Trait-based approaches for guiding the restoration of degraded agricultural landscapes in East Africa

Lohbeck, M., Winowiecki, L., Aynekulu, E., Okia, C., & Vågen, T. G.

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Please cite this publication as follows:

Lohbeck, M., Winowiecki, L., Aynekulu, E., Okia, C., & Vågen, T. G. (2018). Trait-based approaches for guiding the restoration of degraded agricultural landscapes in East Africa. Journal of Applied Ecology, 55(1), 59-68. DOI: 10.1111/1365-2664.13017

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J Appl Ecol. 2018;55:59–68. wileyonlinelibrary.com/journal/jpe  | 59© 2017 The Authors. Journal of Applied Ecology © 2017 British Ecological Society

Received:28November2016  |  Accepted:13September2017DOI:10.1111/1365-2664.13017

F U N C T I O N A L T R A I T S I N A G R O E C O L O G Y

Trait- based approaches for guiding the restoration of degraded agricultural landscapes in East Africa

Madelon Lohbeck1,2  | Leigh Winowiecki1  | Ermias Aynekulu1  | Clement Okia3 |  Tor-Gunnar Vågen1

1WorldAgroforestryCentre(ICRAF),Nairobi,Kenya2ForestEcologyandForestManagementGroup,WageningenUniversity,Wageningen,TheNetherlands3WorldAgroforestryCentre(ICRAF),Kampala,Uganda

CorrespondenceMadelonLohbeckEmail:m.lohbeck@cgiar.org

Funding informationForests,TreesandAgroforestry;NederlandseOrganisatievoorWetenschappelijkOnderzoek,Grant/AwardNumber:863.15.017;IUCN,Grant/AwardNumber:IUCN-1124;CGIAR-FTA;AustralianCentreforInternationalAgriculturalResearch,Grant/AwardNumber:FSC/2012/014;InternationalFundforAgriculturalDevelopment,Grant/AwardNumber:2000000520

HandlingEditor:MarneyIsaac

Abstract1. Functionalecologyprovidesaframeworkthatcanlinkvegetationcharacteristicsofvarious land useswith ecosystem function. However, this application has beenmostly limitedto [semi-]naturalsystemsandsmall spatialscales.Here,weapplyfunctionalecologytofiveagricultural landscapesinKenya,UgandaandEthiopia,andasktowhatextentvegetationcharacteristicscontributetosoilfunctionsthatarekeytofarmers’livelihoods.

2. WeusedtheLandDegradationSurveillanceFramework(LDSF),amulti-scaleas-sessmentoflandhealth.EachLDSFsiteisa10×10kmlandscapeinwhichvegeta-tioncoveranderosionprevalenceweremeasured,atreeinventorywascarriedout,andtopsoil(0–20cm)sampleswerecollectedfororganiccarbon(SOC)analysisinapproximately160×1,000m2plots.Landdegradationisarecurringphenomenonacrossthefivelandscapes,indicatedbyhigherosionprevalence(67%–99%oftheplotswereseverelyeroded).Weusedmixedmodelstoassessifvegetationcover,above-groundwoodybiomassandthefunctionalpropertiesofwoodyvegetation(weighted-meantraitvalues,functionaldiversity[FD])explainvariationinSOCanderosionprevalence.

3. Wefoundthatthevegetationcoverandabove-groundbiomasshadstrongpositiveeffectsonsoilhealthbyincreasingSOCandreducingsoilerosion.Aftercontrollingforcoverandbiomass,wefoundadditionalmarginaleffectsoffunctionalproper-tieswhereFDwaspositivelyassociatedwithSOCandtheabundanceofinvasivespecieswasassociatedwithhighersoilerosion.

4. Synthesis and applications.Thisworkillustrateshowfunctionalecologycanprovidemuch-needed evidence for designing strategies to restore degraded agriculturallandandtheecosystemservicesonwhichfarmersdepend.Weshowthattoensuresoilhealth,itisvitaltoavoidexposedsoil,maintainorpromotetreecover,whileensuringfunctionaldiversityoftreespecies,andtoeradicateinvasivespecies.

