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Article

Long-Term Trends in Brook Trout Habitat in Appalachian Headwater Streams †

by
Zac Zacavish
1 and
Kyle Hartman
2,*
1
Mid-Columbia Fisheries, Yakima, WA 98926, USA
2
School of Natural Resources and the Environment, West Virginia University, Morgantown, WV 26506, USA
*
Author to whom correspondence should be addressed.
†
This work is part of the Master of Science thesis of the first author Zac Zacavish. Master of Science in Wildlife and Fisheries at West Virginia University, USA.
Fishes 2025, 10(10), 512; https://doi.org/10.3390/fishes10100512
Submission received: 6 August 2025 / Revised: 23 September 2025 / Accepted: 3 October 2025 / Published: 10 October 2025
(This article belongs to the Special Issue New Conservation Strategies for Freshwater and Marine Fishes: Habitat Is the Key)
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Abstract

For lotic salmonids, pool habitats are critical to persistence and resilience. In the central Appalachians, brook trout (Salvelinus fontinalis Mitchill 1814) is an imperiled species that relies on pool habitats for refuge during drought and for spawning. We sought to study trends in pool habitats by studying habitat distribution and trends in 25 headwater systems over 18 years. Our analysis documented a significant decreasing trend in critical pool habitat (p = 0.006) and a significant increase in distance between these pools (p = 0.001) since 2003. Natural recruitment of large wood from second-growth riparian areas appears to be slower than losses. However, large wood recruitment from Superstorm Sandy in 2012, at least temporarily stabilized pool numbers. While salmonid populations can be highly resilient, disturbances can create unstable habitat conditions. These conditions could become more probable with projected alteration of flow regime due to climate change. These results highlight the need to further understand the potential impacts acute disturbances like drought, floods, debris flows, and other formidable events could have on temporal habitat availability.
Keywords:
stream; pool; habitat; pool stability; brook trout; Salvelinus fontinalis; disturbance
Key Contribution: This study found that pools, critical habitat for stream-dwelling fishes, declined in number over an 18-year period. Distances between pools also increased. A large disturbance event (Superstorm Sandy) added wood to streams and stabilized pool numbers. Loss of pools likely reduces the resiliency of brook trout to floods and droughts.

Graphical Abstract

1. Introduction

Worldwide, native salmonid fishes are declining despite efforts to protect and enhance their populations [1,2,3,4,5]. In many cases, habitat and its restoration are identified as the key to conserving these salmonids. At large scales, habitat restoration efforts have focused on increasing river connectivity via passage and dam removals [6,7,8,9,10,11]. Whereas at smaller scales, restoring instream and riparian habitat is a critical tool in salmonid conservation [12,13,14].
Numerous studies have identified pool habitats as critical to trout and salmon [15,16,17,18,19,20]. Pool habitats are energetically profitable [21], and pools provide refuge during drought or low flow conditions [22,23] and are critical for overwintering [24,25,26,27].
Pool formation processes are caused by locations that mobilize bed material away from that location faster than it is coming in. In high-gradient, forested systems with small drainage areas (less than 100 km2), these bed scour areas are mostly found at flow obstructions and bedrock-reinforced knickpoints [28]. Bedrock irregularities and geological influence are relatively unsusceptible to disturbances over small time intervals, whereas flow obstructions caused by woody debris or sediment are temporally prone to change [20,21,22,23,24,25,26,27,28,29,30,31,32,33].
The size and stability of pools caused by woody debris or sediment is largely dependent on the size of the wood, channel size, and overall riparian characteristics [24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39]. Larger pieces of wood are more often responsible for pool formation, and loss of large wood (>60 cm) may present as decreased pool frequency [40]. Passive riparian restoration in second-growth forests is slow and is more successful in restoring instream wood and pool recovery in less managed areas, as compared to watersheds managed for timber harvest or agriculture [41].
The degree of human impact in riparian areas of Pacific Northwest (PNW) streams is known to affect the quantity and quality of pool habitats. Managed streams have less large wood (LW) density and fewer LW complexes per km than natural systems in streams in second-growth riparian areas [42]. Further, large pool density also declines in managed forests in the PNW [43]. A study in the Columbia River basin compared stream habitat surveys completed about 50 years apart [44]. In watersheds managed for resource extraction and other human activities, large pools and deep pools decreased by over 50%. Whereas in watersheds minimally affected by human activity, stream pool frequency increased or remained the same [44].
Headwater streams in the eastern U.S. have been, and continue to be, extensively impacted by human activities and are likely experiencing changes in pool habitats like those observed in the PNW. In eastern streams, salmonids include native (brook trout Salvelinus fontinalis Mitchill 1814 and Atlantic salmon Salmo salar Linnaeus) and introduced species (brown trout Salmo trutta Linnaeus 1758 and rainbow trout Oncorhynchus mykiss Walbaum 1792). Eastern salmonids also rely on pool habitats; thus, trends in pools are similarly important to conservation in eastern systems. However, studies examining temporal changes in pools in the eastern U.S. are lacking.
Our research focuses specifically on the temporal shifts in pool habitat observed in Appalachian headwater streams since 2003. The study streams all contain naturally reproducing brook trout. Due to the lack of research on temporal habitat shifts in cold-water systems in the eastern U.S., the primary objective was to address pool trends in Appalachian streams. We tested the hypothesis that the overall trend in pool habitat experienced a non-monotonic trend throughout the study period, especially considering Superstorm Sandy’s 2012 differential impact on watersheds in the study area [45]. Deviation from a monotonic trend would suggest environmental factors inhibiting long-term pool resistance or resilience, which could be attributed to historic land use (i.e., logging) or other factors.
The second objective was to address spatial arrangement of habitat through time. Spatial arrangement along a stream gradient would logically follow a random distribution, with the variety of environmental factors dictating pool formation. Considering the accelerated frequency of bank-full events and catastrophes [45,46,47], these systems are likely not experiencing random distribution. Channel characteristics that are not conducive to pool formation will probabilistically be lost through time. Testing the hypotheses from this study will yield valuable insight into the overall resistance and resilience of Appalachian systems and begin to address how pool habitats, critical to Appalachian brook trout persistence and resilience, are changing over time.

2. Materials and Methods

The study area is composed of 25 stream segments with a high degree of variability in characteristics representative of central Appalachian headwater systems. The 25 segments are located throughout the Monongahela National Forest, with several on privately owned land, and distributed among six HUC-10 watersheds (Cranberry River, Deer Creek of Greenbrier River, Dry Fork, Middle Fork, North Fork of South Branch of the Potomac River, and the Elk River) (Figure 1). These sites were defined as small headwater tributaries (mean drainage area = 7.39 km2) located in the mountainous eastern part of West Virginia. In addition to brook trout, these streams contain typical coldwater fish assemblages.

