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All issues No 423 (2022) Knowl. Manag. Aquat. Ecosyst., 423 (2022) 18 Full HTML
Issue
Knowl. Manag. Aquat. Ecosyst.
Number 423, 2022
Management of habitats and populations/communities
Article Number 18
Number of page(s) 12
DOI https://doi.org/10.1051/kmae/2022018
Published online 29 July 2022

© J.D. Olden et al., Published by EDP Sciences 2022

Licence Creative CommonsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License CC-BY-ND (https://creativecommons.org/licenses/by-nd/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. If you remix, transform, or build upon the material, you may not distribute the modified material.

1 Introduction

Lakes touch nearly all aspects of human society, acting as centers of organization within the landscape, offering countless cultural and ecological services, and supporting a rich diversity of biological life (Reynaud and Lanzanova, 2017; Tickner et al., 2020). Littoral zones − referring to benthic areas above the light compensation depth − are areas of high productivity and provide critical ecosystem functions important to aquatic and terrestrial organisms (Strayer and Findlay, 2010). These habitats support the exchange of energy, mass, and nutrients by coupling the benthic zone and the pelagic zone (Vander Zanden and Vadeboncoeur, 2020), and connecting terrestrial riparian areas to aquatic ecosystems (Francis and Schindler, 2009).

By providing physical structure and habitat complexity, macrophytes, coarse woody habitat (i.e., sticks, branches, and trees) and substratum in littoral zones play critical roles in lake ecosystems (Thomaz and Cunha, 2010; Cantonati and Lowe, 2014; Czarnecka, 2016). Submerged, floating-leaved and emergent macrophytes support primary and secondary production, and ultimately become the detritus that provides food and fuel to heterotrophic bacteria. Macrophytes provide important foraging habitats and protection against predation for macroinvertebrates and juvenile fish as well as spawning habitats for adult fish (Sass et al., 2019). Riparian vegetation contributes a continuous input of coarse woody debris into littoral zones that provide food sources and structurally complex habitats for benthic macroinvertebrates, surface area for periphyton growth, and opportunities for organic sediment retention (Christensen et al., 1996; Smokorowski et al., 2006; Brauns et al., 2007; Czarnecka, 2016). The long-standing recognition of the value of macrophytes and wood has supported the use of artificial structures over the past century to enhance both sport and commercial fisheries (Bolding et al., 2004).

Land use change can disrupt littoral zone integrity and threaten biodiversity and ecosystem services provided by lakes (Moore et al., 2003; Johnson et al., 2018; Costadone and Sytsma, 2022). Residential and recreational shoreline development includes the replacement of natural habitats with retaining walls and rip-rap, swimming beaches, and docks (Ostendorp et al., 2004; Strayer and Findlay, 2010; Kaufmann et al., 2014a). Wood is actively removed from shorelines, or its input reduced through the absence of trees and shrubs in the riparian zone (Marburg et al., 2006), resulting in strong negative associations between littoral coarse woody habitat and lakeshore residential development (e.g., Francis and Schindler, 2006; Twardochleb and Olden, 2016a). Similarly, macrophytes are actively managed in many lakes to improve swimming opportunities and boat access to the shoreline, and are impacted by boat traffic and wave action (Sagerman et al., 2020; Thiemer et al., 2021). Loss and modification of coarse woody habitat and macrophyte cover has widespread impacts on macroinvertebrate (Brauns et al., 2007; McGoff et al., 2013b; Porst et al., 2019), fish (Sass et al., 2006; Lewin et al., 2014; Matern et al., 2021) and water bird communities (Lindsay et al., 2002; Kaufmann et al., 2014b), with consequences that scale up to entire littoral food webs (Francis and Schindler, 2009; Brauns et al., 2011; Twardochleb and Olden, 2016b).

