Relationship between plantation forest and brown trout growth, energetics and population structure in peatland lakes in western Ireland

Forest Ecology and Management 321 (2014) 71–80

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Relationship between plantation forest and brown trout growth, energetics and population structure in peatland lakes in western Ireland Conor T. Graham ⇑, Thomas J. Drinan, Simon S.C. Harrison, John O’Halloran University College Cork, School of Biological, Earth & Environmental Sciences, Distillery Fields, North Mall, Cork, Ireland

a r t i c l e

i n f o

Article history: Available online 1 August 2013 Keywords: Salmo trutta Ireland Fish growth Peatland conifer plantation Population structure Eutrophication

a b s t r a c t Much of the conifer plantation forest in Ireland is on peat soils. Plantations on this soil type are known to pose the greatest risk to degrading water quality by increased sedimentation, acidification and heavy metal accumulation. Peat soils are also known to leach phosphorus (P), nitrogen (N) and dissolved organic carbon (DOC) as a result of forestry operations. In moderation, such nutrient enrichment may have positive trophic impacts in oligotrophic freshwater systems such as those typical of peat catchments in western Ireland. In this study, the water chemistry and brown trout (Salmo trutta) populations of peat-land lakes were investigated to assess the associations of conifer plantation forest on the growth, energetics and population structure of brown trout. We conducted this study over a three-month period in the summer of 2010 by comparing brown trout populations and water chemistry in lakes with three distinct catchment land uses: (i) unplanted blanket bog, (ii) moderate levels of conifer plantation forest (30–40% of catchment afforested), and (iii) high levels of conifer plantation forest (80–90% of catchment afforested). Changes in hydrochemistry associated with conifer plantations resulted in elevated concentrations of N, P and DOC, but no change to pH, with increasing levels of plantation forest within the catchment. Whereas there was no consistent trend in brown trout density between land uses, highest densities were recorded in lakes with afforested catchments. Trout populations in lakes with afforested catchments were dominated by younger fish, primarily 1+ (year old) and 2+ (2 year old) individuals with some 0+ (young of the year) trout present, compared to control lakes, which were largely dominated by 2+ and 3+ (3 year old) individuals. Older age classes of trout had larger body sizes in lakes with high levels of plantation forest within the catchment relative to the other lakes, indicating higher empirical growth rates, likely due to the trophic enrichment effects of forestry. Brown trout specific growth models that incorporate the potential confounding influence of different temperature regimes, showed no consistent relationship between growth and forest cover over the study period. Food consumption models indicated that trout in all sites were energetically challenged during the summer when sampling took place. Discrepancies between the observed body size and estimated growth of trout in lakes may potentially be due to (a) a significant amount of growth occurring outside of the summer study period and/or (b) unusually elevated temperature regimes during the study period, particularly in the afforested sites. No negative impacts of conifer plantation forests on brown trout populations were recorded. However, forest managers may wish to minimise felling coupe within peatland plantations as felling operations may exacerbate nutrient and/or heavy ion input into aquatic systems in such catchments. Ó 2013 Elsevier B.V. All rights reserved.

1. Introduction In the last few decades, Ireland’s landscape has experienced a very high rate of commercial afforestation, such that plantation forests established in the last 60 years now account for approximately 10% of total land cover. Over 90% of total forest cover in Ireland is comprised of plantation forest, more than in any EU ⇑ Corresponding author. Tel.: +353 (0)21 4904617; fax: +353 (0)21 4904664. E-mail address: [email protected] (C.T. Graham). 0378-1127/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.foreco.2013.06.057

member state except Malta (Forest Europe et al., 2011). Plantation forest practises are recognised as a potential source of pollution to associated freshwater systems and represent a considerable risk to the ecological status of surface waters (Rask et al., 1998; Steedman, 2000; Giller and O’Halloran, 2004). While a majority of studies have focused on surface water acidification associated with conifer plantation forests (Harriman and Morrison, 1982; Ormerod et al., 1989; Kelly-Quinn et al., 1996a; Cosby et al., 2001), more recent research has demonstrated the potential for plantation forest mediated eutrophication of freshwater systems, primarily through