2.1. Long-Term Data Collection

Stream habitat was sampled following a modified basin-wide visual estimation technique (BVET [46,47,48]). From 2003 to 2009, 12 or 13 of these streams were surveyed for habitat on alternating years (e.g., Seneca Creek was sampled in 2003 but not in 2004 and sampled again in 2005). From 2010 through 2020, all 25 streams were sampled annually. Habitat sampling was typically conducted in low-flow conditions (which historically occur between June and October). This low-flow sampling ensures that the most habitat complexity was observed within the survey locations, and accuracy of estimates is increased in determining cover. The starting point of each survey occurred in the same locations among years and was marked with spray paint, ribbon, and GPS points. Using a hip chain, surveyors recorded the beginning and end distance of each habitat unit (riffle, run, pool, dry, or cascade) relative to the starting location. Three transect widths/depths were recorded for every habitat unit progressing upstream. Additional variables collected included random large woody counts and size categories [45], wetted widths, and bankfull widths per individual habitat unit. Post-2009, every pool was measured for maximum depths, pool-out depth (depth of the thalweg at pool outflow), cover area, and pool formation type (large woody debris, boulder, bank, free form). Pool area was calculated using pool length and widths. The sampled habitats ranged in length from ~900 to 3000 m, depending on drainage area (Table 1).
The focus of this study was on the distance between pools and the total number of pool habitat units. Pool quality was not considered, because that metric was added partway through the time series. Surveyors defined pools as any low-velocity, unbroken deep water (>30 cm) with a pool area greater than 1 m2. Since surveyors did not directly record distance between or among pools, digitization in ArcGIS was used to geotag each pool location within the survey area, based on recorded hip chain distances traveled along the thalweg from starting locations to each habitat unit. Since the calculation of the pool area used these length measurements, it was assumed these distances represented the most accurate data for pool locations.

2.2. Digitization

One of the objectives of this study was to address temporal distances between pools. To analyze habitat data beyond simple counts or area per year, digitization was performed using ArcGIS 10.5.1 (ESRI, Inc., Redlands, CA, USA). Data were collected with the beginning and end distances of each habitat unit in each study segment measured, permitting digitization of individual pool units over a corrected stream water grid using the measure tool. To achieve the highest degree of spatial accuracy, the points were overlain on a corrected water grid generated off 1/9 arc second (~3 m cell size) digital elevation models (DEMs). While this approach can carry uncertainties in flow projections because grids cannot account for the scale that rugged areas demonstrate [49], corrected flow grids were primarily used as a method to most accurately predict stream channel path. With intrinsic errors associated with digitizing historical data and stream channel changes, any pools found within 10 m between years were considered as the same pool. The location was assigned as the overall average location between the years in that 10 m range based on the first pool occurrence year. Each pool was also filtered to exclude any pools that did not achieve the defined pool parameter (depth).
By using a hydro-corrected DEM among all the study sites [50,51], digitization of the pool features was able to follow a 3 m grid stream channel that represented the most accurate depiction of the average stream channel over the course of the study period, as it followed elevation and flow accumulation at the lowest grid points. In addition, a basin area flow model was generated to account for the daily flow rate encountered by surveyors on the day a habitat was sampled. This model utilizes elevation and basin area ratios to best determine flow rates upstream of a USGS gage station [52]. Because surveys did not record flow rates, this helped account for serial correlation that pool variables were a result of flow variations.
A corrected basin area flow model was established for each study segment to generate the best approximation of flow rates for the date surveys were completed. This approach incorporated 30-year normal precipitation values to spatially correct for variability in precipitation and basin areas that contribute to each USGS gauging station [53]. This spatially corrected for the variability in precipitation over each drainage, yielding the most accurate flow estimations encountered during the habitat sampling dates. Although this method has intrinsic errors because it cannot account for many hydrological variables (springs, upwellings, karst, etc.), it accounts for the environmental variable flow rate at the location of each stream segment based upon basin accumulation and 30-year normal averages. With flow playing such an important role in determining habitat measures and classifications [22,54], the mean daily discharge (CFS) at each stream on the day of habitat surveys gave an understanding of whether variability in relative discharge levels between years was responsible for pool quantity changes or if stream morphology changed through time. Whereas USGS sites were beyond the recommended modeling distance from many of the study sites, the flow estimations were assumed to be appropriate representations of habitat surveys that day. This is largely due to habitat surveys being performed during lowest flows to accurately assess the highest degree of channel morphology diversity and the average flow rate taken amongst all study site estimations.

2.3. Spatial Analysis

Beyond the temporal change in pool numbers and area, the digitization process allowed statistics to be gathered using spatial patterns and distances. A nearest neighbor (NN) test was used to assess the spatial pattern in habitat point data. More simply, it is the relation of each pool to one another in each area. This tool measured the distance between each point feature (pool habitat) and its corresponding Nth neighbor location. To calculate the spatial statistic, the analysis divided the observed average distance amongst pools by the expected average distance (hypothetical random distribution of the points covering the same area). If the average was greater than the hypothetical average of random distribution, the features were considered dispersed [54]. The null hypothesis and basic assumption were that pool features had equal or random probability of occurring through each study area. Study sites were individually tested per year within the basin area to understand distribution change across broader spatial scales.
Nearest neighbor was chosen for this analysis because the features before 2010 did not contain relatable continuous variables (i.e., spawning area and/or cover area). This test is most often associated with an exploratory approach to understanding spatial patterns in habitat selection, not necessarily habitat itself [54]. However, the use of NN has been proven useful in confidently uncovering trends in habitat changes [55]. Ideally, pool quality attributes like cover and spawning area would have been included in this analysis. However, the presented data were not collected in a manner to incorporate additional spatial analyst assessments. Understanding the change in average distances and significant patterns could provide insights into potential channel morphology changes.

2.4. Statistical Analysis

Trend and change detection in environmental variables are statistically challenging because it is hard to define change, assumptions are not always fulfilled, and application of the many different methodologies to the same data may yield divergent results [56]. We used the Mann–Kendall test to analyze any overall series trend in the average pool number, nearest neighbor, and pool area, as recommended by the World Meteorological Organization for public application [56]. Habitat data were not serially correlated, allowing us to test for potential trends in the independent data. We tested the null hypothesis that habitat variables (number, area, and nearest neighbor) followed no significant trend throughout the study period (α = 0.05). Next, we used Spearman’s Partial Correlation Trend Test using flow as a covariate (α = 0.05) to partition out the sample date variance that flow could have had on habitat quantity, pool area, and distance between pools. Since flow rates impact habitat complexity and designations [22], this tested whether pool number, pool area, and nearest neighbor were correlated more with changes in flow rate between years versus the actual trend through time.
We used Pettitt’s test to assess if there was a point at which the distributions of the habitat data did not have the same location parameters (trend changed). Our dataset encompasses Superstorm Sandy in 2012, which was shown to differentially impact habitat (windthrow) [45]. Thus, this test was used to see if mean pool area and quantity changes throughout the study area occurred due to this storm event. If any outlying parameters altered the distribution of habitat data significantly, then a change point exists [57]. The null hypothesis was that no significant change in variable distribution was present in the study period.

