Lake morphometry mediates the relationship between water color and fish biomass in small boreal lakes

The data are for an analysis of the influence of water color and lake depth on fish biomass small (1-10 ha) lakes in boreal Sweden.

AllBorealLakes.csv contains a list of surface areas (variable name hectares, given in hectares) for all lakes greater or equal to 1 hectare surface area in the boreal zone of Sweden. The original lake census comes from the Swedish government (Nisell et al. 2007) and lakes within the boreal zone were extracted based on the boreal zone boundary of Olson et al. (2001). There is also a lake ID number (FID_vivan_) used in the extraction.

SmallBorealLakes.csv contains a list of surface areas (variable name hectares, given in hectares) for all lakes greater or equal to 1 hectare surface area and less than or equal to 10 hectares in the boreal zone of Sweden. The original lake census comes from the Swedish government (Nisell et al. 2007) and lakes within the boreal zone were extracted based on the boreal zone boundary of Olson et al. (2001). There is also a lake ID number (FID_vivan_) used in the extraction.

SNILLE_ms_data.csv contains data on fish biomass for 16 small boreal lakes. The geographic coordinates (Northing and Easting)  are based on the Swedish Grid, see: http://www.lantmateriet.se. Lake surface areas based on the Swedish lake census (Nisell et al. 2007). Mean depth (meters) is based on echo sounding with an integrated GIS (Lowrance m52i). Volumes were calculated by calculating a triangulated irregular network and then mean depth subsequently calculated as volume divided by surface area. kd is the vertical light extinction coefficient (m^-1). We calculated  k_d from the slope of the linear regression of the logarithm of photosynthetically active radiation (measured with LI-COR LI-193 spherical quantum sensor) versus measurement depth (measured in approximately 0.5 meter intervals over the deepest part of the lake). The shallowest measure was excluded from the calculation. The values in the table are the average of kd calculated from three visits to each lake (once each approximately in June, July, and August 2014). kd is an indicator of colored dissolved organic carbon and water color (brownness) in this region and there is relatively little contribution of phytoplankton or inorganic particulate. CPUE Catch-per-unit-effort (kg wet weight / net) is an indicator of fish biomass. For each lake, we set 8 multi mesh gill nets (Nordic 12 nets, 30 x 1.5 m; Mesh sizes: 5, 6.25, 8, 10, 12.5, 15.5, 19.5, 24, 29, 35, 43, 55 mm) over one night (approximately 12 hours) in August 2014. Four nets were deployed in the littoral zone perpendicular to the shoreline. These nets were approximately equally spaced. Two floating nets were deployed across the deepest point of the pelagic zone, and two benthic nets were set in the hypolimnion near the deepest point of the lake. Net-specific catches were averaged with weighting based on the relative extent of the different habitat types (see Karlsson et al. 2015). Specifically, the profundal nets were assumed to represent the total hypolimnetic volume and the pelagic nets were assumed to represent the volume above the hypolimnion. The volume of the littoral nets was calculated by subtracting the volume of the pelagic and profundal habitats from the total lake volume. These weighted CPUE values are given in the file. Species identified through gill netting are abbreviated as: P for European perch (Perca fluviatilis), R for common roach (Rutilus rutilus), N for northern pike (Esox lucius), B for burbot (Lota lota)

Boreal_Area_kd_data.csv contains a list of estimated vertical light extinction coefficients (kd, m^-1) for lakes in boreal Sweden. Specifically, the values are based on water chemistry data from a national water quality survey conducted in Sweden every five years. Lake surface water (0.5 m) was sampled from above the deepest part of the lake during early autumn when the water column is mixed. Water quality analyses were performed using standard limnological techniques (detailed methods available on the internet at: http://www.slu.se/en/departments/aquatic-sciences-assessment/laboratories/geochemicallaboratory/water-chemical-analyses/) by a certified water analysis laboratory at the Swedish University of Agricultural Sciences. The data are freely available on the Internet at http://www.slu.se/vatten-miljo. Absorbance at 420 nm (D) which is a metric of water color (brownness) was used to calculate absorption coefficients per meter (a, m-1) from the initial measurement: a = (D * 2.303) / L. where L is the optical path length in meters, 0.05 in the case of the monitoring data. We then estimated kd (m^-1) based on the calibration curve reported by Seekell et al. (2015): = kd = 0.3121 + 0.1327a. These values were associated with surface areas from the Swedish lake census (Nisell et al. 2007) using a identification number common to both the Swedish water chemistry and lake census datasets. Finally, the file was trimmed to only include lakes with surface areas greater or equal to 1 hectare and less than or equal to 10 hectares.

References:

• Nisell, J., A. Lindsjö, and J. Temnerud (2007), Rikstäckande virtuellt vattendrags nätverk för flödesbaserad modellering VIVAN, [In Swedish], Rapport 2007:17, Institutionen för miljöanalys, SLU.
• Olson DM, Dinerstein E, Wikramanayake ED, Burgess ND, Powell GVN, Underwood EC, D’amico JA, Itoua I, Strand HE, Morrison JC, Loucks CJ, Allnutt TF, Ricketts TH, Kura Y, Lamoreux JF, Wettengel WW, Hedao P, Kassem KR (2001) Terrestrial ecoregions o the world: A new map of life on Earth. BioScience 51:933-938.

• Karlsson J, Bergström AK, Byström P, Gudasz C, Rodriguez P, Hein C (2015) Terrestrial organic matter input suppresses biomass production in lake ecosystems. Ecology 96:2870-2876. doi: 10.1890/15-0515.1

• Seekell DA, Lapierre JF, Karlsson J (2015) Trade-offs between light and nutrient availability across gradients of dissolved organic carbon concentration in Swedish lakes: Implications for patterns in primary production. Canadian Journal of Fisheries and Aquatic Sciences 72:1663-1671. doi: 10.1139/cjfas-2015-0187