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phosphorus fertilisation at the planting stage (Miller et al., 1996; Cummins and Farrell, 2003b; Foy and Bailey-Watts, 2007; Drinan et al., 2013b). Forestry practises have been shown to lead to enhanced concentrations of nutrients leaching into aquatic systems (Cummins and Farrell, 2003b; Rodgers et al., 2010; Drinan et al., 2013b). The capacity of catchment soils to retain phosphorus is a considerable determinant of phosphorus loadings to freshwater systems (Cummins and Farrell, 2003b). In Ireland, approximately 42% and 77% of state and privately owned forests, respectively, are located on peat soils (NFI, 2007), resulting in 27% of Irish blanker bog now being afforested (Conaghan, 2000). Forests planted on this soil type are considered to pose the greatest risk to water quality (Hutton et al., 2008). Peat soils are known to leach phosphorus, nitrogen and dissolved organic carbon as a result of forest operations (Cummins and Farrell, 2003a, 2003b; Renou-Wilson and Farrell, 2007; Rodgers et al., 2010) and peat soils have a very low capacity to absorb and retain phosphorus in particular (Cuttle, 1983). The application of phosphorus fertilisers to forests on peat bogs therefore may pose a considerable risk to freshwaters, particularly considering the inherently oligotrophic status of water bodies in peatland areas (Cummins and Farrell, 2003b; McElarney et al., 2010; Rodgers et al., 2010). The source of leached nitrogen from afforested peatlands into water bodies is predominantly from the peat soil through mineralisation of soil organic matter (Nieminen, 1998, 2003), while dissolved organic carbon inputs are generally as a result of canopy leachate from living trees and litterfall (Qualls et al., 1991; Fröberg et al., 2007) and the decomposition of the organic matter of the forest peat soil (Michalzik et al., 2001). The impact of commercial plantation forest operations on aquatic ecology has been investigated to a greater extent in lotic than in lentic habitats. However, plantation forest-mediated chemical effects on rivers may be somewhat difficult to discern due to the pulsed nature of these inputs which are concentrated at times of major disturbance, particularly planting, thinning and harvesting (Giller and O’Halloran, 2004). Concentrations of instream plant nutrients and other forest-derived materials are highly dependent on rainfall which not only washes chemicals into streams and rivers but also dilutes and flushes them, further exacerbating the difficulty of determining the scale of plantation forest inputs. As a consequence, many studies investigating the impact of hydrochemical change on aquatic biota have found little or no impact of forest operations (Liljaniemi et al., 2002; McKie and Malmqvist, 2008; Heino, 2009). However, dissolved and particulate substances tend to accumulate in downstream lakes which act as nutrient sinks due to their enhanced nutrient cycling and internal loading in comparison to streams (Søndergaard et al., 2003). Lakes may therefore be more suitable systems to assess chemical fluxes associated with forestry operations within a catchment (Drinan et al., 2013b). Studies investigating the relationships of plantation forests on the ecology of aquatic systems have reported a wide range of results including slight and short lived changes in species assemblages (Rask et al., 1998; Patoine et al., 2000; Planas et al., 2000) whereas other show increases in primary production (Rask et al., 1998; Planas et al., 2000; Prepas et al., 2001) and significant impacts on invertebrate communities (Jutila et al., 1998; Rask et al., 1998; Köster et al., 2005) and fish (Jutila et al., 1998; Rask et al., 1998) although there are relatively few studies investigating impacts of forest practises on the latter (Northcote et al., 2004). Conifer plantation forest acidification of aquatic habitats has received greater research focus compared to other issues concerning forest-surface waters interactions (Nisbet, 2001). Canopy interception of airborne pollutants is considered the main process by which coniferous plantations contribute to the acidification of

freshwaters (Reynolds et al., 1994). Low pH levels and elevated levels of labile monomeric aluminium are known to impoverish fish communities (Driscoll et al., 1980) and the early life stages of fish, including salmonids, are highly sensitive to acidification (Kelly-Quinn et al., 1993; Sayer et al., 1993). The distribution and density of brown trout (Salmo trutta L.) have been shown to be negatively impacted by forest associated acidification (Stoner et al., 1984; Rees and Ribbens, 1995; Kelly-Quinn et al., 1996b). Salmonids have been shown to be negatively impacted by nutrient enrichment and eutrophication-mediated declines of salmonid populations have been documented in both lentic (Colby et al., 1972; Persson et al., 1991) and lotic environments (Eklöv et al., 1998). Negative effects of eutrophication on salmonids are usually attributed to two main processes: deterioration of water quality conditions, and nutrient enrichment-mediated alterations in competitive balance with other taxa of fish. Anoxia, arising through either the respiration of excessive algal and plant growths at night, and/or due to microbial decay of organic matter is generally thought to be the critical factor through which eutrophication impacts salmonids (Eklöv et al., 1998, 1999), although eutrophication associated sedimentation of spawning gravels is known to reduce survival of eggs and alevins (Bagliniere and Maisse, 1990; Crisp, 1996; Jutila et al., 1998). Both Colby et al. (1972) and Persson et al. (1991) showed that fish communities of lakes can switch from dominance by coregonids and salmonids to dominance by percids and, finally, cyprinids, as a result of progressive eutrophication. Sustainable land management aims to ensure that ecosystems are not altered by human activities to such a degree that it impairs function. Management alternatives are informed by quantification of ecosystem responses to changes in land use (Stephenson and Morin, 2009). However, studies of anthropogenic impacts on lake water chemistry and biota generally involve evaluating the relative impact of multiple stressors and it can prove difficult to evaluate the impact of one particular element (Dodson et al., 2005) such as forests or their operations on aquatic biota, due to background environmental perturbation (Paterson et al., 1998). However, recent studies in homogenous and otherwise undisturbed blanket bog lakes, have demonstrated a clear deleterious impact of conifer plantations on the water quality of lakes in blanket bog catchments, with forests and their operations mediating elevated levels of nitrogen, phosphorus and dissolved organic carbon (Drinan et al., 2013b), resulting in alterations to Chydoridae communities (Drinan et al., 2013c), aquatic macroinvertebrate assemblages and conservation value of these systems (Drinan et al., 2013a). Our study objective was to investigate relationships between lake hydrochemical conditions and brown trout population biology in two non-randomly selected lakes within each of three land-use treatments at catchment scales: blanket bog reference conditions, and bog catchments converted to low and high levels of plantation forest.