3. Results

An overall trend of declining mean number of pools per stream from 2003 to 2020 was evident (Figure 2). The Mann–Kendall test revealed a highly significant negative trend (S = −73.0, p = 0.006) in the number of pools. However, covariate test results of the environmental variable (flow) followed a monotonic trend during the same time frame (S = −4.14, p = 0.87). This indicated that trends in the number of pools over time were not correlated to differences in discharge during habitat surveys. When flow was accounted for, Spearman’s correlation test (to indicate the magnitude of the linear/monotonic trend with time) showed a highly significant decreasing trend in pool number (rs = −0.67, p = 0.003). With the magnitude and significant change in pool number through time, the final step was to indicate if a significant change took place using the Pettitt test. This test resulted in a significant change point of the habitat data after 2012 (Kt = 52 and p = 0.001). Before 2012, the overall rate of change was roughly −2.7 pools lost per stream per year. Post-Superstorm Sandy in 2012, this rate of change was 0.72 pools gained per year.
There was no temporal trend associated with the pool area across the study sites (S = −23, p = 0.41). Likewise, neither covariate (pool number: p = 0.62) nor flow (p = 0.43) was correlated with pool area. Although there appears to be a leveling off of the pool area after 2012, there was no significant point change in the pool area test (p = 0.57; Figure 3). Overall pool numbers were decreasing over time, but the pool area did not follow the same trend. This would suggest that some pools have environmental attributes that make them temporally more stable. The average rate of change in pool area across streams did not show the same significant shift as average pool number. Prior to Superstorm Sandy in 2012, the pool area declined by 58.4 m2/year per stream. However, following Sandy, the loss of pool area dropped to 15.3 m2/year per stream. This significant point change post-Superstorm Sandy somewhat stabilized pool area loss.

Spatial Patterns in Pools

The average NN distance (stream thalweg distance between pools) increased by 101.2 percent during the study period, with a mean distance of 32.7 m from 2003 to 2010 to 65.8 m from 2011 to 2020 (Figure 4). This NN distance trend showed a highly positive significance (r = 85, p = 0.001).
This NN result showed a drastic change in the probability of significantly dispersed pool habitat among the study sites over time. The probability that a site would display significant dispersal of pool habitat increased from 12 percent from 2003 to 2010 to a probability of 49 percent from 2011 to 2020 (Figure 4). This result is negatively correlated with the significant reduction in pool number occurring during these same time frames. However, the rate of change shifted in 2012. Prior to 2012, the distance between pools increased by 3.9 m/yr. Post-Superstorm Sandy, the distance between pools decreased by 3.1 m/year.

4. Discussion

Pool habitats are critical to stream-dwelling trout and other fishes inhabiting eastern headwater streams. Brook trout commonly utilize pools over other habitats [15,58,59,60,61]. Pools also represent critical habitat needed for spawning and rearing and provide sanctuary during times of low flows for brook trout [22]. Thus, pools are critical to both persistence and resilience of brook trout to disturbance events.
The decreasing trend in pool numbers and the increases in distance between pools we observed should be alarming. Declines in pool quantity and quality have been shown to drastically impact brook trout and other salmonid species [62,63,64,65]. Our findings of declining temporal trends in pools confirm those in the Pacific Northwest [42,43,44]. There, declines in pool frequency were related to land management such as timber harvest [44]. Timber extraction in the riparian area reduces the volume of large wood available to naturally recruit to the stream, reducing this pool-forming material. Thus, streams relying on large wood as pool-forming mechanisms would be expected to be more influenced by land management actions, increased base flows, drought, and extreme weather events that may alter large wood recruitment, transport, or retention [45,47,66,67,68]. The watersheds of our study streams are second-growth forests, and most still experience wood extractions, including in the streamside management zones. This harvest likely reduces the availability of natural recruitment of large wood to these streams.
Similarly, the projected increases in storm intensity related to climate change [69,70] may function like timber harvest by removing large wood from riparian areas through windfall. Superstorm Sandy hit West Virginia as a snowstorm on 29 October 2012. The heavy wet snow and high winds when many deciduous trees still held leaves resulted in significant tree damage at middle elevations in the central Appalachians. Surveys immediately following the storm found it delivered up to one piece of large wood/m of thalweg in the most affected of our study streams [45].
In the central Appalachians, climate change is already increasing air temperatures and winter discharge but creating longer, dryer hot spells [47]. It is further expected to increase the intensity of storms [69]. These, and projected changes in timing, magnitude, and intensity of precipitation events related to climate change, highlight the importance of understanding stream habitat resistance and resilience to these climatic alterations. The temporal changes observed in pool number and nearest neighbor post-Superstorm Sandy (2012) indicate that disturbances can at least temporarily aid in stabilizing pool habitat via wood loading [45]. However, disturbance-scale events do not necessarily form long-term pool habitat and can even reduce the long-term potential for large wood from an area [70,71]. Increased frequency of storms could cause small-diameter wood debris to be the primary pool-forming material. This smaller diameter is less suitable for stable habitat formations in larger streams [72,73], whereas the secondary growth that could naturally recruit to the stream may form more stable habitat [70,71]. Little research has been performed to analyze temporal pool stability, especially considering the variability demonstrated across watersheds and stream networks. Identifying these areas and the long-term shifts in habitat could help not only conserve cold-water fisheries but also determine which systems support more stable habitats in an uncertain future.
While most studies focus on fish population responses to these variables, our study addressed the overall trend in critical (pool) habitat of an already restricted trout species [74]. Although relatively short in time scale, it should not be surprising that these trends exist. These systems experienced extensive logging and likely have historically experienced a dramatic change in channel morphology [34,39,74]. These impacts likely persist because most forest stands within the study sites are considered secondary growth, and wood is a critical pool-forming mechanism in these and similar headwater streams [34,75,76]. In the central Appalachians, forestry best management plans (BMPs) frequently call for limiting harvest to 50% of basal area within the 30.5 m riparian buffer of perennial streams. However, no limits are placed on how frequently the 50% basal area might be removed [77,78]. In several of our study streams on private land, multiple harvests have taken place in the streamside management zone since 2003. The BMPs also require logging debris and tops to be removed from streams. We suggest that the declines in pool numbers and distances prior to Superstorm Sandy resulted because recruitment of wood was outpaced by stream cleaning and transport. Thus, small average riparian timber size likely contributes to the long-term decline and interannual variability of pool habitat [31,35,39].
Trout populations occupying these high-gradient systems already undergo drastic fluctuations in abundance and survival [64,79,80]. These temporal variations are largely due to temperature, disturbances, competition, and natural variations in other environmental factors [22,64,79,81,82]. Our finding of a decade of pool habitat loss is alarming alone, but when coupled with increases in distance amongst pools, these concerns compound for brook trout populations moving forward. Coincident to our findings of decadal declines in pools in our study streams, brook trout populations were found to be decreasing in other Appalachian streams. In Maryland, stream declines were substantial [83], and in the Shenandoah National Park [84], brook trout populations declined by 50% or more in nearly 70% of 94 sites between 1996 and 2022.
While this study does not directly address the impact of legacy timber harvest, the lingering effects could still be impacting Appalachian streams. Combined with the effects of climate change, the lasting effects of timber activities could further exacerbate the trend in habitat loss across Appalachia via reduced natural wood recruitment to streams. Climate change, in conjunction with the increases in distance between and among pools in a system, could intensify population impacts during episodic events like droughts by reducing the carrying capacity for brook trout there [22]. Salmonids have been predicted to lose much of their southern, lower elevation, and mainstem habitats with climate change [64,85,86], with some regions expected to lose up to 60% of available trout habitat if air temperature alone rises 5 °C [85], while many other populations will not exceed 90% modeled persistence within the next 100 years [82,87,88]. Facing this grim outlook for brook trout, it is critical to further understand what makes pool habitat more temporally available and stable through time to protect the genetic diversity and overall range of this ecologically and socially valuable species [89]. In addition, it will be important to identify and remedy the streams that do not exhibit strong habitat stability through time or are experiencing a continued trend in habitat loss.