Habitat assessments of littoral zones remain fundamental to decision-making regarding lake management (Ostendorp et al., 2004; Rowan et al., 2006; Kaufmann et al., 2014c). Surveys of littoral habitat are used to support the development and calibration of biotic multimetric indices of ecological integrity (McGoff et al., 2013a; Miler et al., 2015). Monitoring of nearshore habitat also helps identify and prioritize management actions where low availability of macrophytes and woody habitats limit fisheries production. Similarly, quantifying invasive macrophyte distributions guide preventive management actions to reduce infestation levels and mitigate impacts on recreational watercraft, swimming, and waterfront property values (Eiswerth et al., 2000, Olden and Tamayo, 2014). Traditional approaches to littoral habitat assessments rely on data collected from field surveys, including multiple transect lines and/or quadrants from different point locations around the lake (Kaufmann et al., 2014a). Such approaches are labor-intensive and discrete in space, thus limiting their use to conduct whole-lake assessments of littoral habitat both efficiently and effectively.

Technological advances are offering new tools and approaches for the spatially-continuous mapping of littoral habitats in lakes. Breakthroughs in remote sensing from satellites, aircrafts, and unmanned aerial vehicles allow for monitoring above-water coverage of macrophytes and other physical structures. For example, drones and multispectral imagery have been used to compare the effectiveness of different control methods to reduce densities of invasive Eurasian watermilfoil (Brooks, 2020), and high-resolution aerial photographs used to quantify the spatial extent of docks on lake shorelines (Beck et al., 2013; Radomski et al., 2010). Side-scan sonar technology provides opportunities to map underwater lake features such as substrate and coarse woody habitat (Koeller, 2014), and more recently, autonomous surface vessels have been developed to monitor invasive macrophyte growth through imagery and collection of depth information (Codd-Downey et al., 2021). Underwater videography has also shown continued utility to estimate fish richness and abundance (Ebner and Morgan, 2013; Wilson et al., 2014; Hitt et al., 2021), behavior (Coghlan et al., 2017) and habitat use Davis et al. 1997 (Pratt et al., 2005), largely because it is cost-effective and more widely tractable. Its use to conduct spatially-continuous assessments of littoral habitat, to our knowledge, has not been fully explored.

The objective of this study was to demonstrate the ability to use simple, cost-effective underwater videography to conduct lake-wide spatially-continuous assessments of littoral habitat. We illustrate this approach for lakes across a gradient of shoreline and riparian development in northwestern United States. By mapping the areal coverage of macrophytes, coarse woody habitat, bottom substrates, and artificial structures in littoral zones we demonstrate the advantages of underwater videography over time-intensive traditional field surveys that are limited in their spatial extent. This study supports the growing necessity of rapid and high-resolution monitoring of nearshore habitats in lakes and reservoirs, thus helping inform management efforts seeking to prevent the removal, or conversely optimizing the enhancement, of littoral habitat to benefit fish and freshwater ecosystems.

2 Methods

2.1 Study system

We surveyed 9 lakes in the Puget Sound lowlands of Washington State, USA, spanning a gradient of shoreline and riparian development (Fig. 1a, Tab. 1). Undeveloped lakes have restricted public access, no residential dwellings (0 residences km−1), largely intact riparian canopy of native evergreen and less abundant deciduous trees, and natural shorelines (Fig. 1b). Lakes of moderate development have intermediate densities of residential buildings (10–30 residences km−1), riparian vegetation largely consisting of deciduous trees, shrubs and grass lawns, and mixed natural-impacted shorelines with a low-moderate density of docks (15–20 docks km−1) (Fig. 1c). Highly developed lakes are largely surrounded by residential buildings (>30 residences km−1), riparian areas characterized by open space, ornamental gardens and grass lawns, non-native shrubs, and native deciduous trees that typically outnumber evergreen tree species, and artificially manicured shorelines with armored banks and a high density of docks (>30 docks km−1) (Fig. 1d). Residential density (# primary dwellings km−1) and dock density (# docks km−1) along lake shorelines were calculated using tax parcel information and 2017 aerial photographs provided by King County (https://kingcounty.gov/services/gis/Maps/parcel-viewer.aspx).

thumbnail Fig. 1

(a) Location of the 9 study lakes in western Washington, USA, with photo examples of shoreline condition for (b) undeveloped (Walsh Lake), (c) moderately developed (Lake Geneva), and (d) highly developed lakes (Steel Lake). All photos by Julian Olden.
Table 1

Study lake attributes organized according to increasing levels of human development (shoreline residence density).