2. Methods 2.1. Site selection and description Eighteen potential study lakes were identified based on geographic location, size, soil type, geology and catchment land use in areas of blanket bog throughout western Ireland using ArcGIS (ESRI ArcMap v.9.3). Blanket bog is an area of peatland, formed where there is a climate of high rainfall and a low level of evapotranspiration, allowing a peat layer of variable depth to develop over large expanses of ground. All candidate lakes were approximately 10–35 ha in area, on lowland blanket bog overlying igneous (granite) geology with minimal human impact within their

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catchments other than plantation forest operations. Following hydrochemical assessment of these eighteen lakes using analyses of water chemistry parameters shown to be impacted by forestry operations (Drinan et al., 2013b) including nitrogen, phosphorus, dissolved organic carbon, aluminium and chlorophyll a concentrations, six sites (Fig. 1) were selected for this study. These sites were selected to be representative of the lakes within this geographical area, with two lakes in catchments with no afforestation (Controls: C1 & C2), two with medium (M1 & M2) and two with high (H1 & H2) forest levels in their catchment (Table 1). We selected the study sites on these basis so as to assess the association of these water chemistry parameters, shown to be impacted by forestry activities (Drinan et al., 2013b), with brown trout growth, energetics and population structure in lakes with typical afforested catchments in this area of blanket bog. There was no afforestation within the catchment of the low nutrient (control) lakes, approximately 30–40% catchment afforestation of the medium level and 80–95% catchment afforestation of the high forest level lakes. The areas of lake catchments that were not afforested consisted of unimpacted blanket bog peatland. Plantations consisted of first-rotation exotic conifers,

primarily of Sitka spruce (Picea sitchensis Bongard) with smaller proportions of lodgepole pine (Pinus contorta Douglas ex Louden). The plant species surrounding the control lakes and the unforested areas of the medium and high forest lakes were typical blanket bog species (Fossitt, 2000), such as purple moor-grass (Molinia caerulea (Linnaeus)), cross-leaved heath (Erica tetralix Linnaeus), deergrass (Scirpus cespitosus Linnaeus), common cottongrass (Eriophorum angustifolium Roth), bog asphodel (Narthecium ossifragum (Linnaeus) Hudson) and white beak-sedge (Rhynchospora alba (Linnaeus) Vahl). Further details of the study lakes are provided in Table 1. 2.2. Water chemistry sampling Conductivity, dissolved oxygen and temperature were measured in lake using WTW portable meters: WTW 330i Conductivity meter and a WTW 330 Oxygen meter. Both meters were calibrated prior to each sampling occasion. Water samples were taken from the littoral zone of each lake in April, June, July and September 2010 at a depth of approximately one meter. Water samples were collected in acid-washed polypropylene bottles, stored at 4 °C in a

Fig. 1. Map of Ireland showing the location of the six study lakes.

Table 1 Geographic location, physical characteristics and proportion of catchment afforested in the six study lakes in western Ireland. Lake

Latitude/ longitude

Altitude (m)

Lake area (ha)

Lake mean depth (m)

C1 (Lough Nuala)

N 53°150 58.6 W 009°200 56.2 N 53°150 49.4 W 009°280 33.3 N 53°180 59.6

65

34.5

6.2

67

10.5

98

C2 (Lough Adooraun) M1 (Lough Shliabh an Aonaigh) M2 (Loch na nArd Doiriu) H1 (Seecon Lough) H2 (Lough Naweelen)

W 009°150 59.4 N 53°190 49.9 W 009°180 12.8 N 53°210 37.1 W 009°220 33.8 N 53°200 26.9 W 009°180 25

Catchment area (ha)

Catchment afforested (%)

Plantation age (years)

