Drought Disturbances Increase Temporal Variability

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RESEARCH ARTICLE

Drought disturbances increase temporal variabilityof zooplankton community structure in oodplains

Nadson R. Simes, Fbio A. Lansac-Tha and Claudia C. Bonecker

Departamento de Biologia, Universidade Estadual de Maring, Maring, PR, Brazil

The present study veri ed how hydrological regime affects the temporal variability ofzooplankton communities in oodplains. Multidimensional methods were employed tocharacterise community variability, and the temporal trend of the community was alsoevaluated using a matrices comparison procedure. In connected ponds from the main river,the absence of oods (drought) modi ed the variability of the zooplankton community by

increasing temporal dissimilarity of community. In these environments, the periodic input ofwater due to oods maintained a lower variability of environmental characteristics, sustaininga lower temporal variability of the community. This observation suggests that, in oodplains,ooding is a component of the system that assists with both the maintenance of ecologicalprocesses (temporal variability of the community) and the high biodiversity of theseenvironments. This study therefore suggests that the temporal variability of communities inoodplains is increased by extended droughts, and emphasises the importance of ooddynamics to maintain temporal stability of zooplankton communities in river oodplainsystems.

Received: March 22, 2012Revised: August 20, 2012

Accepted: October 25, 2012

Keywords:Connectivity / Dry / Flood / Stability / Temporal trend / Temporal turnover

1 Introduction

Ecological systems are naturally dynamic and constantlyreorganising themselves according to environmentalvariations [1]. Therefore, variability is a natural propertyof ecological communities [2] that can, at some levels,surpass the tolerance range of organisms, producing anadverse effect on populations [3] and causing changes inecosystem function due to alterations in communitystructure, biodiversity loss and shifts in energy ow.Understanding this variability is essential for theoreticaland applied studies in ecology as it allows us to: (i) identifythe effects of extrinsic factors on ecological processes; (ii)understand the dynamics of ecological systems; (iii)identify the environmental range tolerated by organisms;and (iv) intervene rationally in activities of environmental

management and assist with the implementation ofplanning strategies and biodiversity conservation [4, 5].

Recently, the variability of communities has been usedas a tool to describe ecosystem stability in order tounderstand howecological systems respond to natural andanthropogenic disturbances [6 9]. Disturbances arede ned as events in time that disrupt the naturaldevelopment of the ecosystem, community or populationand change the availability of resources and physicalconditions [10]. In general, the disturbance either removesindividuals or acts within the tolerance limits of organismsby affecting reproduction, growth and survival [11].

Identifying and differentiating the variability produced bynatural and anthropogenic situations is a strategy for developing a better understanding of ecological systems.This study was conducted in a region with conspicuousand natural environmental uctuations ( oods) that is alsosubject to anthropogenic impacts, due to an upstreamcascade of reservoirs that has generated several changesin the hydrodynamic, physical, chemical and biologicalaspects of the water [12 15]. The reservoirs affect theaquatic communities as they reduce the variation in thenatural range of water level, modifying the periodicity of

Handling Editor: Karsten Rinke

Correspondence: Nadson R. Simes, Departamento deBiologia, Universidade Estadual de Maring, PEA/Nupelia, Av.Colombo 5790, 87020-900, Maring, PR, BrazilE-mail: [email protected] Fax: (55) 44 3011 4625

International Review of Hydrobiology 2013, 98 , 24 33 DOI 10.1002/iroh.201201473

2013 WILEY VCH Verlag GmbH & Co. KGaA, Weinheim 24


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oods and, consequently, the ecological dynamics of river-oodplain systems [16, 17].

In this way, the present study aimed to verify whether the temporal variability in the zooplankton community intwo distinct sets of ponds (connected or unconnected to

the main river) responds differently to hydrologicalvariation related to the presence or absence of oods.We predicted that the temporal variability of the communitywould be increased when there are changes in the naturalvariability of the system.

2 Methods

The hydrographic basin of Paran River is the secondlargest in South America in terms of length and drainagearea [18]. At its upper stretch, where the Upper ParanRiver oodplain is situated 2230 0 and 2300 0 Southlatitude; 5300 0 and 5330 0 West longitude, the aquaticlandscape is formed by rivers, secondary channels,backwaters and connected, temporary and isolatedponds [19]. In Brazilian territory, this oodplain occupiesan area exceeding 802 150 km 2 and plays an importantsocial and economic role through tourism and shingactivities common to this ecosystem. The oodplain is alsoof environmental importance as a system with highbiological diversity, but its ecological integrity has recentlybeen threatened by the operation of reservoirs [15, 19].