Limitations

This analysis examined habitat on 25 cold, headwater brook trout streams over an 18-year period to identify changes in critical pool habitats needed by brook trout. It relied upon the generation of some variables from data digitization (NN). This process carries some inherent errors from the ArcGIS measure tool accuracy, DEM accuracy of stream channel, and error in study site starting/ending points. Steps to mitigate duplicating pools between years were outlined in the methods. However, errors in spatial scaling (1/9 arc second) are likely missing the detail required to approximate headwater channel complexity. Although this associated error is a drawback, the overall analysis of pool number trends still indicated a negative trend through time. Logically, this would cause the digitized pools to become farther away from one another.
Another potential limitation is in the reclassification of pools to standardized 30 cm depths over a 1 m2 area. This reclassification is warranted to standardize data across study sites. However, some sites have limited numbers of pools that meet our criteria (greater than 30 cm deep over 1 m2). Even with these limitations, there are few studies that specifically investigate the temporal nature of salmonid habitat (especially between and among flooding events in small, heterogeneous catchments across a large area). Future studies should collect and address specific attributes of temporal pool quality, availability, and arrangement, and their relationship with fish populations. This will help management of a valuable resource moving forward into a changing world.

5. Conclusions

This study provides an understanding of the spatiotemporal trends in stream pool habitats in the Appalachian Mountains. However, due to common land management activities and the shared dependence of large wood as a pool-forming mechanism in the Appalachians, Pacific Northwest, and elsewhere, our findings could have broader implications for salmonids and other species. The findings highlight the variable nature of pools and suggest how an extreme weather event led to at least temporary stabilization in pool habitat through wood loading. Extreme weather events like heat waves, storms, extreme rain events, and tropical cyclones are already increasing in frequency [90,91,92]. Events like Superstorm Sandy fell and damage trees in riparian areas, reducing mean stand age. This could reduce the amount of large, older wood available to naturally recruit to streams over time. In streams relying on large wood as pool-forming mechanisms, the worsening extreme weather events could result in fewer pools, as wood recruitment to streams from riparian areas may not keep pace with instream losses of large wood. Prior to Superstorm Sandy, our finding of declining pool frequency and numbers over time suggests that current forestry riparian BMPs are not conservative enough to protect large wood recruitment. Likely as a result, instream restoration of lotic cover has been increasingly implemented in both western and eastern streams.

Author Contributions

Both authors contributed to conceptualization, validation, investigation, data curation, writing of the original draft, and visualization. Z.Z. was responsible for software and formal analysis. K.H. provided resources, review and editing, supervision, and funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the West Virginia Division of Wildlife (017270-00001.1.1009654R) and the USDA Forest Service (017388-00002.1.1009731R). Support was also provided by the USDA National Institute of Food and Agriculture, McIntire Stennis Program and the West Virginia Agricultural and Forestry Experiment Station (Project 1021457) to the second author.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