315

0

–

3.0

83

0

–

15.8

2.6

230

40.6

32–34

64

16.8

6.2

1629

32.5

28–31

105

26.6

2.2

876

81.3

24–34

74

15.8

4.9

512

96

29–37

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cooler box and transported to the laboratory for analysis within 24 h after collection. A further 12 water chemistry parameters were analysed in the laboratory. pH was determined using a WTW pH meter (pH330i) following 2-point calibration with WTW technical buffer solutions (4.01 & 7.00). Total dissolved organic carbon (TDOC) was determined using Shimadzu Total Organic Carbon Analyser. Total phosphorus (TP) was determined using the molybdate-ascorbic acid method (Murphy and Riley, 1962), following digestion of the unfiltered sample with persulphate and sulphuric acid. Total nitrogen (TN) was determined using the automated Lachat Quik-Chem 8000 FIA method (QuikChem Method 10-107-04-1-C) following persulphate oxidation of unfiltered sample with potassium persulphate and boric acid (Grasshoff et al., 1983). Ammonia was determined by automated Lachat Quik-Chem 8000 FIA on 0.45 lm-filtered samples (salicylate method QuikChem Method 10-107-06-3-D). Chlorophyll a was determined by the manual colorimetric method after extraction with methanol. Colour was determined by the manual colorimetric method, using diluted HACH colour standards for calibration (Eaton et al., 2005). 2.3. Fish sampling Fish were sampled in early June and September 2010 following Inland Fisheries Ireland protocols (based on European Committee for Standardization (European Committee for Standardization (CEN), 2005). This method provides a whole lake estimate for species occurrence and relative fish abundance is expressed as catchper-unit-effort (CPUE): R(Y/A)/N, where Y = number captured, A = area of net deployed and N = number of lifts of the net. Sampling was carried out using multi-mesh gill nets which were 60 m-long, 1.5-m deep and were composed of 12 different mesh sizes ranging from eight to 50 mm mesh sizes following a geometric series. Three gill nets were deployed during each sampling event, one each in the benthic, littoral and pelagic habitats in each lake. Two fyke nets were also deployed in each lake on each sampling date. All nets were set for a period of 24 h on each sampling occasion. All fish captured were identified to species, their fork length and weight were recorded to the nearest millimetre and to the nearest 0.1 g, respectively, and scales were removed from just below the dorsal fin for age determination. 2.4. Fish energetics The observed mean instantaneous daily growth rate (% d1) of each year class in each lake was calculated as a percentage of actual growth over the course of the study period, using the following equation from Elliott (1984):

Observed daily growth rate : Go ¼ 100flnðW t =W o Þg=t using the mean observed fish weights of each year class at the beginning (Wo) and end (Wt) of the study period of t days of growth. Potential maximum daily instantaneous growth rate (% d1) for fish of a given weight under the measured lake temperatures were calculated from the equation below:

mass was incorporated, calculated using the equation in Elliott and Hurley (1995): 1=b

Intermediate fish mass : W m ¼ f100½ðW bt  W bo Þ=ðGo btÞg

In order to compare growth rates between the lakes independent of the influence of temperature, we calculated the proportion of maximum potential growth rate (PMPG) realised as:

ðObserved daily growth rate=Potential maximum growth rateÞ  100: This metric allows for the comparison of growth rate between sites independent of temperature as it is an expression of the amount of growth that is possible for a fish of the measured size at the measured temperature experienced. Without such a calculated metric of growth, comparison between sites would be somewhat difficult as fish of equal mass growing at their maximum physiological potential at different temperatures would show different actual growth rates due to the significant influence temperature has on growth rates. Three components of food consumption (ration) were calculated based on a series of equations derived from laboratory experiments by Elliott (1975a, b, c). These were: the maximum potential ration size (Cmax mg dry weight day1) for a fish of a given weight at the specified lake temperature; the maintenance ration (Cmain mg dry weight day-1) which is the calculated ration required for maintaining metabolism for a trout of the measured weight at the temperature experienced over the study period; and the calculated actual daily ration consumption (Cactual mg dry weight d1) of each fish at the given lake temperature to produce the observed fish growth. The maximum daily food consumption, Cmax, was calculated from the equation given by Elliott (1975b):

Potential maximum ration : C max ¼ AD W b1 eb3T where the values of the constants AD, b1 and b3 are given in Elliott (1975b). The daily maintenance food consumption, Cmain, of trout was calculated using the equation given by Elliott (1975a):

Maintenance ration : C main ¼ aW b1 eb2T where the values of the constants a, b1 and b2 are given in Elliott, 1975a. The actual daily food consumption, Cactual, of a fish was calculated using the equation given by Elliott (1975a)

Actual daily ration consumed : C actual ¼ a1W b1 eb2Pþða2þb3PÞT where W is the trout weight wet in g, T is the lake temperature in °Celsius, and a1, a2, b1, b2 and b3 are constants (Elliott, 1975b). P is the daily instantaneous growth rate expressed as a percentage of the potential maximum daily instantaneous growth rate (PMPG). The calculations described above were carried out on measurements taken from a total of 1077 brown trout and from lake temperature (to the nearest 0.1 °C) recorded at each lake using two temperature probes, immersed for five minutes on each date, of six separate dates over the study period. These dates included the dates the fish were sampled on the 2nd to the 7th of June and the 15th to the 20th of September, as well as the 25th of June, the 20th of July and the 16th and 27th of August.