2.2 Habitat survey using underwater videography

Underwater habitat surveys were performed during the temperate summer (24 May–15 June 2018). Video recordings were captured with a 360° high-definition video camera (GoPro Inc. Camera Fusion 360) mounted inside a waterproof acrylic ball that was attached to a pole and fastened 1-m below the surface of the water from the gunnel of a motor-powered canoe ( Fig. 2). The pole was tilted at a 20° angle (from vertical) to reduce water flow resistance and to optimize video capture by ensuring the lake bottom was in the center of the aperture. The canoe was powered using a Minn KotaTM (40-lb thrust) 12-V transom mount trolling motor with a standard 3-1/4" diameter propellor. Surveys consisted of circumnavigating the entire perimeter of each study lake with the canoe at a constant speed of 2 km · hour−1 and at a water depth of approximately 3 m to ensure a consistent field of view. Maintaining the target water depth required maneuvering around docks and fallen trees, which was only possible when structures were separated by at least 2 m. Habitat assessment did not occur when achieving the target water depth was not possible.

Video records were captured in 3.2K and converted to a time lapse video consisting of photos taken at a rate of one every two seconds. Using time lapse is beneficial for two reasons. First, it allowed for estimating the surveyed area for each photo given the determination of the field of view and a constant canoe speed. This was accomplished by estimating the field of view at the time of the survey by taking a video recording of a 10-m weighted white rope (with 1-m intervals delineated with black marks) placed at a depth of 3 meters, and estimating the distance visible. Second, time lapse facilitates easy georeferencing by time synchronizing each photo to a geographic co-ordinate using a GPS unit (Garmin Oregon 600) in QGIS (2020). The video camera and GPS unit were synchronized, and time stamps used to link observations, as opposed to the two units being connected.

thumbnail Fig. 2

Underwater videography apparatus depicting the canoe with high-definition video camera (GoPro Inc. Camera Fusion 360) mounted inside a waterproof acrylic ball attached to a pole and fastened to the gunnel.

2.3 Video processing and habitat classification

The video camera consists of two 18-megapixel lenses each capturing images at a 180° wide angle, and one focused upwards at the water surface and another downwards at the lake bottom. The rendering process involved stitching the two videos together to produce a seamless 360° video using the GoPro Fusion Studio software. This process is known to create minor image noise where the two videos are stitched together, however the 20° positioning of the camera ensured that any slight distortion occurred close to the water surface. When needed, video quality was improved using the brightness, exposure, saturation, vibrance, picture noise and sharpen tools available in Adobe Premiere Pro software. For lakes exhibiting relatively higher turbidity (Tab. 1), Adobe Photoshop and Lightroom were used to improve the video's quality, including the Smart Sharpen filter and Edges Mask using the Canny Edge detection algorithm; both are standard procedures.

Time-lapse photos were extracted from the video at a two-second internal, resulting in 1500–3500 pictures taken in each lake. Collections of 5 pictures representing a transect distance of 7.5 m (at a speed of 2 km · hour−1) were examined to estimate proportional areal coverage for the following habitat variables (Fig. 3):

  • Short submerged macrophytes (<5–10 cm from substrate surface) that were completely growing under water with roots attached to the substrate or without any root system.

  • Tall submerged macrophytes (>5–10 cm from substrate surface) that were completely growing under water with roots attached to the substrate or without any root system.