Samples were collected quarterly during two periods(2000 2001 and 2005 2006) in six ponds associated with

three large rivers of the regions (the Paran, Ivinheima andBaia Rivers) that form three distinct sub-systems (Fig. 1;Paran, Ivinheima and Baia sub-systems, respectively,abbreviated as PS, IS and BS). For each sub-system weselected two ponds, one connected (CP) and the other

isolated (IP) from the main river. Thus, each pond wassampled 16 times (except IP in the PS, which was sampled14 times due to complete drying of the pond), eightsamples during 2000 2001 and eight during 2005 2006,collecting a total of 94 samples. These ponds had a depthranging 1.1 3.5 m, and an area ranging from 0.01 to113.8 ha (species richness was not correlated with pondarea). In general, the ponds showed high variability ofabiotic characteristics due to ood temporal dynamics (seesummary, Appendix 1).

This study is part of a long-term ecological researchproject begun in 2000 [13]. We studied two periods withdistinct hydrological features. The rst period (2000 2001)was characterised by a drought that affected the water level of the Baia and Paran rivers but did not affect theIvinheima River, whereas the second period (2005 2006)was characterised by oods typical of the Baia and Paranrivers but was the driest period for the Ivinheima River. Thehydrological variation was compared using ood intensity,ood amplitude, uvial connectivity index (i.e. the ratiobetween the number of days under potamophase (highwater period) and the number of days of the year) [18] andwater level variation graphs (Fig. 2). Thus, in the period2000 2001, a drought occurred in the Baia and Paran

53 15o

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IP Isolated Pond

Connected Pond

I v i n h

e i m a

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Figure 1. Location of thesampling in theUpper Paran River oodplain. Eachsub-system has one pond isolated fromand one pond connected with the mainriver.

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sub-systems (both in uenced by oods of the ParanRiver), while the Ivinheima sub-system was drier in theperiod 2005 2006 (Fig. 2). This design allowed us to testthe community variability in distinct hydrological situationsindependent of the periods studied. Thus, the droughtevent was our main categorical variable. The water level ofreference used to indicate the threshold between pota-mophase and limnophase (low water period) [18] tocharacterise ood pulses was the level at which river water over owed into the isolated ponds; this value was 3.5 for Paran River and 2.6 for Ivinheima River [20, 21]. Water

level has been highlighted because is responsible for driveexpansion and contraction of environments, and bioticinteractions (see [22]).

Zooplankton communities were sampled under thewater subsurface of the pelagic region at each pond in themorning using a pump to lter 600 L of water per samplethrough a plankton net (68 m). The material waspreserved in a formaldehyde solution (4%) buffered withcalcium carbonate. The species (rotifers, cladocerans andcopepods) were identi ed using specialised literature(see [13]). Zooplankton organisms were quanti ed usinga Sedgewick-Rafter chamber underan optical microscope.At least 80 individuals were counted in three subsequentsubsamples [23] obtained with a Hensen-Stempell pipette(2.5 mL). Species that had one occurrence in each periodwere removed from the analysis to minimise identi cationerrors and to improve the quality of the multivariateanalyses.

2.1 Data analysis

In order to verify the existence of differences in therichness and abundance of the zooplankton communitybetween the connected and isolated ponds in the two

analysed periods (drought and ooded), we performedfactorial analyses of variance. Data were log trans-formed to achieve the assumptions of normality andhomoscedasticity.

The environmental variability of each pond wasestimated by calculating the coef cient of variation for each limnological feature in each period (drought andooded). Highest coef cient of variation suggest highestenvironmental variability. We ran a factorial analysis ofvariance to test the difference in environmental variabilitybetween the types of ponds and periods. The assumptions

of normality and homoscedasticity were previously testedusing Shapiro Wilk and Levene tests, respectively.

In order to test if the temporal variability of thezooplankton community in each pond differed betweendrought and ooded periods, we used the procedureproposed by Anderson et al. [24]. This method tests thevariability between groups by calculating a multivariatedispersion measure using a PCoA (Principal CoordinateAnalysis). For the PCoA, a centroid for each group(drought and ooded periods) was calculated in a space ofprincipal coordinates, and the distances between thesampling units and their respective centroids wereevaluated. Groups occurring at greater distance fromthe centroids suggest greater variability in relation to thegroup found at lower distances. The difference betweengroups was estimated using a permutation test. Data weretransformed into log 2 (x ) 1, in an effort to de ne ageometric order in the relative abundance among species(for details, see [24]), and the Bray Curtis distance ofdissimilarity was used.