Potential maximum growth rate : Gw ¼ cW b m ðT  T LIM Þ=ðT M  T LIM Þ 2.5. Data analysis where TLIM = TL if T 6 TM or TLIM = TU if T > TM. TM is the temperature for optimum growth and TL and TU are the lower and upper temperatures at which growth rate is zero. The weight exponent b is the power transformation of weight that produces linear growth with time, and c is the growth rate trout at the optimum temperature. All five of these parameters are given in Elliott et al. (1995). The intermediate fish mass (Wm) (g) between the initial (Wo) and final

Among-lake differences in: individual water chemistry parameters, CPUE of total trout; and the size of individual year class of brown trout in June and September were assessed using analysis of variance (ANOVA; PASW Statistic 17). The average sizes of brown trout were only compared for those year classes that had sufficient sample size for ANOVA testing. Prior to performing

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Temperature (ºC)

ANOVAs, normality and homogeneity of variance were tested using Kolmogorov–Smirnov and Cochran’s C-test respectively. Where data were non-normally distributed and/or heterogenous, appropriate transformations were conducted. Significant results were tested for pair-wise comparisons by post hoc Tukey–Kramer tests. 3. Results 3.1. Water chemistry The summary statistics of the water chemistry values from each of the study sites is outlined in Table 2. There were clear differences in lake nutrients among treatments, with significantly greater concentrations of TP in H1 compared to the two control lakes, and higher levels of TP in H2 relative to the two control and the two medium forest lakes (Tables 3). There were higher concentrations of both TDOC and TN in H2 relative to the two control lakes and higher concentrations of these two parameters in H1 compared to C1 (Tables 3). Ammonia was higher in H2 than C1 and dissolved oxygen concentrations were lower in the two high foresteded lakes compared to C1. Whereas both H1 and H2 had higher concentrations of aluminium compared to C1, C2 and M1, only H2 had significantly higher colour compared to these three lakes (Tables 3). There was no significant difference in the levels of chlorophyll a, pH or temperature between any of the study lakes (Fig. 2; Tables 3). Temperature was relatively high throughout the study period, averaging over 17 °C in all study lakes.

Fig. 2. Temperature regimes in each of the six study lakes in western Ireland over the course of the study period. The dashed line indicates the upper temperature (19 °C) at which brown trout can grow (Elliot, 1981).

Assessment of trophic status of lakes in Ireland is based on the maximum chlorophyll a concentration recorded during the summer and autumn months (McGarrigle et al., 2010). Under this modified Environmental Protection Agency (EPA) version of the Organisation for Economic Cooperation and Development (OECD) scheme for lake trophic classification, C1, C2, M1 and M2 were

Table 2 Mean (±SE) water chemistry values for the six study sites in western Ireland. Water chemistry parameter

Control sites

Chlorophyll a (mg l1) Dissolved oxygen (mg l1) Colour (Hazen) pH TDOC (mg l1) TP (mg l1) TN (mg l1) Ammonia (mg l1) Total monomeric Al (lg l1)

Medium forested

High forested

C1

C2

M1

M2

H1

H2

2.9 ± 0.45 9.8 ± 0.3 52.8 ± 7.33 5.9 ± 0.19 6.2 ± 0.62 0.006 ± 0 0.4 ± 0.02 0.02 ± 0 21.9 ± 4.43

3 ± 0.41 8.1 ± 0.87 100.7 ± 13.42 5.6 ± 0.25 9.4 ± 1.16 0.009 ± 0 0.5 ± 0.04 0.02 ± 0 53.1 ± 4.29

3.2 ± 1.05 7.7 ± 0.33 137.5 ± 19.28 6.6 ± 0.18 11.6 ± 1.44 0.015 ± 0 0.5 ± 0.06 0.03 ± 0 54.6 ± 12.11

2.7 ± 0.91 8.7 ± 0.3 179.5 ± 18.17 6.2 ± 0.32 13 ± 1.59 0.015 ± 0 0.7 ± 0.06 0.03 ± 0.01 76.9 ± 10.49

7.3 ± 2.11 7.7 ± 0.36 244 ± 43.79 5.5 ± 0.4 15.6 ± 2.55 0.034 ± 0.01 0.7 ± 0.1 0.04 ± 0.01 153.7 ± 26.49

8.9 ± 3.33 7.7 ± 0.23 285.5 ± 44.5 5.8 ± 0.26 18.8 ± 2.55 0.038 ± 0.01 0.9 ± 0.1 0.05 ± 0.01 135 ± 28.72

Table 3 Summary of one-way ANOVAs of water chemistry parameters, CPUE of total brown trout and size of each year class (1 + to 4 + years old) of trout between the six study sites in western Ireland. Any sites sharing a common letter are not significantly different. Parameters that were square root transformed or log transformed to achieve normality and/or homogeneity of variance are denoted by a * and  , respectively. Parameter

Chlorophyll a  Dissolved oxygen Colour  pH TDOC TP  TN Ammonia Total monomeric Al Temperature CPUE 1 + weight (June)* 1 + weight (September) 2 + weight (June)  2 + weight (September)  3 + weight (June)  3 + weight (September)* 4 + weight (June)* 4 + weight (September) 

One-way ANOVA

Post hoc Tukey tests

df

F

p

C1

C2

M1

M2

H1

H2

5,18 5,18 5,18 5,18 5,18 5,18 5,18 5,18 5,18 5,30 5,6 4,85 5,125 5,219 5,197 5,182 5,130 5,49 2,15