The authors have reviewed and edited the output and take full responsibility for the content of this publication. This study would not have been possible without the many graduate and undergraduate students who contributed to the long-term study in the Hartman Lab or the continued support by WVDNR and the USFS. We thank J. Kingsbury for creating the study map.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Dadswell, M.; Spares, A.; Reader, J.; McLean, M.; McDermott, T.; Samways, K.; Lilly, J. The decline and impending collapse of the Atlantic Salmon (Salmo salar) population in the North Atlantic Ocean: A review of possible causes. Rev. Fish. Sci. Aquac. 2022, 30, 215–258. [Google Scholar] [CrossRef]
  2. Kaeriyama, M. Warming climate impacts on production dynamics of southern populations of Pacific salmon in the North Pacific Ocean. Fish. Ocean. 2023, 32, 121–132. [Google Scholar] [CrossRef]
  3. Mills, K.E.; Pershing, A.J.; Sheehan, T.F.; Mountain, D. Climate and ecosystem linkages explain widespread declines in North American Atlantic salmon populations. Glob. Change Biol. 2013, 19, 3046–3061. [Google Scholar] [CrossRef]
  4. Nicola, G.G.; Elvira, B.; Jonsson, B.; Ayllon, D.; Almodovar, A. Local and global climatic drivers of Atlantic salmon decline in southern Europe. Fish. Res. 2018, 198, 78–85. [Google Scholar] [CrossRef]
  5. Peterman, R.M.; Dorner, B. A widespread decrease in productivity of Sockeye Salmon (Oncorhynchus nerka) populations in western North America. Can. J. Fish. Aq. Sci. 2012, 69, 1255–1260. [Google Scholar] [CrossRef]
  6. Skalski, J.R.; Whitlock, S.L.; Townsend, R.L.; Harnish, R.A. Passage and survival of juvenile salmonid smolts through dams in the Columbia and Snake Rivers, 2010–2018. N. Am. J. Fish. Manag. 2021, 41, 678–696. [Google Scholar] [CrossRef]
  7. Duda, J.J.; Torgersen, C.E.; Brenkman, S.J.; Peters, R.J.; Sutton, K.T.; Connor, H.A.; Kennedy, P.; Corbett, S.C.; Welty, E.Z.; Pess, G.R.; et al. Reconnecting the Elwha River: Spatial patterns of fish response to dam removal. Front. Ecol. Evol. 2021, 9, 765488. [Google Scholar] [CrossRef]
  8. Dodd, J.R.; Cowx, I.G.; Joyce, D.A.; Bolland, J.D. Can’t pass or won’t pass: The importance of motivation when quantifying improved connectivity for riverine brown trout Salmo trutta. J. Fish Biol. 2024, 104, 851–865. [Google Scholar] [CrossRef]
  9. Copeland, T.; Blythe, D.; Schoby, W.; Felts, E.; Murphy, P. Population effect of a large-scale stream restoration effort on Chinook salmon in the Pahsimeroi River, Idaho. Riv. Res. Appl. 2021, 37, 100–110. [Google Scholar] [CrossRef]
  10. Hill, N.L.; Trueman, J.R.; Prévost, A.D.; Fraser, D.J.; Ardren, W.R.; Grant, J.W.A. Effect of dam removal on habitat use by spawning Atlantic salmon. J. Great Lakes Res. 2019, 45, 394–399. [Google Scholar] [CrossRef]
  11. Hogg, R.S.; Coghlan, S.M.; Zydlewski, J.; Gardner, C. Fish community response to a small-stream dam removal in a Maine coastal river tributary. Trans. Am. Fish. Soc. 2015, 144, 467–479. [Google Scholar] [CrossRef]
  12. Hodge, B.W.; Henderson, R.; Brehme, C.E. Restoration effects on a native cutthroat trout stream. Riv. Res. Appl. 2025, 41, 541–552. [Google Scholar] [CrossRef]
  13. Richer, E.E.; Graf, S.; Kondratieff, M.C. Evaluating the performance of instream structures for a stream restoration project in Colorado. Riv. Res. Appl. 2024, 40, 308–321. [Google Scholar] [CrossRef]
  14. Rogers, K.M.; Rummel, S.M.; Lavelle, K.M.; Duchamp, J.E.; Niles, J.M.; Janetski, D.J. A comparison of Brook Trout passage at road culverts to broadscale assessment criteria in a Pennsylvania headwater stream. N. Am. J. Fish. Manag. 2021, 41, 1351–1359. [Google Scholar] [CrossRef]
  15. Davis, L.A.; Wagner, T. Scale-dependent seasonal pool habitat use by sympatric wild Brook Trout and Brown Trout populations. Trans. Am. Fish. Soc. 2016, 145, 888–902. [Google Scholar] [CrossRef]
  16. Dare, M.R.; Hubert, W.A.; Gerow, K.G. Changes in habitat availability and habitat use and movements by two trout species in response to declining discharge in a regulated river during winter. N. Am. J. Fish. Manag. 2002, 22, 917–928. [Google Scholar] [CrossRef]
  17. Hallbert, T.B.; Keeley, E.R. Instream complexity increases habitat quality and growth for cutthroat trout in headwater streams. Can. J. Fish. Aquat. Sci. 2023, 80, 992–1005. [Google Scholar] [CrossRef]
  18. Jakober, M.J. Autumn and Winter Movement and Habitat Use of Resident Bull Trout and Westslope Cutthroat Trout in Montana. Master’s Thesis, Montana State University, Bozeman, MT, USA, 1995. [Google Scholar]
  19. Roper, B.B.; Scarnecchia, D.L.; La Marr, T.J. Summer distribution of and habitat use by Chinook salmon and steelhead within a major basin of the South Umpqua River, Oregon. Trans. Am. Fish. Soc. 1994, 123, 298–308. [Google Scholar] [CrossRef]
  20. Saffel, P.D.; Scarnecchia, D.L. Habitat use by juvenile bull trout in belt-series geology watersheds of northern Idaho. Northw. Sci. 1995, 69, 304–317. [Google Scholar]
  21. Rosenfeld, J.S.; Boss, S. Fitness consequences of habitat use for juvenile cutthroat trout: Energetic costs and benefits in pools and riffles. Can. J. Fish. Aquat. Sci. 2001, 58, 585–593. [Google Scholar] [CrossRef]
  22. Hakala, J.P.; Hartman, K.J. Drought effect on stream morphology and brook trout (Salvelinus fontinalis) populations in forested headwater streams. Hydrobiologia 2004, 515, 203–213. [Google Scholar] [CrossRef]
  23. Elliott, J.M. Pools as refugia for brown trout during two summer droughts: Trout responses to thermal and oxygen stress. J. Fish Biol. 2000, 56, 938–948. [Google Scholar] [CrossRef]
  24. Muhlfeld, C.C.; Bennett, D.H.; Marotz, B. Fall and winter habitat use and movement by Columbia River redband trout in a small stream in Montana. N. Am. J. Fish. Manag. 2001, 21, 170–177. [Google Scholar] [CrossRef]
  25. Swales, S.; Lauzier, R.B.; Levings, C.D. Winter habitat preferences of juvenile salmonids in two interior rivers in British Columbia. Can. J. Zool. 1986, 64, 1506–1514. [Google Scholar] [CrossRef]
  26. Bean, J.R.; Wilcox, A.C.; Woessner, W.W.; Muhlfeld, C.C. Multiscale hydrogeomorphic influences on bull trout (Salvelinus confluentus) spawning habitat. Can. J. Fish. Aq. Sci. 2015, 72, 514–526. [Google Scholar] [CrossRef]
  27. Muhlfeld, C.C. Spawning characteristics of redband trout in a headwater stream in Montana. N. Am. J. Fish. Manag. 2002, 22, 1314–1320. [Google Scholar] [CrossRef]
  28. Buffington, J.M.; Lisle, T.E.; Woodsmith, R.D.; Hilton, S. Controls on the size and occurrence of pool in coarse-grained forest rivers. Riv. Res. Appl. 2002, 18, 507–531. [Google Scholar] [CrossRef]
  29. Lisle, T.E. Effects of woody debris on anadromous salmonid habitat, Prince of Wales Island, southeast Alaska. N. Am. J. Fish. Manag. 1986, 6, 538–550. [Google Scholar] [CrossRef]
