than the observed number. Expected intensity of leopard killing was positively related to protected area size, livestock density and wild prey abundance. Detection of leopard killing was higher in areas with more developed road networks. Synthesis and applications: Ranger based monitoring data on poaching of carnivores are cost effective, but traditional analysis does not take into account imperfect detection. We show that innovative statistics (single-visit N-mixture modeling) can reliably quantify poaching events and address their drivers, at large geographical scales. We used the example of the Persian leopard across Iran, but our approach is also applicable to understand killing dynamics of other species. Results suggest that a high frequency of leopard killing is likely to occur in areas with > 100 livestock per km 2 and > 450 individuals of wild prey per km 2 . This highlights the need for improved management of livestock grazing and effective measures around high-risk protected areas to mitigate human-leopard conflict and reduce killing of leopards.

Journal of Applied Ecology↗

USGS2025

An approach to modeling abundance of marine wildlife over space and time using unstructured aerial surveys

Estimating spatial and temporal patterns in abundance is often a goal of ecological studies and can be useful for informing management decisions, such as determining the optimal placement of wildlife protection zones. However, estimating abundance can be difficult in practice, especially over large areas, because of imperfect detection, where individuals are present but not detected because of either availability or observer error. Several methods for estimating abundance that account for imperfect detection exist but can be logistically challenging to implement. We present a simpler approach to some of the more commonly used techniques for estimating the abundance of marine wildlife over space and time from unstructured aerial surveys. This approach combines a spatial model for count data with auxiliary information on detection probability obtained from small-scale or previous studies. We employ generalized linear models and generalized additive models with spatial habitat covariates to illustrate this approach using maximum-likelihood with free, open-source statistical software. This framework is intended to be accessible and flexible, requiring lower survey costs and less computation time than other alternatives for estimating abundance. Indeed, our simulation results show that this approach can reduce computation times, while appropriately characterizing uncertainty, compared to a Bayesian approach. We also present R code for our approach using an example of estimating Florida manatee ( Trichechus manatus latirostris ) abundance in Indian River County in Florida, USA. This approach could be applied to other study systems and marine wildlife species using unstructured aerial surveys.

Florida↗

USGS2011

Assessing first-order emulator inference for physical parameters in nonlinear mechanistic models

We present an approach for estimating physical parameters in nonlinear models that relies on an approximation to the mechanistic model itself for computational efficiency. The proposed methodology is validated and applied in two different modeling scenarios: (a) Simulation and (b) lower trophic level ocean ecosystem model. The approach we develop relies on the ability to predict right singular vectors (resulting from a decomposition of computer model experimental output) based on the computer model input and an experimental set of parameters. Critically, we model the right singular vectors in terms of the model parameters via a nonlinear statistical model. Specifically, we focus our attention on first-order models of these right singular vectors rather than the second-order (covariance) structure.

Journal of Agricultural, Biological, and Environme↗

USGS2024

Structure and morphology of chars and activated carbons obtained from thermal treatment of coal and biomass origin materials, including their wastes: Results from the ICCP Microscopy of Carbon Materials Working Group

This paper describes the evaluation of petrographic textures in char and activated carbon derived from coal, coal by-products and biomass, formed during carbonization and activation processes. This work represents the results of interlaboratory exercises from 2016 to 2022 of the Microscopy of Carbon Materials Working Group in Commission III of the International Committee for Coal and Organic Petrology. The interlaboratory exercises were run on photomicrograph samples. For textural characterization of carbon materials, the existing American Society for Testing and Materials (ASTM) classification system for metallurgical coke was applied. Morphological differences were evaluated in 29 carbon material types, including 22 char samples, and 7 activated carbon (AC) samples obtained experimentally using conventional direct/indirect and microwave heating technologies. This approach gives an extended view on the identification of microporous carbons, and how a certain heat treatment develops a certain optical texture and structure in a raw material. The requested evaluation of carbon materials was related to their porosity, origin, extent, and characteristics, which are particular to each carbon material type. Because carbon matrices can form a wide range of optical textures during heat treatment it is important to demonstrate which carbon occurrences will have a crucial role in industrial applications dominated by adsorption phenomena. The interlaboratory exercises included 17 participants from 14 laboratories. Four sets of digital black and white and colour photomicrographs were distributed, which in total comprised 184 fields of different types of carbon material. The results were evaluated based on four levels: (i) optical texture (isotropic/anisotropic), (ii) optical type and size (punctiform, mosaic, fiber, ribbon, domain), iii) morphology (porous, non-porous/massive), and (iv) particle origin (precursor type). The statistical method applied to evaluate the results was based on “raw agreement indices”. Comparative analyses of the average values of the level of overall agreement showed homogeneity in the results, the mean value was 89%, with a minimum value of 87% and a maximum value of 91% for those who participated in at least three out of four exercises.