The temporal trend of the community was assessedusing time lag analysis [25] in order to assess whether eachcommunity had convergence, divergence or no pattern ofdirectional change. Here, convergence means decreasing

Figure 2. Daily variation of water level of the Paran and Ivinheima rivers (2000 2008). The vertical dotted line indicatesover ow level. Arrows show the sampling date. Rectangles de ne the sampling periods. MI, maximum level; PA,potamophase amplitude; CI, uvial connectivity index.

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dissimilarity with time, while divergence suggests anincreasing of dissimilarity with time [26]. In this procedure,a community dissimilaritymatrix is regressed with increasingtime lags to yield a measure of the rate and nature ofcommunity change over time (for details, see Fig. 1 in [25]).

This approach is a community-level extension of autocorre-lation analysis [25]. Signi cance was estimated using theMantel test to compare the relationshipbetween dissimilaritymatrices andthe time lagmatrix. (a)Firstly, we evaluated thepresence and absence of species, using the Simpsondissimilarity [27] because it is less dependent on thespeciesrichness. This index ranges from zero to one, with valuesnear zero indicating the highest similarity and values near one indicating lowest similarity. A signi cant positiveassociation between the Simpson dissimilarity matrix andthe matrix time lag indicates an increase in communitydissimilarity over time, suggesting divergence of thecommunity from its initial con guration. The reverse showsan increase in similarity in the community over time. (b)Secondly, the maintenance of species rank during eachsampling period was measured using the non-parametricKendall rank correlation coef cient [28]. High coef cientswith positive values indicate that thespecies keep their ranksin a distributionof abundance,suggesting thepredominanceof similar ecological relationships over time because thepatterns of species dominance and rarity are maintained.Values near zero indicate that the distribution of abundanceamong species has changed unpredictably, whereasnegative values indicate a reversal in the pattern ofabundance distribution among species, suggesting ecologi-

cal changes that modify the community structure. Asigni cant positive correlation between the Kendall dissimi-larity matrix and time lag suggests that ranks of speciesabundance tend to become more similar over time, while asigni cant negative correlation indicates a community trendto reverse the dominance pattern among species.

The analyses were performed with the software Rversion 2.8.1 [29], using the packages vegan [30] andsimba [31].

3 Results

3.1 Environmental variability

There was no difference in water temperature, dissolvedoxygen, pH, electrical conductivity and total nitrogencoef cient of variation between the types of ponds or periods (Anova, p > 0.05). Total alkalinity and chlorophylla were more variable in the isolated ponds in both periods(F 1,8 10.13, p 0.013; F 1,8 12.64, p 0.007;respec-tively; Fig. 3a and c). Secchi disc and total phosphorusshowed signi cant interaction ( p < 0.05) between types ofponds and periods. That means the hydrological variationdid not affect equally the types of ponds (Fig. 3b and d). For

instance, the drought period increased the variability of thetotal phosphorus in the connected ponds (Fig. 3d).

3.2 Zooplankton community attributes

The accumulated species richness ranged from 65 taxa inPS (isolated pond) to 104 taxa in BS (connected pond;Table 1). Species richness differed only between the typesof ponds ( F 1,90 22.78; p < 0.001), with higher values inthe connected ponds (Table 1). Drought and oodedperiods showed no difference in richness. Abundancevaried between 1 10 3 and 8504 10 3 ind m 3 , withdifferences in the range between the periods only inconnected ponds (Table 1).

Of the species pool, eight were more abundant ofeach group in both periods: Keratella cochlearis , Lecane proiecta , Hexarthra mira (rotifers) , Moina minuta , Bosminahagmanni , Ceriodaphnia cornuta (cladocers) , Notodiapto- mus amazonicus and Thermocyclops minutus (copepods ).

3.3 Zooplankton community variability

The temporal variability of the zooplankton community inconnected ponds differed between study periods ( p < 0.05,Fig. 4), with lower variability in 2005 2006 for ponds in PSand BS and in 2000 2001 at IS, showing lower communityvariability in ooded periods. In isolated ponds, there was nodifference in the temporal variability of the zooplanktoncommunity between the periods ( p > 0.05), showing that, inthese locations, the temporal variability of community

structure was more similar between the two hydrologicalperiods. In the ponds from PS, although the area formed inthe drought period by graphic representation was relativelysmaller than that formed in the ooded period, themultivariate test did not detect signi cant differences(possibly as in the drought period the number of sampleswas lower due to complete drying of the pond).