2.47 3.71 9.54 2.02 6.18 8.41 5.32 2.97 8.69 0.28 5.72 2.16 6.9 10.5 14.2 34.5 26.6 21.4 2.72

0.71 0.017 <0.0001 0.125 0.002 <0.0001 0.004 0.04 <0.0001 0.921 0.028 0.08 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.07

– b a – a a a a a – ab – a b abc a a a –

– ab a – ab a ab ab a – ab – abc ab ac a a a –

– a ab – abc ab abc ab a – ab – ab b b a a a –

– ab abc – abc ab abc ab ab – a – a b bc ab a ab –

– a bc – bc bc bc ab b – b – c a a c b b –

– a c – c c c b b – ab – bc a a bc b b –

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all classified as oligotrophic and H1 and H2 were classified as mesotrophic. 3.2. Population structure There was as much variation within as there was among land use treatments in the CPUE of brown trout. The highest CPUE was recorded in two forest lakes, M1 and H2, which had CPUEs more than twice that of the two control lakes (Fig. 3). Despite low statistical power due to the fact that CPUE was only assessed in each site twice, CPUE was statistically higher in H1 compared to M2 (Table 3). There were two distinct brown trout population age structures in the study lakes (Fig. 3). The two control lakes were dominated by 2+ and in particular, 3+ individuals, with no 0+ and relatively few 1+ trout in these lakes. The brown trout populations in the four lakes with plantation forest in their catchment were dominated by younger individuals, mainly 1+ and 2+ fish but some young-of-the year trout also. Unlike the two control lakes, these forested lakes also contained low numbers of older fish of up to 8+ in age (Fig. 3). European eels (Anguilla anguilla L.) were present in very low abundance, with no more than two individuals captured in any one lake. Although not captured in gill nets, threespined stickleback (Gasterosteus aculeatus L.) were recorded in the gut contents of brown trout in each of the six study sites. 3.3. Brown trout average sizes, growth and energetics

corrected growth rate of brown trout over the study period. Over the summer study period, growth of trout was generally low in all lakes, with no age class in any of the study lakes achieving greater than 45% of the potential maximum growth rate at the measured lake temperature (Fig. 5). Calculated actual daily ration consumed by all year class of brown trout in all study lakes were far less than potential maximum daily rations, with no year class consuming a third of the potential maximum ration. Actual ration consumed was barely above the amount of ration required to maintain metabolism for all year classes in all six study lakes over the summer study period (Fig. 6).

4. Discussion Our findings demonstrated an association of lake nutrient enrichment, but not acidification, with plantation forest in blanket

Proportion of fish captured

The growth trajectories of trout differed between the two high forest sites and the four other lakes (Fig. 4). Trout in HI and H2 were generally larger for each age class than in the other lakes, particularly the 3+ and 4+ individuals, which were approximately 50% heavier on average than their counterparts in the control and medium forest lakes (Fig. 4; Table 3). This observed trend of larger fish in afforested lakes was not seen in the calculated PMPG (Fig. 5) which showed no pattern between treatments of temperature

Fig. 5. The proportion of the maximum potential growth (PMPG) achieved by brown trout during the study period in the six study lakes in western Ireland.

CPUE

(A)

(B)

Age Class

Site

Fig. 3. Comparison of (A) mean (±SE) CPUE of total trout over the two sampling dates and (B) the age structure of the brown trout populations in each of the six study lakes in western Ireland, expressed as a proportion of the total number of brown trout captured in each lake.

(B)

Mean weight (g)

(A)

Age Class

Age Class

Fig. 4. The mean size of 0+ to 4+ year classes in each of the six study lakes in western Ireland in (A) June and (B) September.

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Fig. 6. Calculated maximum (black bars), maintenance (grey bars) and actual daily rations (hatched bars) (±SE) of brown trout over the study period in each of the six study lakes in western Ireland. Note different scales.