  30. Bisson, P.A.; Bilby, R.E.; Bryant, M.D.; Dolloff, A.C.; Grette, G.B.; House, R.A.; Murphy, M.L.; Koski, K.V.; Sedell, J.R. Large woody debris in forested streams in the Pacific Northwest: Past, present, and future. In Streamside Management: Forestry and Fishery Interactions; University of Washington, Institute of Forest Resources: Seattle, WA, USA, 1987; pp. 143–190. [Google Scholar]
  31. Murphy, M.L.; Koski, K.V. Input and depletion of woody debris in Alaska streams and implications for streamside management. N. Am. J. Fish. Manag. 1989, 9, 427–436. [Google Scholar] [CrossRef]
  32. Bilby, R.E.; Ward, J.W. Characteristics and function of large woody debris in streams draining old-growth, clear-cut, and second growth forests in southwestern Washington. Can. J. Fish. Aq. Sci. 1991, 48, 2499–2508. [Google Scholar] [CrossRef]
  33. Beechie, T.J.; Pess, G.; Kennard, P.; Bilby, R.E.; Bolton, S. Modeling recovery rates and pathways for woody debris recruitment in northwestern Washington streams. N. Am. J. Fish. Mgt. 2000, 20, 436–452. [Google Scholar] [CrossRef]
  34. Hartman, G.F.; Scrivener, J.C.; Miles, M.J. Impacts of logging in Carnation Creek, a high-energy coastal stream in British Columbia, and their implications for restoring fish habitat. Can. J. Fish. Aquat. Res. 1994, 53, 237–251. [Google Scholar] [CrossRef]
  35. Flebbe, P.A.; Dolloff, C.A. Trout use of woody debris and habitat in Appalachian wilderness streams of North Carolina. N. Am. J. Fish. Mgt. 1995, 15, 579–590. [Google Scholar] [CrossRef]
  36. Hedman, C.W.; Lear, D.H.V.; Swank, W.T. In-stream large woody debris loading and riparian forest seral stage associations in the southern Appalachian Mountains. Can. J. For. Manag. 1996, 26, 1218–1227. [Google Scholar] [CrossRef]
  37. Abbe, T.B. Patterns, Mechanics and Geomorphic Effects of Wood Debris Accumulations in a Forest River System. Ph.D. Thesis, University of Washington, Seattle, WA, USA, 2000. Available online: https://wvu.idm.oclc.org/login?url=https://www.proquest.com/dissertations-theses/patterns-mechanics-geomorphic-effects-wood-debris/docview/304638861/se-2?accountid=2837 (accessed on 24 September 2025).
  38. Braudrick, C.A.; Grant, G.E. When do logs move in rivers? Water Resour. Res. 2000, 36, 571–584. [Google Scholar] [CrossRef]
  39. Ralph, S.C.; Poole, G.C.; Conquest, L.L.; Naiman, R.J. Stream channel morphology and woody debris in logged and unlogged basins in western Washington. Can. J. Fish. Aquat. Res. 2011, 51, 37–51. [Google Scholar] [CrossRef]
  40. Rosenfeld, J.S.; Huato, L. Relationship between large woody debris characteristics and pool formation in small coastal British Columbia streams. N. Am. J. Fish. Manag. 2003, 23, 928–938. [Google Scholar] [CrossRef]
  41. Martens, K.D.; Devine, W.D. Pool formation and the role of instream wood in small streams in predominantly second-growth forests. Envir. Manag. 2023, 71, 1011–1023. [Google Scholar] [CrossRef]
  42. McIntosh, B.A. Management History of Eastside Ecosystems: Changes in Fish Habitat over 50 Years 1935 to 1992; General Technical Report PNW 321; U.S. Forest Service, Pacific Northwest Research Station: Corvallis, OR, USA, 1994; p. 55. Available online: https://research.fs.usda.gov/treesearch/6232 (accessed on 22 September 2025).
  43. McIntosh, B.A. Historical changes in fish habitat for select river basins of eastern Oregon and Washington. Northw. Sci. 1994, 68, 36–53. [Google Scholar]
  44. McIntosh, B.A.; Sedell, J.R.; Thurow, R.F.; Clarke, S.E.; Chandler, G.L. Historical changes in pool habitats in the Columbia River Basin. Ecol. Appl. 2000, 10, 1478–1496. [Google Scholar] [CrossRef]
  45. Andrew, R.G.; Hartman, K.J. Uneven inputs of woody debris in Appalachian streams from Superstorm Sandy. Can. J. Fish. Aq. Sci. 2014, 72, 1–6. [Google Scholar] [CrossRef]
  46. Knox, J.C. Sensitivity of modern and Holocene floods to climate change. Quat. Sci. Rev. 2000, 19, 439–457. [Google Scholar] [CrossRef]
  47. Andrew, R.G.; Schwinghamer, C.W.; Hartman, K.J.; Briggs, E.E. Climate change influence on brook trout populations in the Central Appalachians. Ecol. Freshw. Fish 2018, 31, 710–725. [Google Scholar] [CrossRef]
  48. Hankin, D.G.; Reeves, G.H. Estimating total fish abundance and total habitat area in small streams based on visual estimation methods. Can. J. Fish. Aq. Sci. 1988, 45, 834–844. [Google Scholar] [CrossRef]
  49. Wise, S.M. Effect of differing DEM creation methods on the results from a hydrological model. Comp. Geosci. 2007, 33, 1351–1365. [Google Scholar] [CrossRef]
  50. Maathius, B.H.P.; Wang, L. Digital elevation model-based hydro-processing. Geocart. Intern. 2006, 21, 21–26. [Google Scholar] [CrossRef]
  51. Zhang, H.; Huang, G.H.; Wang, D. Establishment of channel networks in a digital elevation model of the prairie region through hydrological correction and geomorphological assessment. Can. Wat. Res. J. 2013, 38, 12–23. [Google Scholar] [CrossRef]
  52. Vadas, R.L., Jr. Instream-flow needs for anadromous salmonids and lamprey on the Pacific coast, with special reference to the Pacific Southwest. Environ. Monitor. Assess. 2000, 64, 331–358. [Google Scholar] [CrossRef]
  53. Zambrano-Bigiarini, M. HydroTSM: Time Series Management, Analysis and Interpolation for Hydrological Modelling; R package Version 0.7-0. 2024. Available online: https://cran.r-project.org/web/packages/hydroTSM/index.html (accessed on 24 September 2025).
  54. Bayard, T.S.; Elpick, C.S. Using spatial point-pattern assessment to understand the social and environmental mechanisms that drive avian habitat selection. AUK 2010, 127, 485–494. [Google Scholar] [CrossRef]
  55. Wing, M.G.; Keim, R.F.; Skaugset, A.E. Applying geostatistics to qualify distributions of large woody debris in streams. Comp. Geosci. 1999, 25, 801–807. [Google Scholar] [CrossRef]
  56. Kundzewicz, Z.W.; Radziekewski, M. Methodologies for trend detection. Climate Variability and Change-Hydrological Impacts. In Proceedings of the Fifth FRIEND World Conference, Havana, Cuba, 27 November–1 December 2006. [Google Scholar]
  57. Pettitt, A.N. A non-parametric approach to the change-point problem. Appl. Stat. 1979, 28, 126–135. [Google Scholar] [CrossRef]
  58. Blair, S.G.; Gutowsky, L.F.G.; Fox, M.G. Factors affecting seasonal habitat use of native brook trout (Salvelinus fontinalis) in urban headwater streams. Ecol. Freshw. Fish 2021, 30, 490–502. [Google Scholar] [CrossRef]
  59. Pelletier, C. Brook Trout Habitat Selection and Movement Response to Critically High Temperatures in Fragmented Stream Networks. Master’s Thesis, University of Rhode Island, Kingston, RI, USA, 2023; p. 88. [Google Scholar]
  60. Davis, J.M.; Baxter, C.V.; Rosi-Marshal, E.J.; Pierce, J.L.; Crosby, B.T. Anticipating stream ecosystem responses to climate change: Toward predications that incorporate effects via land-water linkages. Ecosystems 2013, 16, 909–922. [Google Scholar] [CrossRef]