  • Floating-leaved macrophytes with root systems attached to the substrate and with leaves that float on the water surface.

  • Coarse woody habitat defined as entirely or partially submerged wood pieces with diameter ≥10 cm and length ≥1.5 m for practicality of identification.

  • Benthic substrate composition using the modified Wentworth scale of mud (<0.5 mm), sand (0.5–2 mm), pebble (2–64 mm), cobble (64–256 mm), and boulder (>256 mm).

  • Artificial structures, referring predominantly to docks pilings, with the rare instances of underwater mats and other sunken objects.

thumbnail Fig. 3

Example time-lapse photos extracted from continuous underwater video of Pine Lake used to quantify proportional areal coverage of (a) short submerged macrophytes (<5 cm from substrate surface, (b) tall submerged macrophytes (>5 cm from substrate surface), (c) floating-leaved macrophytes, (d) coarse woody habitat, (e) benthic substrate, and (f) artificial structures (i.e., predominantly dock pilings).

2.4 Statistical analyses

Summary statistics and correlation analysis were used to explore patterns in the areal coverage of macrophytes, coarse woody habitat, bottom substrates, and artificial structures (docks) in the littoral zones of the study lakes. ArcGIS was used for georeferencing the dataset and producing maps. Rstudio (v. 1.3.1093), an integrated development environment for R (v. 4.0.2), was used to conduct all analyses and produce visual supports (with the following additional packages: “ggplot2”, “ggthemes”, “dplyr”, “plotly”).

3 Results

Underwater videography allowed for the spatially-continuous quantification of littoral zone habitat across a series of lakes, and revealed considerable within- and across-lake variation in the distribution of habitat types along a gradient of shoreline development. Shorelines of less developed lakes such as Walsh L., Padden L., and North L. ( Fig. 4a–c) were dominated by near contiguous macrophyte beds around lake perimeter, whereas littoral zones of moderate- and highly-developed lakes such as Wilderness L., Geneva L., Pine L., and Steel L. (Fig. 4d, e, f, i) were characterized by patchily-distributed macrophyte beds. Coarse woody habitat was similarly variable in space, being either located in isolated patches (e.g., Walsh L., Killarney L., and Star L.: Fig. 4a, g, h), or found across larger swathes of the shoreline (e.g., Wilderness L. and Steel L.: Fig. 4d, i).

Total areal coverage of macrophytes, coarse woody habitat, bottom substrates, and artificial structures varied substantially across the study lakes ( Fig. 5). Short submerged macrophytes (e.g., bladderwort: Utricularia spp.; and rootless, macrophytic algae, Nitella spp. and Chara spp.), tall submerged macrophytes (e.g., big-leaf pondweed: Potamogeton amplifolius; common waterweed: Elodea canadensis; Eurasian watermilfoil: Myriophyllum spicatum) and mud substrates were generally the most prevalent. By contrast, floating macrophytes (e.g., fragrant waterlily: Nymphaea odorata; pink waterlily: Nymphaea spp.; and yellow waterlily: Nuphar polysepala), coarse wood, pebble, and cobble substrates were less common. The proportional areal extent of short submerged macrophytes tended to increase, whereas tall submerged macrophytes and floating macrophytes decreased, with increasing shoreline development ( Fig. 6a–c). For example, Star L. (high shoreline development) was dominated by submerged macrophytes that was largely comprised of Eurasian watermilfoil, an invasive species that reaches high levels of plant material growing under or near the water surface (Figs. 4h and 5).