bog catchments in western Ireland. The observed moderate eutrophication in our study here was greater in the high forest lakes. A recent more extensive study on the impact of conifer plantation on the hydrochemistry of twenty six blanket bog lakes in Ireland demonstrated a clear deleterious impact of conifer plantation on water quality, particularly nutrient enrichment, potentially through application of inorganic fertiliser at crop planting (Drinan et al., 2013b). This impact has been demonstrated in a number of other studies also, although the majority of these have focused on running waters (Binkley and Brown, 1993; Neal et al., 1998; Binkley et al., 1999; Cummins and Farrell, 2003b). Study lakes with forest plantations in their catchments had higher nutrient levels, and larger brown trout, than catchments without forest. The supply of readily available plant nutrients can have a strong limiting effect on primary and secondary productivity of freshwaters (Johnston et al., 1990; Peterson et al., 1993). Trophic enrichment can therefore lead to increased food availability for fish (Egglishaw and Shackley, 1985; Johnston et al., 1990; Peterson et al., 1993), which is one of the major factors controlling the growth and production of salmonids (Egglishaw and Shackley, 1985). The moderate nutrient enrichment of some freshwaters therefore has the potential to enhance salmonid populations in some nutrient poor systems. The supply of invertebrate prey items whose growth, density and production may respond positively to nutrient enrichment, as shown in other studies (Hynes, 1960; Peterson et al., 1993) including lakes with conifer afforested catchments (Rask et al., 1998). In turn, increased prey availability has been shown to stimulate the density, growth and production of salmonids (Johnston et al., 1990; Peterson et al., 1993; Erkinaro and Niemelä, 1995; Poff and Huryn, 1998; Wipfli et al., 1999; Wipfli et al., 2003; Johansen et al., 2005). Hence, the lakes with forested catchments in this study may support more putative prey, and hence, greater populations of larger body-sized brown trout. Uniquely in studies of conifer plantation and water interactions, we recorded no evidence of negative eutrophication or acidification impacts on brown trout populations in our study lakes. Many other studies have highlighted the negative impacts plantation forest has on fish in freshwaters (Rees and Ribbens, 1995; Jutila et al., 1998; Rask et al., 1998). The mild nutrient enrichment associated with afforestation recorded in this study elevated the lakes with large areas of conifer plantation in their catchment from

oligotrophic to mesotrophic, resulting in these lakes remaining essentially, nutrient depauperate (McGarrigle et al., 2010). Moderate nutrient enrichment such as recorded here, could possible impact on inter-species competition. However, our study lakes were unusual in a global extent in that they are virtually only populated with brown trout, with very low numbers of three-spined stickleback (recorded in gut contents, unpublished data) and eels also present. Eutrophication-mediated changes to lacustrine fish communities through alterations in competitive balance usually results in the replacement of salmonids and coregonids with percids and eventually coregonids, with increasing nutrient status (Persson et al., 1991). However, Ireland has a very impoverished fish community with only fourteen native species, none of which are percids or cyprinids (Maitland and Lyle, 1992; Griffiths, 1997). Introductions of non-native species of fish into peatland lakes in Ireland is generally extremely rare due to the inherently oligotrophic nature of these systems. Variation in CPUE between lakes among land use categories suggests brown trout density in these systems is not simply responding to hydochemistry. The highest densities of trout were found in lakes with conifer plantation within their catchment. The trout in H1 and H2 were larger in body size than their counterparts in the other four lakes. The differences recorded in the growth trajectories of the trout in the lakes with high levels of forest in their catchment relative to the other lakes, based on the average size of year classes, were not reflected in PMPG over the study period. Fish in lakes H1 and H2 were generally 50% larger on average than their conspecifics in the other four lakes after three to four years of growth. However, this variation in life history growth between the lakes with highly afforested catchments and the other sites was not recorded in these lakes relative to the other study sites over the time period of the study. There are two potential mutually nonexclusive explanations for this apparent contradiction. Firstly, growth of brown trout may typically occur in these systems outside of the summer season in which we sampled the fish. Previous research on the seasonal growth rate of salmonids has demonstrated that highest growth rate occurs typically in spring (Bacon et al., 2005; Xu et al., 2010). Laboratory studies have shown that the appetite of captive Atlantic salmon peaked in May and declined over the summer period despite being fed to excess and being held at temperatures suitable for growth