3.4 Temporal trend of species composition

In the connected ponds, a positive relationship wasobserved between the matrix of the dissimilarity coef cientand the matrix of time lag (Mantel, Table 2) during drier periods (2000 2001 for BS and PS, and 2005 2006 in ISponds). This observation suggests that, during droughtperiods, the zooplankton community in connected pondsdiverged temporally, changing species composition, whilein the ood period the composition did not demonstrate atrend of temporal modi cation.

There wasno pattern of temporal dissimilarity for isolatedponds (Table 2), as PS had no trend of community variationover time in the two analysed periods; BS tended to increasein dissimilarity only during 2005 2006, and IS tended toincrease in dissimilarity during both sampling periods.




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3.5 Temporal trend of relative abundance

In connected ponds, a negative relationship was observedbetween the Kendall matrix of similarity and the matrix oftime lag during thedrier period (2000 2001 for ponds in PSand BS, and 2005 2006 for the pond in IS; Table 3). Thus,the absence of ooding produced a temporal shift in the

community dominance, reversing the distribution ofspecies abundance over time in connected ponds. Onthe other hand, no temporal trend was recorded incommunities from connected ponds during periods withtypical oods (Table 3) indicating that, under greater amplitudes of ooding, the distribution of the ranks ofspecies abundance is indifferent to time lag.

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Figure 3. Coef cient of variation of water features between periods (Drought and Flooded) and types of ponds (Isolated andconnected ponds). Total alkalinity (a), Secchi disc (b), Chlorophyll a (c) and total phosphorus (d). & , mean; , standard error.

Table 1. Attributes of the zooplankton community in the studied ponds during the period 2000 2001 and 2005 2006 in theUpper Paran River oodplain: S total, species richness; S (sd), average species richness and standard deviation

Type of pond Sub-systems

2000 2001 2005 2006

S total S (sd)Variation ofabundance* S total S (sd)

Variationof abundance*

Connected ponds Paran 86 30(6) 7 8504 94 29(9) 1 24Baia 104 38(9) 14 1808 89 34(7) 1 93Ivinheima 90 30(7) 38 369 91 34(6) 3 49

Isolated ponds Paran 65 25(8) 9 93 79 21(7) 1 276Baia 89 32(6) 19 401 83 29(5) 6 151Ivinheima 70 25(5) 29 245 79 21(6) 1 575

* values in ind m 3 10 3 .




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The isolated ponds demonstrated no differentiatedresults for inversion of species ranks during the analysedperiods (Table3). In PS, there wasno association betweenthe time lag of samplings and the matrix of Kendallcorrelation. BS presented a negative trend during theperiod 2005 2006, and IS demonstrated a trend ofreversal of species ranks during both sampling periods.

4 Discussion

The temporal variability of the zooplankton communitystructure differed between the analysed periods inconnected ponds that demonstrated greater variabilityduring the drought period (i.e. higher dispersion betweensamplings in time; seeFig. 4).As indicatedby Mantel tests,

Figure 4. Two-dimensional representation of PCoA of the structure of the zooplankton community in two periods (droughtand ooded) in the Upper Paran River oodplain. The scores represent different sampling months between analysed

periods. p -values show differences between periods.




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theabsence of oods changed the community compositiondue to an increase in temporal dissimilarity, and promoteda distribution reversal of species abundances. This meansthat, in connected ponds, the zooplankton communitydiverges with the prolongation of drought due to change inspecies composition and the pattern of relative abun-dance. It is likely that the recurrence of oods in connectedponds maintains environmental conditions that are lesschangeable than drought periods (such as low variability ofchlorophyll a ), favouring the lower variability of thecommunity [32 34]. Thus, droughts produce a disturbing

effect because they increase community variability, asevidenced in 2000 2001 for the Paran and Baia sub-systems (PS and BS), and in 2005 2006 for the Ivinheimasub-system (IS).

One of the effects of oods in oodplains is to buffer local variability, mainly in connected ponds, as the

constant input of water maintains the renewal ofnutrients [14, 33]. Consequently, ooding makes theenvironmental variation range narrower and more con-stant, mainly regarding resources (Fig. 3c and d). Inresponse, communities exhibit lower variation as environ-mental conditions vary within a constant range, assuggested by Bengtsson et al. [35], resulting in a lower variation in the niche amplitude of species and thus a lower rate of temporal turnover. Nevertheless, in the absence ofoods, autochthonous processes of productivity overcomeallochthonous processes of energy input [33], promoting

an increase in environmental variationandconsequently incommunity variation [35].