Coarse woody habitat showed a strong negative association with shoreline residential density, where more urbanized lakes were almost completely devoid of wood (Fig. 6d). Wilderness L. was the one notably outlier from this general relationship, where much higher coarse woody habitat was estimated than expected based on shoreline development (Figs. 5 and 6d). Interestingly, this lake is densely populated on its western shoreline whereas is it completely undeveloped and supports a heavily wooded riparian zone on its eastern shoreline (Fig. 4d). Finally, the proportion of shorelines with artificial structures (docks) increased markedly with shoreline residential density (Fig. 6e), whereas benthic substrate composition showed no associations (Fig. 6f–i).

thumbnail Fig. 4

Spatially-continuous maps of macrophytes (combined submerged and floating-leaved macrophytes) and coarse woody habitat for (a) Walsh Lake, (b) Padden Lake, (c) North Lake, (d) Wilderness Lake, (e) Lake Geneva, (f) Pine Lake, (g) Lake Killarney, (h) Star Lake, and (i) Steel Lake. Lines representing coarse woody habitat are thinner compared to the lines representing macrophytes coverage, allowing both habitat types to be simultaneously mapped.
thumbnail Fig. 5

Total shoreline extent of habitat categories for the study lakes presented from lowest (left) to highest (right) shoreline development (residence density).
thumbnail Fig. 6

Lake-wide relationships between proportional habitat coverage and shoreline development (residence density) according to (a) short submerged macrophytes, (b) tall submerged macrophytes, (c) floating-leaved macrophytes, (d) coarse woody habitat, (e) artificial structure, (f) mud substrate, (g) sand substrate, (h) pebble substrate, and (i) cobble substrate. Pearson-moment correlation coefficients (R) and significant levels (* P < 0.05, ** P < 0.01, *** P < 0.001) are presented.

4 Discussion

Physical habitats in lake littoral zones provide an exhaustive list of critical ecosystem functions, including mediating light, temperature, and nutrient dynamics, and support important foraging and refuge areas for macroinvertebrates, fishes and water birds (Strayer and Findlay, 2010). This study demonstrated the promise of underwater videography to map lake-wide littoral habitat, providing spatially continuous estimates of submerged and floating macrophytes, coarse woody debris, substrate composition, and artificial structures. Data can be obtained rapidly and at relatively low cost, and provides a permanent record of habitat conditions that can be used to monitor trends over time. Spatially-continuous mapping of littoral habitat has many practical applications for resource managers seeking to prevent the loss of native plants, control the infestation of invasive plants, or optimize the enhancement of coarse woody habitat to benefit freshwater ecosystems.

In support of the underwater videography approach, estimation of littoral zone habitat pointed to negative associations between shoreline development, measured by residential density, and the extent of tall submerged and floating macrophytes. Past studies have demonstrated that the lakeshore homeowners commonly remove floating macrophytes to improve aesthetic enjoyment, swimming, and fishing opportunities (Sagerman et al., 2020; Thiemer et al., 2021). Similarly, littoral extent of coarse woody habitat demonstrated marked decreases with lakeshore residential development; a result also supported by the literature (e.g., Christensen et al., 1996; Jennings et al., 2003; Francis and Schindler, 2006). Wood is actively removed from shorelines to provide boat access, or its input reduced through the removal of trees and shrubs in the riparian zone to improve lake views (Marburg et al., 2006). Our underwater estimates of artificial structure, predominantly docks, ranged between 1 and 2% of the littoral zone; a value comparable to other regions using aerial photographs such as Minnesota lakes where 3% of the littoral zone were estimated to be impacted by docks (Radomski et al., 2010).