  61. Hartman, K.J.; Logan, M.N. Movement and habitat use by transplanted Brook Trout in an Appalachian headwater stream. Northeast. Nat. 2010, 17, 357–372. [Google Scholar] [CrossRef]
  62. Lonzarich, D.G.; Lonzarich, M.R.; Warren, M.L., Jr. Effects of riffle length on the short-term movement of fishes among stream pools. Can. J. Fish. Aquat. Res. 2000, 57, 1508–1514. [Google Scholar] [CrossRef]
  63. Elliott, J.M. Periodic habitat loss alters the competitive coexistence between brown trout and bullheads in small stream over 34 years. J. Anim. Ecol. 2006, 75, 54–63. [Google Scholar] [CrossRef]
  64. Wenger, S.L.; Isaak, D.J.; Luce, C.H.; Nevile, H.M.; Fausch, K.D.; Dunham, J.B.; Dauwalter, D.C.; Young, M.K.; Elsner, M.M.; Reiman, B.E.; et al. Flow regime, temperature, and biotic interactions drive differential declines of trout species under climate change. Proc. Nat. Acad. Sci. USA 2011, 8, 14175–14180. [Google Scholar] [CrossRef] [PubMed]
  65. Petty, J.T.; Hansbarger, J.L.; Huntsman, B.M.; Mazik, P.M. Brook trout movement in response to temperature, flow and thermal refuge within a complex Appalachian riverscape. Trans. Am. Fish. Soc. 2012, 141, 1060–1073. [Google Scholar] [CrossRef]
  66. IPCC Climate Change 2007: Working Group 2: Impacts, Adaptation and Vulnerability; Intergovernmental Pan on Climate Change: Geneva, Switzerland, 2007.
  67. IPCC 2014. Climate Change 2014: Summary for Policymakers. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Fifth Assessment Report of the Intergovernmental Panel of Climate Change, 2014; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2014. [Google Scholar]
  68. Comiti, F.; Lucia, A.; Rickenmann, D. Large wood recruitment and transport during large floods: A review. Geomorphology 2016, 269, 23–39. [Google Scholar] [CrossRef]
  69. Jong, B.-T.; Delworth, T.L.; Cooke, W.F.; Tseng, K.-C.; Murakami, H. Increases in extreme precipitation over the Northeast United States using high-resolution climate model simulations. NPJ Clim. Atmos. Sci. 2023, 6, 18. [Google Scholar] [CrossRef]
  70. Roghair, C.N.; Dolloff, A.C.; Underwood, M.K. Response of a brook trout population and instream habitat to a catastrophic flood and debris flow. Trans. Am. Fish. Soc. 2002, 131, 718–730. [Google Scholar] [CrossRef]
  71. Jones, T.A.; Daniels, L.D. Dynamics of large woody debris in small streams disturbed by the 2001 Dogrib fire in the Alberta foothills. For. Ecol. Manag. 2008, 256, 1751–1759. [Google Scholar] [CrossRef]
  72. Roni, P.; Beechie, T.J.; Bilby, R.E.; Leonetti, F.E.; Pollock, M.M.; Pess, G.R. A review of stream restoration techniques a hierarchical strategy for prioritizing restoration in Pacific Northwest watersheds. N. Am. J. Fish. Manag. 2002, 22, 1–20. [Google Scholar] [CrossRef]
  73. Spies, T.; Pollock, M.; Reeves, G.; Beechie, T. Effects of Riparian Thinning on Wood Recruitment: A Scientific Synthesis; U.S. Forest Service, Pacific Northwest Region, Science Review Team Wood Recruitment Subgroup: Portland, OR, USA, 2013. [Google Scholar]
  74. Hudy, M.; Thieling, T.M.; Gillespie, N.; Smith, E.P. Distribution, status, and land use characteristics of subwatersheds within the native range of brook trout in the Eastern United States. N. Am. J. Fish. Manag. 2008, 28, 1069–1085. [Google Scholar] [CrossRef]
  75. Studinski, J.M.; Hartman, K.J.; Niles, J.M.; Keyser, P. The effects of riparian forest disturbance on stream temperature, sedimentation, and morphology. Hydrobiologia 2012, 686, 107–117. [Google Scholar] [CrossRef]
  76. Dolloff, C.A.; Flebbe, P.A.; Owen, M.D. Fish habitat and fish populations in a southern Appalachian watershed before and after Hurricane Hugo. Trans. Am. Fish. Soc. 1994, 123, 668–678. [Google Scholar] [CrossRef]
  77. Virginia Department of Forestry. Virginia’s Forestry Best Management Practices for Water Quality; Technical Manual; Virginia Department of Forestry: Charlottesville, VA, USA, 2011.
  78. West Virginia Division of Forestry. Best Management Practices for Controlling Soil Erosion and Sedimentation from Logging Operations. Available online: https://wvforestry.com/wp-content/uploads/2024/07/Pub_SiviculturalBMP_DOC_DOF_20240719.pdf (accessed on 24 September 2025).
  79. Bassar, R.D.; Letcher, B.J.; Nislow, K.H.; Whiteley, A.R. Changes in seasonal climate outpace compensatory density-dependence in eastern brook trout. Glob. Change Biol. 2016, 22, 577–593. [Google Scholar] [CrossRef] [PubMed]
  80. Kanno, Y.; Pregler, K.C.; Hitt, N.P.; Letcher, B.H.; Hocking, D.J.; Wofford, J.B. Seasonal temperature and precipitation regulate brook trout young-of-the-year abundance and population dynamics. Freshw. Biol. 2016, 61, 88–99. [Google Scholar] [CrossRef]
  81. Petty, J.T.; Thorne, D.; Huntsman, B.M.; Mazik, P.M. The temperature-productivity squeeze: Constraints on brook trout growth along an Appalachian river continuum. Hydrobiologia 2014, 727, 15–166. [Google Scholar] [CrossRef]
  82. Santiago, J.M.; Rafeal, M.; Joaquin, S.; Diego, G.; Carlos, A.; Francisco, M.; Javier, P.; Monjo, R.; Ribalaygua, J. Waning habitats due to climate change: The effects of changes in streamflow and temperature at the rear edge of the distribution of a cold-water fish. Hydrol. Earth Syst. Sci. 2017, 21, 4073–4101. [Google Scholar] [CrossRef]
  83. Stranko, S.A. Brook trout declines with land cover temperature changes in Maryland. N. Am. J. Fish. Manag. 2008, 28, 1223–1232. [Google Scholar] [CrossRef]
  84. Childress, E.S.; Demarest, E.D.; Wofford, J.E.; Hitt, N.P.; Letcher, B.H. Strong variation in brook trout trends across geology, elevation, and stream size in Shenandoah National Park. Trans. Am. Fish. Soc. 2024, 153, 250–263. [Google Scholar] [CrossRef]
  85. Rahel, F.J.; Keleher, C.J.; Anderson, J.L. Potential habitat loss and population regimentation for cold water fish in the North Platte drainage of the Rocky Mountains: Response to climate warming. Limnol. Oceanogr. 1996, 41, 1116–1123. [Google Scholar] [CrossRef]
  86. Flebbe, P.A.; Roghair, L.D.; Bruggink, J.L. Spatial modeling to project southern Appalachian trout distributions in a warmer climate. Trans. Am. Fish. Soc. 2006, 135, 1371–1382. [Google Scholar] [CrossRef]
  87. Roberts, J.J.; Fausch, K.D.; Peterson, D.P.; Hooten, M.B. Fragmentation and thermal risk from climate change interact to affect persistence of native trout in the Colorado River basin. Glob. Change Biol. 2013, 19, 1383–1398. [Google Scholar] [CrossRef] [PubMed]
  88. Kovach, R.P.; Muhlfeld, C.C.; Al-Chokhachy, R.; Dunham, J.B.; Letcher, B.H.; Kershner, J.L. Impacts of climatic variation on trout: A global synthesis and path forward. Rev. Fish Biol. Fish. 2016, 26, 135–151. [Google Scholar] [CrossRef]