The temporal variability of the zooplankton communityin isolated ponds (Fig. 4) did not differ between theanalysed periods. However, in BS, we observed differenttemporal trends of the community between the periods,

Table 2. Result of the Mantel test between the matrix of time lag and the matrix of Simpson dissimilarity in the differenttypes of ponds (connected or isolated) from the Upper Paran River oodplain in different hydrological periods

Connected ponds Isolated ponds

r -Mantel p -Value r -Mantel p -Value

Baia sub-system2000 2001 0.454 0.013 2000 2001 0.068 0.3432005 2006 0.010 0.457 2005 2006 0.298 0.038

Paran sub-system2000 2001 0.683 0.002 2000 2001 0.381 0.9352005 2006 0.095 0.290 2005 2006 0.037 0.551

Ivinheima sub-system2000 2001 0.050 0.393 2000 2001 0.303 0.0652005 2006 0.528 0.002 2005 2006 0.463 0.021

Number of samplings is equal to eight in each study period (except for the isolated pond from the Paran sub-system (PS) in2000 2001, with six samplings as the pond dried up).

Table 3. Result of Mantel test between the matrix of time lag and matrix of Kendall correlation in the different types ofponds (connected or isolated) from the Upper Paran River oodplain in different hydrological periods

Connected ponds Isolated ponds

r -Mantel p -Value r -Mantel p -Value

Baia sub-system2000 2001 0.567 0.001 2000 2001 0.150 0.7872005 2006 0.279 0.102 2005 2006 0.620 0.002

Paran sub-system2000 2001 0.721 0.001 2000 2001 0.122 0.3442005 2006 0.066 0.292 2005 2006 0.079 0.335

Ivinheima sub-system2000 2001 0.045 0.566 2000 2001 0.343 0.0442005 2006 0.394 0.024 2005 2006 0.397 0.028

Number of samplings is equal to eight in each study period (except for the isolated pond from the Paran sub-system (PS) in2000 2001, with six samplings as the pond dried up).




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with an increase in temporal dissimilarity in the oodedperiod, in addition to a reversal in the pattern of speciesdistribution. The isolated ponds exhibited a greater variability of environmental conditions (Fig. 3a and c) withhigher chlorophyll a , which is an environmental factor

related to the environmental trophic state and foodresources. Such environmental variability promotes ahigher rate of species replacement due to temporal nichepartitioning, permitting the persistence of multispeci ccommunities over time that maintain the high naturalvariability of these communities [36]. From this perspec-tive, the community variability in isolated ponds is naturallyhigh as these locations are recurrently experiencingconditions that exceed the tolerance limit of somespecies [36, 37], so the conditions that disfavour thedevelopment of some populations can be required for thedevelopment of others.

Studies approach disturbances as discrete eventscausing sudden changes in communities [10, 11]. Inoodplains, a ood is sometimes considered to be adisturbance, as it affects the community structure throughthe removal of individuals [38, 39]. However, it is a periodiccomponent of the system [38, 40], producing temporalvariability of communities, and maintaining ecologicalprocesses and the high biodiversity of river oodplainsystems [16, 41, 42]. Furthermore, the diversity of nativespecies in a landscape will be greater when thedisturbances occur according to historical patterns andfrequencies [40]. In this way, in oodplain systems,absence of oods can result in conditions of greater

stress undermining local biodiversity, because the tempo-ral variability of communities and essential nutrient (e.g.total phosphorus) were increased in connected pondswhere the historical pattern of variability (i.e. ooding) wasabsent, promoting a temporal divergence of the communi-ty. Disturbances created by species removal and fertiliza-tion also cause divergence in community structure [26].

Studies conducted in rivers suggest that temporalvariability of communities is lower where environmentalconditions are relatively constant [6, 7]. However, on abroader temporal scale (such as an inter-annual variationand/or inter-hydrosedimentological cycles), the temporalvariability of communities is lower when the environmentalconditions change in a natural way. For instance, inoodplains natural variability of the communities occurs inthe presence of oods. Nevertheless, communities inconnectedpondswere more affected than those in isolatedponds when the region suffered from droughts. Extendeddroughts represent a stress situation for the zooplanktoncommunity in connected ponds, whereas in isolated pondsthis effect is attenuated because the high variability isinherent to these locations. Isolated ponds did not exhibitdifferent temporal variability of the community, or differenttrends between periods, as communities in these isolated

locations display high variability regardless of the water level.