The method of underwater videography offers several advantages compared with other physical habitat survey methods (Ostendorp et al., 2004; Rowan et al., 2006; Wehrly et al., 2012). Underwater videography supports comparatively inexpensive physical habitat surveys that do not require highly specialized knowledge when analyzing the photos collected in the field (unless plants are identified to species). This is in contrast to the processing of aerial photography data to estimate physical habitat, used extensively in the United States and Europe, which necessitates experts with data science and GIS skills, and is limited to above water assessments (Ostendorp et al., 2004; Ostendorp and Ostendorp, 2015; Wehrly et al., 2012). Other widely used physical habitat survey methods are based almost exclusively on field data collection by boat or from the shore, such as the Lake Habitat Survey in the United Kingdom (Rowan et al., 2006), the Lake Shorezone Functionality Index in Italy (Siligardi et al., 2010), the Lakeshore Modification Index in Slovenia (Peterlin and Urbani, 2012), and the National Lakes Assessment in the United States (Kaufmann et al., 2014c). These more traditional methods are restricted to measuring floating-leaved and emergent macrophytes at or above the water surface, therefore the characteristics of underwater littoral habitats can only be evaluated to a limited depth, for example, with the assistance from an underwater viewer with a small field of view and often poor visual determination. The capture of below water habitats, such as bottom substrates, submerged wood, and submerged aquatic plants, is similarly limited by aerial photography (Ostendorp et al., 2004; Ostendorp and Ostendorp, 2015).

Perhaps the biggest advantage of underwater videography is that the estimation of littoral habitats can be conducted at fine spatial grains across broad spatial extents, offering a continuous assessments at lake-wide scales. Habitat surveys can also be accomplished in a relatively efficient and cost-effective manner. For our study lakes (shoreline perimeter: 1.6–3.9 km), field data collections typically took approximately 3 hours to complete, including the time required to launch the boat, setup the apparatus, and circumvent the shoreline to capture underwater video. Back in the office, video processing and habitat classification required an additional 12 h, on average, resulting in 15 total hours to complete a spatially continuous littoral zone assessment. The cost of the waterproof video camera and GPS unit did not exceed $500 USD, making the technique relatively affordable.

Although habitat assessments using underwater videography offer a number of distinct advantages, we recognize that key challenges also exist. First, avoiding the necessary extensive point sampling needed for whole-lake assessments reduces time in the field, but is replaced with time required to process video and for a trained interpreter to manually classify habitat types. Time required for visual classification will vary depending on the size of the waterbody and whether species or functional forms (such as in our study) are being assigned. The issue of inter-interpreter reliability should be considered in the future. Much like how computer vision algorithms are being explored for macrophyte classification from satellite and drone multispectral images (Husson et al., 2017; Milas et al., 2017), automated image analysis is also possible for underwater videography. In contrast to visual classification, digital classification that involves color quantization of underwater images would allow for rapid estimation of major habitat types, such as distinguishing macrophytes from wood and substrate. The utility of using color spectra was recognized a long time ago for delineating floating macrophytes beds from aerial photos (Marshall and Lee, 1994). Second, various lake factors will influence the effectiveness of underwater videography, such as water transparency and bathymetry, which make sonar-based approaches potentially more effective. The water column must be sufficiently transparent, and thus the lake bottom illuminated and within the depth-of-view for the proposed technique to be viable. Third, mapping the distribution of emergent macrophytes is not possible, although this limitation could be addressed by simultaneously capturing above-water video during the surveys. Additional refinements to the approach presented here will only further strengthen the use of underwater videography in lake littoral habitat assessments.

Spatially-continuous mapping of littoral habitat has many practical applications for lake resource managers. Management strategies seeking to restore native macrophytes or control infestations of invasive species rely on extensive mapping and monitoring of plant species. For example, lake-wide monitoring is effective to compare physical, chemical and biological control strategies for invasive macrophytes (e.g., Codd-Downey et al., 2021). Wood additions have become a popular littoral zone habitat enhancement tool, and there remains a need to better understand the spatial distribution of coarse woody habitat and prioritize areas of enhancement to benefit fish production (Sass et al., 2019). Similarly, substrate mapping is critical given that substrate-spawning fish species are common in lakes, and many species of commercial and recreational fisheries use specific substrates for reproduction. For example, lake-dwelling walleye (Sander vitreus) prefer to spawn on gravel versus sand areas (Johnson, 1961), yellow perch (Perca flavescens) spawn in cobble substrate when rooted macrophytes are not available (Robillard and Marsden, 2001), and smallmouth bass (Micropterus dolomieu) construct nests in gravel-cobble substrates (Warren, 2009). Various techniques are currently being used to create or enhance spawning habitat for substrate spawning fish in temperate climate regions (Taylor et al., 2017).