  89. Haak, A.L.; Williams, J.E.; Neville, H.M.; Daulwatler, D.C.; Colyer, W.T. Conserving peripheral trout populations: The values and risks of life on the edge. Fisheries 2011, 35, 530–549. [Google Scholar] [CrossRef]
  90. Robinson, W.A. Climate change and extreme weather: A review focusing on the continental United States. J. Air Waste Manag. Assoc. 2021, 71, 1186–1209. [Google Scholar] [CrossRef]
  91. Shearer, E.J.; Gorooh, V.A.; Nguyen, P.; Hsu, K.-L.; Sorooshian, S. Unveiling four decades of intensifying precipitation from tropical cyclones using satellite measurements. Sci. Rep. 2022, 12, 13569. [Google Scholar] [CrossRef]
  92. Wasko, C.; Rory, N.; Stein, L.; O’Shea, D. Evidence of shorter more extreme rainfalls and increased flood variability under climate change. J. Hydrol. 2021, 603B, 126994. [Google Scholar] [CrossRef]
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Figure 1. Study area showing the relative location within West Virginia, USA, and the location of the 25 headwater streams sampled annually for habitat between 2003 and 2020. Each stream supported naturally reproducing wild brook trout. Stream numbers above are identified in Table 1.
Figure 1. Study area showing the relative location within West Virginia, USA, and the location of the 25 headwater streams sampled annually for habitat between 2003 and 2020. Each stream supported naturally reproducing wild brook trout. Stream numbers above are identified in Table 1.
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Figure 2. Mean number of pools per stream from 2003 to 2020. Prior to 2009, streams were sampled every other year and annually thereafter. The black trend line shows the decline in pools over the entire series. The red circles and red line show declines in pool number prior to Superstorm Sandy in 2012. The green points (and trend line) show the increase in pools following Sandy.
Figure 2. Mean number of pools per stream from 2003 to 2020. Prior to 2009, streams were sampled every other year and annually thereafter. The black trend line shows the decline in pools over the entire series. The red circles and red line show declines in pool number prior to Superstorm Sandy in 2012. The green points (and trend line) show the increase in pools following Sandy.
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Figure 3. Mean pool area (m2 per stream) from 2003 to 2020. Prior to 2009, streams were sampled every other year and annually thereafter.
Figure 3. Mean pool area (m2 per stream) from 2003 to 2020. Prior to 2009, streams were sampled every other year and annually thereafter.
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Figure 4. Average nearest neighbor distance (meters) found between pools across 25 streams from 2003 to 2020. Prior to Superstorm Sandy in 2012 (filled circles), pools were declining and becoming further apart. Post-Sandy, (open squares) this trend was reversed. Dashed lines represent trends for periods pre- or post-Sandy.
Figure 4. Average nearest neighbor distance (meters) found between pools across 25 streams from 2003 to 2020. Prior to Superstorm Sandy in 2012 (filled circles), pools were declining and becoming further apart. Post-Sandy, (open squares) this trend was reversed. Dashed lines represent trends for periods pre- or post-Sandy.
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Table 1. Characteristics of 25 streams in east–central West Virginia, USA, used for habitat measurements from 2003 to 2020. Public lands are within the U.S. Forest Service Monongahela National Forest. Streams on private lands are within lands held by the Penn-Virginia Corporation (formerly MWERF, Mead Westvaco Ecosystem Forest). Within the Pottsville geological type are 5 streams receiving limestone sand to buffer downstream reaches and 5 without (unlimed) limestone sand additions.
Table 1. Characteristics of 25 streams in east–central West Virginia, USA, used for habitat measurements from 2003 to 2020. Public lands are within the U.S. Forest Service Monongahela National Forest. Streams on private lands are within lands held by the Penn-Virginia Corporation (formerly MWERF, Mead Westvaco Ecosystem Forest). Within the Pottsville geological type are 5 streams receiving limestone sand to buffer downstream reaches and 5 without (unlimed) limestone sand additions.
Stream NameHUC-10GeologyPublic/PrivateElevation (m)Drainage Area (km2)Mean Bankfull Width (m)
Big RunNFSBP RiverHampshirePublic (USFS)11553.823.85
Birch ForkMiddle Fork RiverPottsville-LimedPrivate (MWERF)8655.076.6
Block RunGreenbrier RiverChemungPublic (USFS)10237.346.24
Brushy RunNFSBP RiverMauch ChunkPublic (USFS)69718.656.86
Clubhouse RunGreenbrier RiverChemungPublic (USFS)9558.096.15
Crooked ForkUpper Elk RiverMauch ChunkPublic (USFS)10208.366.2
Elklick RunDry ForkHampshirePublic (USFS)61313.658.88
Elleber RunGreenbrier RiverChemungPublic (USFS)11295.576.23
Lick RunGreenbrier RiverChemungPublic (USFS)9272.584.37
Light RunMiddle Fork RiverPottsville-UnlimedPrivate (MWERF)7576.136.8
Little BranchCranberry RiverPottsville-UnlimedPublic (USFS)10751.994.66
Little Low PlaceNFSBP RiverHampshirePublic (USFS)9705.515.69
Long Run (MWERF)Middle Fork RiverPottsville-LimedPrivate (MWERF)7597.656.69
Long Run (Seneca)NFSBP RiverMauch ChunkPublic (USFS)69513.547.25
North Fork Panther RunMiddle Fork RiverPottsville-UnlimedPrivate (MWERF)7603.65.72
North Fork Red RunDry ForkMauch ChunkPublic (USFS)94213.8910.3
Panther RunMiddle Fork RiverPottsville-LimedPrivate (MWERF)7555.516.28
Poca RunGreenbrier RiverChemungPublic (USFS)10552.534.38
Roaring CreekNFSBP RiverMauch ChunkPublic (USFS)7656.325.71
Rocky RunMiddle Fork RiverPottsville-LimedPrivate (MWERF)8178.447.27
Sand/Red RunCranberry RiverPottsville-UnlimedPublic (USFS)10654.556.27
Schoolcraft RunMiddle Fork RiverPottsville-LimedPrivate (MWERF)7367.946.87
Seneca CreekNFSBP RiverHampshirePublic (USFS)11445.285.19
Sugar DrainMiddle Fork RiverPottsville-UnlimedPrivate (MWERF)8721.734.21
Whites RunNFSBP RiverHampshirePublic (USFS)72812.86.68
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Zacavish, Z.; Hartman, K. Long-Term Trends in Brook Trout Habitat in Appalachian Headwater Streams. Fishes 2025, 10, 512. https://doi.org/10.3390/fishes10100512

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Zacavish Z, Hartman K. Long-Term Trends in Brook Trout Habitat in Appalachian Headwater Streams. Fishes. 2025; 10(10):512. https://doi.org/10.3390/fishes10100512

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Zacavish, Zac, and Kyle Hartman. 2025. "Long-Term Trends in Brook Trout Habitat in Appalachian Headwater Streams" Fishes 10, no. 10: 512. https://doi.org/10.3390/fishes10100512

APA Style

Zacavish, Z., & Hartman, K. (2025). Long-Term Trends in Brook Trout Habitat in Appalachian Headwater Streams. Fishes, 10(10), 512. https://doi.org/10.3390/fishes10100512

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