K E Y W O R D S

agriculturalland,agroecology,agroforestry,erosion,functionaldiversity,functionaltraits,landdegradation,soilhealth,soilorganiccarbon,vegetation

60  |    Journal of Applied Ecology LOHBECK Et aL.

1  | INTRODUCTION

Thenegativeimpactsoflanddegradationonproductivity,biodiversityand local livelihoods have become undeniable (Pereira etal., 2010;Pimentel&Burgess,2013).Asaconsequence,restoration,heredefinedas the practice of assisting the recovery of degraded ecosystems, isnowaglobalpriority (Minnemeyer,Laestadius,Sizer,SaintLaurent,&Potapov,2011).Restorationprovidesopportunitiestocounteractdeg-radationandreviveecosystemfunctions,includingcomponentsofbio-diversity(Benayas,Newton,Diaz,&Bullock,2009;Chazdon,2008)andsoilfertility,whichiskeytofarmers’livelihoods(Diemontetal.,2006).

In this study, we assess degradation in agricultural landscapesusingtwomainindicators:soilorganiccarbon(SOC)anderosionprev-alence.SOC isawidelyused indicatorofsoilhealthas it influencesseveralimportantsoilpropertiessuchascationexchangecapacityandwaterholdingcapacity(Lal,Griffin,Apt,Lave,&Morgan,2004).Soilerosion is an indicatorof landdegradation and is includedas a keyprocessleadingtolossofSOCanddecliningsoilhealthandproduc-tivity(Dregne,2002).Bothindicatorsareheavilyinfluencedbyman-agement,andunsustainablelandusehasbeenshowntoreduceSOCand increaseerosion,making these suitable indicators for assessinglanddegradationandsoilhealth(Dregne,2002;Laletal.,2004;Vågen,Winowiecki,Abegaz,&Hadgu,2013;Winowieckietal.,2015).

Increasingtreecoverisacoreactivityforrestoringdegradedlands(Lamb,Erskine,&Parrotta,2005).Recentevidenceshowsthatincreas-ingtreecover inthedrytropicscan improvesoil function, includingwateravailability(Ilstedtetal.,2016).Furthermore,increasingwoodybiomasspositivelyaffectsproductivityandlitterdecompositionratesinregeneratingforests(Lohbeck,Poorter,Martínez-Ramos,&Bongers,2015)andSOCinagroforestrysystems(Hombegowda,vanStraaten,Köhler,&Hölscher,2016;Lorenz&Lal,2014).However,theinfluenceoftreesonsoilhealthmaydifferfordifferenttreespecies,andunder-standing this is crucial fordesigningeffective restoration strategies.Insightscanbegainedfromthefieldoffunctionalecology(Laughlin,2014;Sandel,Corbin,&Krupa,2011),whichprovidesaframeworktomechanistically link landusewithspecies’ functional traitsandeco-systemfunction(e.g.Cadotte,Carscadden,&Mirotchnick,2011;Díazetal.,2007;Lavoreletal.,2010).

Plantfunctionaltraits,andatacoarsebiologicalscalefunctionaltypes,areindicatorsofplantstrategiesandofhowspeciesinfluenceecosystem function (Petchey & Gaston, 2006). Accordingly, many

plant functional traits and types contribute to soil health (Table1).Wooddensity, for instance, indicates species’ positioningalong the“resource-economics spectrum” (Chave etal., 2009). High-wooddensityspecieshaveexpensive-to-constructtissuesthatdecomposeslowly,andtherebyhaveamoreconstantandlastingpositiveeffectonSOCinputs(deDeyn,Cornelissen,&Bardgett,2008).Functionaltraitsthatdescribethearchitectureoftreesmayinfluencesoilhealthby altered understorey climatic conditions. For instance, trees thathaveatallandnarrowgrowthformwillshadethesoiltoalesserex-tentandmayincreasetemperature,decreasesoilmoistureandnega-tivelyaffectsoilhealth(Chapin,2003;Linetal.,2016).Furthermore,certain functional types are known to have specific effects on soilhealth.Treesabletofixatmosphericdinitrogen(N2)dosobymutu-alisticsymbiosiswithbacteria,resultinginfastergrowth(Battermanetal.,2013)andenhancedsoilhealth(e.g.Adams,Turnbull,Sprent,&Buchmann,2016;Bradfordetal.,2002).Deciduousspeciesundergoleaf senescence forpartof theyear, therebyproducing largequan-titiesof litterfororganic-carbon inputs intothesoil (deDeynetal.,2008).Incontrast,somefunctionaltypesareknownfortheirnegativeimpactsonsoilhealth:invasivespecieshavebeenassociatedwithin-creasederosion(Grover&Musick,1990;Vågen&Winowiecki,2014),decreasedecosystemcarbon(Jackson,Banner,Jobbágy,Pockman,&Wall,2002)anddecreasedstreamflow(Cleverly,Smith,Sala,&Devitt,1997).Also commonlyplanted exotics such asEucalyptus spp.mayreduceunderstoreyvegetationcoveranddiversity(Thijsetal.,2014)andnegativelyimpacthydrology(Zhou,Morris,Yan,Yu,&Peng,2002;butseeReynolds,Wassie,Wubalem,Liang,&Collins,2016).