This study suggested that the temporal variability ofcommunities has a component of adjustment to naturalenvironmental uctuations, because the connected ponds

that showed lesser recourse variability had their temporalvariability affected in extended drought periods; whereascommunities in environments with recurring variations(isolated ponds) may become most variable when suchuctuations fail to occur. Thus, we corroborated thehypothesis that the temporal variability of the communityis increased when there are changes in the naturalvariability of the system, so that allochthonous eventsdisrupt the dynamic of natural variation, therefore changingthe temporal stability of the community. In this way,regulated rivers should be managed to maintain naturalbehaviour in order to maximise the natural dynamics ofcommunities and, consequently, ecosystem functioning.

We thank to Limnology laboratory (Nupelia) by assis- tance with physical and chemical variables of water. We thank Dr. Luis Mauricio Bini (Universidade Federal de Goias) by important comments andsuggestions. This work was supported by Conselho Nacional de Desenvolvimento a Pesquisa (CNPq) with nancing of the Long Term Ecological Research; and Coordenao de Aperfeioa- mento de Pessoal de Ensino Superior (CAPES).

5 References

[1] Brown, J. H., Whitham, T. G., Morgan, E. S. K., Gehring,C. A., Complex species interactions and the dynamics ofecological systems: Long-term experiments. Science 2001,293 , 643 650.

[2] Landres, P.,Morgan,P., Swanson, F.,Overview of theuse ofnatural variability concepts in managing ecological systems.Ecol. Appl. 1999, 9 , 1179 1188.

[3] Vinebrooke, R. D., Cottingham, K. L., Norberg, J., Scheffer,M. et al., Impacts of multiple stressors on biodiversity andecosystem functioning: The role of species co-tolerance.Oikos 2004, 104 , 451 457.

[4] Palmer, M., Hakenkamp, C., Nelson-Baker, K., Ecologicalheterogeneity in streams: Why variance matters. J. North Am. Benthol. Soc. 1997, 16 , 189 202.

[5] Micheli, F., Cottingham, K. L., Bascompte, J., Bjornstad,O. N. et al., The dual nature of community variability. Oikos 1999, 85 , 161 169.

[6] Scarsbrook, M., Persistence and stability of lotic invertebratecommunities in New Zealand. Freshwater Biol. 2002, 47 ,417 431.

[7] Brown,L., Milner, A., Hannah, D.,Stability andpersistence ofalpine stream macroinvertebrate communities and the role ofphysicochemical habitat variables. Hydrobiologia 2006, 560 ,159 173.

[8] Angeler, D. G., Moreno, J. M., Zooplankton communityresilience after press-type anthropogenicstress in temporaryponds. Ecol. Appl. 2007, 17 , 1105 1115.




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Appendix 1

Summary of the environmental characteristics in the connected and isolated ponds from the Upper Paran River oodplain during the study periods (2000 2001 and 2005 2006). WT, water temperature; DO, dissolved oxygen; pH;EC, electrical conductivity; TA, total alkalinity; SD, Secchi disc; Chl, chlorophyll a; NT, total nitrogen; PT, totalphosphorus.

WT (C)DO

(mg L1 ) pHEC

(S cm 1 )TA

(eq L 1 )SD(m)

Chl(g L1 )

NT(g L1 )

PT(g L1 )

Connected ponds2000 2001

Mean 24.9 6.5 6.7 42.9 254.2 0.6 12.9 545.6 149.3Standard deviation 3.7 2.3 0.7 10.9 94.3 0.3 10.0 377.9 216.3

2005 2006Mean 24.9 5.7 6.5 44.9 282.1 1.0 6.6 655.4 40.8Standard deviation 3.8 1.9 0.4 11.6 92.1 0.7 7.0 341.2 21.5

Isolated ponds2000 2001

Mean 24.6 6.3 6.6 36.7 185.0 0.3 20.4 547.6 110.6Standard deviation 4.0 1.8 0.7 13.4 145.9 0.1 24.7 203.7 100.92005 2006

Mean 24.7 5.7 6.4 39.4 237.7 0.4 10.8 1047.6 75.3Standard deviation 4.1 1.9 0.4 11.7 113.7 0.3 10.8 688.2 36.0



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Drought Disturbances Increase Temporal Variability