International Journal of Coal Geology↗

USGS2012

Foraging optimally for home ranges

Economic models predict behavior of animals based on the presumption that natural selection has shaped behaviors important to an animal's fitness to maximize benefits over costs. Economic analyses have shown that territories of animals are structured by trade-offs between benefits gained from resources and costs of defending them. Intuitively, home ranges should be similarly structured, but trade-offs are difficult to assess because there are no costs of defense, thus economic models of home-range behavior are rare. We present economic models that predict how home ranges can be efficient with respect to spatially distributed resources, discounted for travel costs, under 2 strategies of optimization, resource maximization and area minimization. We show how constraints such as competitors can influence structure of homes ranges through resource depression, ultimately structuring density of animals within a population and their distribution on a landscape. We present simulations based on these models to show how they can be generally predictive of home-range behavior and the mechanisms that structure the spatial distribution of animals. We also show how contiguous home ranges estimated statistically from location data can be misleading for animals that optimize home ranges on landscapes with patchily distributed resources. We conclude with a summary of how we applied our models to nonterritorial black bears (Ursus americanus) living in the mountains of North Carolina, where we found their home ranges were best predicted by an area-minimization strategy constrained by intraspecific competition within a social hierarchy. Economic models can provide strong inference about home-range behavior and the resources that structure home ranges by offering falsifiable, a priori hypotheses that can be tested with field observations.

Journal of Mammalogy↗

USGS2005

Juvenile rainbow trout production in New York tributaries of Lake Ontario: implications for Atlantic salmon restoration

Three Pacific salmonid species Onchorynchus spp. have replaced the extirpated Atlantic salmon Salmo salar as the main migratory salmonid in the Lake Ontario drainage. One of those species, the nonnative rainbow trout O. mykiss, has become widely distributed within the historical Atlantic salmon habitat, occupying an ecological niche similar to that of juvenile Atlantic salmon. Consequently, both a tributary's carrying capacity for Atlantic salmon and competition from established nonnative species are important when considering the feasibility of Atlantic salmon restoration. Estimation of juvenile rainbow trout production will help evaluate the capacity of tributaries to produce salmonids that occupy similar niches. Geostatistical methods were applied to standardized and efficiency-corrected electrofishing data from three of New York's best salmonid-producing streams to precisely estimate juvenile rainbow trout populations. Results indicated that each study stream could produce 20,000–40,000 age-0 and 4,000–10,000 age-1 and older rainbow trout per year. Statistical interpolation indicated areas of significantly different production potential and points of significant changes in productivity. Closer examination of the niche similarity and competitive potential of these two species is needed to properly interpret these estimates with regard to Atlantic salmon restoration.

New York↗

USGS2005

Two-stage sequential sampling: A neighborhood-free adaptive sampling procedure

Designing an efficient sampling scheme for a rare and clustered population is a challenging area of research. Adaptive cluster sampling, which has been shown to be viable for such a population, is based on sampling a neighborhood of units around a unit that meets a specified condition. However, the edge units produced by sampling neighborhoods have proven to limit the efficiency and applicability of adaptive cluster sampling. We propose a sampling design that is adaptive in the sense that the final sample depends on observed values, but it avoids the use of neighborhoods and the sampling of edge units. Unbiased estimators of population total and its variance are derived using Murthy's estimator. The modified two-stage sampling design is easy to implement and can be applied to a wider range of populations than adaptive cluster sampling. We evaluate the proposed sampling design by simulating sampling of two real biological populations and an artificial population for which the variable of interest took the value either 0 or 1 (e.g., indicating presence and absence of a rare event). We show that the proposed sampling design is more efficient than conventional sampling in nearly all cases. The approach used to derive estimators (Murthy's estimator) opens the door for unbiased estimators to be found for similar sequential sampling designs. ?? 2005 American Statistical Association and the International Biometric Society.

Journal of Agricultural, Biological, and Environme↗

USGS2014

Mechanisms driving recruitment variability in fish: comparisons between the Laurentian Great Lakes and marine systems

In his seminal work, Hjort (in Fluctuations in the great fisheries of Northern Europe. Conseil Parmanent International Pour L'Exploration De La Mar. Rapports et Proces-Verbaux, 20: 1–228, 1914) observed that fish population levels fluctuated widely, year-class strength was set early in life, and egg production by adults could not alone explain variability in year-class strength. These observations laid the foundation for hypotheses on mechanisms driving recruitment variability in marine systems. More recently, researchers have sought to explain year-class strength of important fish in the Laurentian Great Lakes and some of the hypotheses developed for marine fisheries have been transferred to Great Lakes fish. We conducted a literature review to determine the applicability of marine recruitment hypotheses to Great Lakes fish. We found that temperature, interspecific interactions, and spawner effects (abundance, age, and condition of adults) were the most important factors in explaining recruitment variability in Great Lakes fish, whereas relatively fewer studies identified bottom-up trophodynamic factors or hydrodynamic factors as important. Next, we compared recruitment between Great Lakes and Baltic Sea fish populations and found no statistical difference in factors driving recruitment between the two systems, indicating that recruitment hypotheses may often be transferable between Great Lakes and marine systems. Many recruitment hypotheses developed for marine fish have yet to be applied to Great Lakes fish. We suggest that future research on recruitment in the Great Lakes should focus on forecasting the effects of climate change and invasive species. Further, because the Great Lakes are smaller and more enclosed than marine systems, and have abundant fishery-independent data, they are excellent candidates for future hypothesis testing on recruitment in fish.

Laurentian Great Lakes↗