Besides predictions on how species-level functional traits andtypes influenceecosystem function, twomain theoriesexplainhowthe traits of species co-occurring in a community (community-levelfunctional properties) influence ecosystem function.Themass-ratiohypothesispredictsthatthetraitsofthedominantspeciesdrivefunc-tions(Grime,1998),whilethenichecomplementarityhypothesispre-dicts that functionally diverse communities are better able tomakeoptimaluseofavailableresourcesandtherebyincreaseoverallfunc-tionality(e.g.Cardinaleetal.,2012).

We evaluate the extent towhich vegetation contributes to soilhealth.We do so by assessing a hierarchy of vegetation indicatorsthatreflectincreasinglydetailedcharacteristicsofthevegetationandthereby systematically assesswhat aspects ofvegetation should bepromotedforrestoringdegradedlandscapes.

TABLE  1 Summaryofthehypothesizedrelationshipsbetweenfunctionaltraits/typesandsoilhealth.+/−indicatepositive/negativepredictedeffectsonsoilhealth,indicatedbySOC(soilorganiccarbon)(positively)anderosion(negatively)

Functional trait/ type Plant strategies and ecosystem function

Effect on soil health

Wooddensity Conservativestrategy,slowgrowth,slowdecomposition,above-groundbiomass +

Adultheight Lightdemanding,moreevapotranspiration,above-groundbiomass,tallarchitecturecausinglessshading −

N2-fixing Fastgrowth,highfoliarnitrogen,N-mineralization,soilnitrification +

Deciduous Lessevapotranspiration,fasterdecomposition,morelitterproduction,shallowroots,highwooddensity +

Invasive Out-competingoriginalvegetationcover,fastgrowthandreproduction −

Exotic Fastgrowth,lightdemanding,reducedsoilwateravailability −

     |  61Journal of Applied EcologyLOHBECK Et aL.

We hypothesize that: (i) increased vegetation cover reducessoildegradation(increasesSOCanddecreaseserosion); (ii)above-groundwoodybiomassreducessoildegradation;and(iii)functionalpropertiesofthevegetationaffectsoildegradation.Specifically,(a)increasedfunctionaldiversity(FD)reducessoildegradation,(b)par-ticularfunctionaltraits(highwooddensity,lowadultheight)reducesoildegradation,and(c)particularfunctionaltypesofwoodyvege-tation (N2-fixers,deciduousspecies) reducesoildegradationwhileotherfunctionaltypes(invasivespecies,exoticspecies)increasesoildegradation.

2  | MATERIALS AND METHODS

2.1 | Study sites

Thestudytookplaceinfiveagriculturallandscapesinthreecountriesin East Africa (Figure1). All landscapes are characterized by small-holder farming systems and are degraded, indicated bywidespreaderosion. Table S1 summarizes key climatic variables and vegetationtypesperlandscape,whileFigureS1givesthevariationinvegetationstructurefoundacrosslandscapes.