Underwater videography represents a relevant tool for environmental monitoring due to the temporal (high frequency to produce information) and spatial (broader areas) resolutions in which habitat data is collected, and that it provides a permanent record of habitat conditions that can be used to monitor trends over time. This makes it a valuable approach to quantify shifts in the extent and composition of littoral zone habitats in response to climate-induced drought that draws down lakes and dam operations that lead to fluctuations in reservoir water levels (Carmignani and Roy, 2017). The relative ease of data collection also opens new opportunities for rapid littoral zone assessments conducted by citizen science groups or lake associations who could share equipment. In summary, underwater videography complements both traditional and remote-sensing approaches to littoral assessments by allowing for spatially-continuous habitat mapping that is both cost-effective and relatively easy to implement.

Author contributions

JDO conceived and designed the study, JDO and AB collected the data, AB processed the videos and conducted the habitat classification, JDO analyzed the data, JDO and OM wrote the manuscript.

Acknowledgements

We are grateful for helpful comments from an anonymous reviewer. J.D.O. was supported by the Richard C. and Lois M. Worthington Endowed Professor in Fisheries Management from the School of Aquatic and Fishery Sciences, University of Washington.

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Cite this article as: Olden JD, Miler O, Bijaye A. 2022. Lake-wide mapping of littoral habitat using underwater videography. Knowl. Manag. Aquat. Ecosyst., 423, 18.

All Tables

Table 1

Study lake attributes organized according to increasing levels of human development (shoreline residence density).

In the text

All Figures

thumbnail Fig. 1

(a) Location of the 9 study lakes in western Washington, USA, with photo examples of shoreline condition for (b) undeveloped (Walsh Lake), (c) moderately developed (Lake Geneva), and (d) highly developed lakes (Steel Lake). All photos by Julian Olden.
In the text
thumbnail Fig. 2

Underwater videography apparatus depicting the canoe with high-definition video camera (GoPro Inc. Camera Fusion 360) mounted inside a waterproof acrylic ball attached to a pole and fastened to the gunnel.
In the text
thumbnail Fig. 3

Example time-lapse photos extracted from continuous underwater video of Pine Lake used to quantify proportional areal coverage of (a) short submerged macrophytes (<5 cm from substrate surface, (b) tall submerged macrophytes (>5 cm from substrate surface), (c) floating-leaved macrophytes, (d) coarse woody habitat, (e) benthic substrate, and (f) artificial structures (i.e., predominantly dock pilings).
In the text
thumbnail Fig. 4

Spatially-continuous maps of macrophytes (combined submerged and floating-leaved macrophytes) and coarse woody habitat for (a) Walsh Lake, (b) Padden Lake, (c) North Lake, (d) Wilderness Lake, (e) Lake Geneva, (f) Pine Lake, (g) Lake Killarney, (h) Star Lake, and (i) Steel Lake. Lines representing coarse woody habitat are thinner compared to the lines representing macrophytes coverage, allowing both habitat types to be simultaneously mapped.
In the text
thumbnail Fig. 5

Total shoreline extent of habitat categories for the study lakes presented from lowest (left) to highest (right) shoreline development (residence density).
In the text
thumbnail Fig. 6

Lake-wide relationships between proportional habitat coverage and shoreline development (residence density) according to (a) short submerged macrophytes, (b) tall submerged macrophytes, (c) floating-leaved macrophytes, (d) coarse woody habitat, (e) artificial structure, (f) mud substrate, (g) sand substrate, (h) pebble substrate, and (i) cobble substrate. Pearson-moment correlation coefficients (R) and significant levels (* P < 0.05, ** P < 0.01, *** P < 0.001) are presented.
In the text

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