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(Simpson and Thorpe, 1997) which may reflect evolutionary adaptation by wild populations to concentrate growth when local conditions of temperature and food availability are optimum (Bacon et al., 2005). Secondly, the temperature regimes of all six of the study lakes were relatively high throughout the study period. Temperature has long been recognised as a major influence on the ecology and physiology of fish (Fry, 1947, 1971; Magnuson et al., 1979; Elliott, 1994; Graham and Harrod, 2009). Temperature directly controls metabolic processes and, besides food availability, is the single most important factor that determines growth rates in fish (Fry, 1971; Brett, 1979; Elliott, 1994). The upper temperature level at which brown trout can feed is approximately 19 °C (Elliot, 1981). Above this threshold is referred to as the starvation temperature range when the resistance time to death of is a function of temperature, body size and energy reserves of the brown trout (Elliot, 1981). As temperature increases, metabolism and energy demands increase. However, if ration intake is limited, this temperature range at which brown trout can grow is restricted to below this 19 °C threshold and may reach a point where energetic inputs from food are insufficient and fish have to utilise stored energy reserves (Fry, 1971; Otterlei et al., 1999) at a considerably lower thermal threshold (Elliot, 1981). Our calculations of maintenance (Cmain) and actual (Cactual) daily ration suggest that, over the course of the study, brown trout may have been thermally and hence, energetically challenged with barely sufficient calculated energy intake to fulfil maintenance requirements, resulting in very little surplus energy available for growth over the study period. Although there was no statistical significant difference in temperature between study lakes, it is probably that there was an ecologically significant difference in thermal regimes. Temperatures in H1 and H2 were 2 °C higher relative to the two control lakes on one date in the sampling period. Additionally, the amount of energy required to fulfil maintenance requirements increases with body size (Elliott, 1975a, 1976). This combination of greater size of trout and the higher temperatures experienced in the high forest lakes could have exerted a significant energetic pressure on the trout in the lakes with highly afforested catchments, potentially explaining the discrepancies between the average size of year classes and the PMPG over the study period. For example, at the temperatures recorded on the 25th of June, the calculated maintenance requirement of the averaged size 4+ trout in H1 was 43.5% greater than their counterparts in C1. Salmonids, and brown trout in particular, are renowned for their life history variation and their remarkable ability to adapt to new or altered conditions (Elliott, 1994; Klemetsen et al., 2003; Cucherousset et al., 2005), such as those due to afforestation of these lake catchments over the last 40–50 years. Despite this remarkable plasticity in their behaviour in response to environmental change, their ability to adapt to elevated temperature regimes may be limited by physiological constraints rather than behavioural ones. Although we found no statistical difference in lake temperature here, this may be due to limited statistical power. Recent research in Ireland however, has shown temperatures of small lakes in afforested peatland catchments to be elevated by nearly 1 °C relative to non-forested lakes (Drinan et al., 2013b). Similarly, forestry in Scotland has been shown to elevate the summer temperature of moorland streams (Hannah et al., 2008). The mechanisms behind such forestry-mediated elevations on temperature in freshwaters are unclear. However, plantation forests surrounding lakes can provide considerable shelter from wind that may reduce lake temperatures. Wind speeds at open sides can be five to ten times higher at open lakes compared to forested lakes (Moore et al., 2005). As the lake water of the high forest lakes were significantly darker relative to the control lakes, this may facilitate higher temperatures through increased absorption of solar energy. Certainly, this

question warrants further study, particularly as if forestry is elevating lakes temperatures, this could compound any negative effects caused by climate change and put further metabolic stress on brown trout and other salmonids. Observed differences in the brown trout age structures of lakes with and without conifer plantations in their catchment, are potentially explained by life history variation of brown trout due to differences in opportunity for growth in the natal streams within these catchments. Fishes migrate if the growth and survivorship advantages of utilising a second habitat, plus the cost of moving between these habitats, outweigh the advantages of remaining within their natal habitat (Gross, 1987). Fast growing brown trout migrate downstream from natal streams to lakes earlier than slow growing individuals (Forseth et al., 1999) and data from Sweden showed that the larger individuals of a cohort of brown trout undertook downstream foraging migrations (Carlsson et al., 2004). The moderate forestry-mediated nutrient eutrophication recorded in our lakes is likely mirrored in the natal tributaries to these lakes. Nutrient enrichment of oligotrophic lotic systems can result in increases in potential food supply for salmonids (Johnston et al., 1990; Peterson et al., 1993; Wipfli et al., 1998), resulting in higher growth rates (Johnston et al., 1990; Peterson et al., 1993; Wipfli et al., 2003). Therefore, the presence of younger trout in the lakes with highly afforested catchments may be explained by these fish reaching an energetic or size threshold in these lakes at an earlier age due to forestry mediated trophic enrichment stimulation of primary and secondary production in their natal streams, enabling them to undertake this migration. No impact of forestry associated acidification was detected during this study. Although forest-associated acidification continues to be a widespread problem (Kowalik and Ormerod, 2005; Ormerod and Durance, 2008), reductions in atmospheric sulphur emissions have been linked to observed reductions in episodic acid depositions in streams draining afforested catchments (Malcolm et al., 2012). We did not find an adverse effect of conifer plantations on the brown trout in peatland lakes in this study. Whereas there was no impact of forestry-mediated acidification detected, moderate nutrient enrichment was associated with forestry activities, with sites with high levels of plantation within their catchments having a higher trophic status that the other study sites. Forest activity likely stimulated brown trout growth in catchments with high levels of plantation forest, through enhanced primary and secondary production. Enhanced growth was recorded in lakes with high forest levels within their catchment through average size of fish captured but no impact of growth over the study period was detected. This discrepancy is possibly explained by growth occurring outside of the study period and/or somewhat elevated temperature regime during the study period exerting energetic stress on the fish, particularly in the forested lakes. Our findings presented here are from a relative small number of selected lakes which are likely representative of these ecosystems located in blanket bog catchments in this area of Ireland. Many of the plantations on blanket bogs in Ireland are now reaching harvestable age and Drinan et al. (2013b) demonstrated that the forestry-mediated negative impacts on peatland lake hydrochemistry is exacerbated during the felling stage. Research is warranted to investigate if plant nutrient or heavy ion input into aquatic habitats within such catchments is intensified by felling to a point that exceeds an ecological threshold and hence exert considerable negative impacts on the ecology of these systems. Such data is of vital importance to forestry managers to allow them to develop felling strategies that minimise any felling induced inputs to aquatic systems. Until such data is available, minimisation of the area of plantation felled within peatland catchments at any time may be advisable. Similarly, further research is needed to further investigate the potential of forestry to elevate the temperature of

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