In Uganda, we focused on two landscapes in eastern Uganda,bordering Mount Elgon National Park: Mbale (34.24E, 1.09N) andBumagabula (34.39E, 1.16N).The area is characterized by amoun-tainoustopography,whereBumagabulaislocatedathigherelevationandhashigherrainfallthanMbale.Maize,legumes,bananaandcoffeeare commonly cultivated, often in agroforestry systems,with someeucalypt plantations and cattle grazing areas. The region has highpopulationdensities,estimatedat620personsperkm2in2002(UBS,

2012).InEthiopia,wefocusedontwolandscapes,thesubhumidAno(36.97E,9.09N)andthesemi-aridAlemTena(38.90E,8.24N).Inbothsites,themaincropsweresorghum,maizeandteff,withtreescom-monlyintegratedintofarmingsystems(Iiyamaetal.,2016).InKenya,wefocusedononelandscape,Waita(38.19E,0.91S),inKituicounty.Thisisalowlandsitewheresmallholderfarmerscultivatemaize,milletandsorghumwithsmall-scalecattleproduction.Waitaisthedriestofourlandscapeswithanannualrainfallof767mmperyear.

2.2 | Sampling framework

TheLandDegradationSurveillanceFramework(LDSF)wasusedtoas-sessbiophysicalindicatorsatthefivelandscapesites.TheLDSFusesahierarchicalsamplingframework;eachsiteis100km2,andconsistsofsixteen1-km2clusters,eachclusterconsistsoften1,000-m2sam-pling plots and each plot consists of four 100-m2 subplots (Vågen,Winowiecki, Tamene Desta, & Tondoh, 2013). Positioning of siteswasbasedonongoingprojectactivitiesinareasofinterest.Locationswererandomizedtocovervariationintopographyandlanduseswhileavoiding lakes and rivers. The LDSF is designed for simultaneouslyassessingkey indicatorsofecosystemhealthacrossmultiple spatialscalesandatgeo-referencedlocations.

2.3 | Soil health indicators

Soilerosionprevalencewasscoredateachsubplot(n=640observa-tionspersite),whenerosionwasobservedinoverhalfofthefoursub-plotsperplot,thisplotwasconsideredtobeseverelyeroded(binary0/1).Topsoilsamples (0–20cm)werecollectedateachsubplotand

F IGURE  1 MapsofthefivestudylocationsacrossthreecountriesinEastAfrica.SeeTableS1andmethodsformoreinformation

MbaleBumagabula

Uganda

Alem Tena

Ano

Ethiopia

Waita

Kenya

62  |    Journal of Applied Ecology LOHBECK Et aL.

thoroughlymixedtoformacompositetopsoilsampleforeachplot.SOCandsandcontentweremeasuredthroughMIRabsorbance,de-tailedmethodsofwhicharepresentedinAppendixS1.Mid-infraredspectroscopy is becoming awell-establishedmethod for predictingsoil properties (cf.Madari etal., 2006; Reeves, Follett,McCarty, &Kimble,2006;Vågen,Winowiecki,Abegaz,etal.,2013).Tenpercentofthesoilsamplescollectedateachsitewereconsideredreferencesamples(n=32persite)andwereanalysedforSOCandsandcontent.Calibrationmodelsweredevelopedforthepredictionofsoilproper-tiesusingMIRspectra from the ICRAFpan-AfricanMIRspectral li-braryandtheresultsofsoilanalysisonthereferencesamples(Vågen,Winowiecki,Abegaz,etal.,2013;Vågen,Winowiecki,Tondoh,Desta,&Gumbricht,2016).Thismethodhasbeenshowntoaccuratelypre-dictSOCacrossSub-SaharanAfrica(Vågenetal.,2016).

2.4 | Vegetation cover and biomass estimations

Vegetationcoveringthesoil(mainlyherbsandgrasses)wasratedineachofthesubplotsusingaBraun–Blanquetvegetationratingscalethatrangesfrom0(exposedsoil)to5(>65%cover;Braun-Blanquet,1932).Plot-levelvegetationcoverrepresentsthemeanofthevegeta-tioncoverclassesfromthefoursubplots.Treeinventorieswerecar-riedoutinslightlydifferentwaysdependingonthesite,asexplainedindetailinAppendixS2.Weestimatedplot-levelabove-groundbiomass(Mg/ha)usingagenericallometricformulabasedonthediameteratbreastheight(DBH),species-specificwooddensityandasite-specific“environmentalstressfactor”(Chaveetal.,2014).Thiswasexpressedonaper-hectarebasisasandisthuscorrectedfordifferencesinplot-levelsamplingeffortacrossthesitesandplots.

2.5 | Functional properties of the woody vegetation

Atotalof2,673treesbelongingto137differentspecieswereidenti-fiedacrossthefivelandscapes.Dataforanumberofrelevantfunc-tionaltraitsandtypeswereretrievedfromflorasandonlinesourcesforthetreespecies:Wooddensity(g/cm3),adultheight(m),N2-fixing(0/1),deciduous(0/1), invasive(0/1)andexotic (0/1),forwhichde-tailedmethodsarepresentedinAppendixS3.

Species-levelfunctionaltraitswerescaledtoplot-levelfunctionalproperties using two complementary metrics: community-weightedmean (CWM)andFD.TheCWM (Garnier etal., 2004) is calculatedbasedoneachsingle traitor typeandweightedbyspecies’ relativebasalareaintheplot.Forcontinuoustraitvalues,theCWMreflectsthe trait value of “theweighted-averagewoody plant” in the com-munity, forbinaryvariables this reflects theproportionof thebasalareathat isrepresentedbythattype.FDwascalculatedusingRao’squadraticentropy(Rao’sQ) (Botta-Dukát,2005)andisbasedonthefunctionaldistancebetweenspeciesweightedbytheirrelativebasalareas,makinguseofalltraitssimultaneously.Rao’sQisconceptuallysimilartofunctionaldispersion(Laliberté&Legendre,2010)andesti-mateshow functionallydifferent theco-occurring speciesare.Plot-levelfunctionalpropertieswerecalculatedusingtherpackage“FD”(Laliberté&Shipley,2012).

2.6 | Statistical analysis

In this study,we took theplot as aunit of replication,with a totalof745plots.Weusedgeneralized linearmixedmodels, from ther package“lme4”(Bates,Maechler,Bolker,&Walker,2015)tosystem-aticallytestfortheeffectsofvegetationonsoilhealthinaseriesofmodelsthatreflectincreasedcomplexity(Table2).

Mixed-effectsmodelsenableaccountingfordifferencesincross-sitesamplingdesign,bytakingsiteasarandomeffect,allowingarandomintercept for each site. With package “LMERConvenienceFunctions”(Tremblay&Ransijn,2015),weconfirmedthatsiteindeedcontributedasarandomeffect.Inmodel5,wesystematicallyreplacedthedifferentplot-levelfunctionalproperties(6CWM+1FD=7variationsonmodel5),resultingin12modelspersoilhealthindicatorand24modelsintotal.

Themodelwith thebest fitwas selectedbasedonAkaike infor-mation criterion, adjusted for small sampling size (AICc) (Burnham&Anderson,2002).AICpenalizesformodelcomplexity,hencetakingaconservativeapproachtoassessingtheimpactsoftreesandfunctionaltraitsonsoilhealth.Whenmodelsdidnotdiffersignificantly(ΔAICc < 2),wechosethemodelthathadthehighestmarginalandconditionalR2 (Nakagawa,Schielzeth,&O’Hara,2013), computedusingpackage“piecewiseSEM”(Lefcheck,2015).Forsevereerosion(binary,0/1),weusedglmer(family=binomial)whileforSOC(continuous,range3–96g/kg)weusedlmer.Modelstatisticswerederivedusingpackages“sjstats”and “sjPlot” (Lüdecke,2016a,2016b),while significance levels reflectthez-associatedp-value (forerosion),or the t-associatedp-value (forSOC)derivedusing“nlme” (Pinheiro,Bates,DebRoy,&Sarkar,2016).Allanalyseswerecarriedoutusingrversion3.2.4(RCoreTeam,2014).

3  | RESULTS

3.1 | Site conditions

ThefiveEastAfricanstudysitesrepresentalargevarietyofclimatic,topographicalandland-usecharacteristics(FiguresS1andS2).Erosionwaswidespreadacrossthesites(67%–99%acrosseachlandscape),in-dicatingtheneedformoresustainablelandmanagementpracticesandland restorationactivities.Average topsoilOCwas29.8g/kg±13.2forBumagabula,27.9g/kg±4.2 forAno,21.2g/kg±8.3 forMbale,14.3g/kg±4.0forAlemand10.1g/kg±4.0forWaita(FigureS2).

3.2 | Optimal model

The most complex model, with the largest number of variables(Table2,model5),bestexplainedSOCandsoilerosion.Thismodelincludedsoiltexture(sandcontent),vegetationcover,above-groundwoodybiomassand functionalpropertiesof thewoodyvegetation.Wefoundthatsoilhealth(lowererosionandhigherSOC)wasassoci-atedwithhighervegetationcoverandhigherabove-groundbiomass,asexpected.Aftercontrollingforthese,wefoundthatdistinctfunc-tional properties related to distinct aspects of soil health; invasivespecieswereassociatedwith increasederosionwhileFDwasasso-ciatedwith increasedSOC (Figures2and3,Table3).Althoughour

     |  63Journal of Applied EcologyLOHBECK Et aL.

modelselectionsuggestsaroleforfunctionalpropertiesofthewoodyvegetationinexplainingsoilhealth,theirmarginaleffectsalonewerenotsignificant.Thevarianceexplainedbythetotalmodelforsevereerosionwas40%(32%forfixedfactorsalone),whilethevarianceex-plainedforSOCwas56%(11%forfixedfactorsalone).Modelfitdidnotimprovewhenallowingthesitestodifferinthevegetationindica-tors’ fixedfactoreffects,suggestingthattheeffectsfoundarecon-sistentacrossthesites.TableS2givestheinterceptsacrossthesites.

4  | DISCUSSION

Restoration of agricultural landscapes provides an opportunity to in-creasetheproductivityandresilienceofagriculturalsystemsandsimul-taneouslycontributetoconservationobjectives.Functionalecologyis

a promising tool to guide science-based restoration (Laughlin, 2014)though its application to managed agricultural landscapes has beenlagging(Woodetal.,2015).Inthisstudy,weappliedatrait-basedap-proachtosoilhealthindegradedagriculturallandscapesandfoundthatthemarginaleffectsof thevegetationandtheir functionalpropertiesweredirectionallyintuitiveandhadclearimplicationsforrestoration.

4.1 | Vegetation effects on soil health

Wefound thatvegetationcoverandabove-groundbiomassare im-portantforsoilhealthashighervalueswereassociatedwithincreasedSOCanddecreasederosion.Wealsofoundmarginaladditionaleffectsforthefunctionalpropertiesofthewoodyvegetation.Invasivespecieswereassociatedwithincreasederosion,whileFDwasassociatedwithincreasedSOC.

TABLE  2 Themodelstestedinthisstudythatreflectincreasinglydetailedinformationonthevegetationtoexplainsoilhealth(erosionandSOC(soilorganiccarbon)).Givenaretherationaleforeachmodelandtheimplicationsforrestoration

# Model Rationale Implications for restoration

1 Soilhealth~Intercept Datacannotexplainsoilhealth None

2 Soilhealth~Sandcontent Soiltextureexplainssoilhealth None

3 Soilhealth~Sandcontent+Vegetationcover Vegetationcovercontributestosoilhealth Promotevegetationcover

4 Soilhealth~Sandcontent+Vegetationcover+Above-groundwoodybiomass

Above-groundbiomasscontributestosoilhealth

Plantandpromotetrees

5 Soilhealth~Sandcontent+Vegetationcover+Above-groundwoodybiomass+Functionalproperties(CWM/FD)

Functionalpropertiescontributetosoilhealth See5aand5b

5a Soilhealth~Sandcontent+Vegetationcover+Above-groundwoodybiomass+Community-weightedmeanfunctional-traitvaluesa

Functionaltraitsofthedominantspeciescontributetosoilhealth(mass-ratioeffect)

Plantandpromotespecificfunctionaltypesoftrees(andavoidothers)

5b Soilhealth~Sandcontent+Vegetationcover+Above-groundwoodybiomass+Functional-traitdiversity

Functionaldiversitycontributestosoilhealth(nichecomplementarityeffect)

Plantandpromoteadiverserangeoffunctionaltypesoftrees

aCWMsarecalculatedforsingletraits,sothismodelwastestedforeachofthesixfunctionaltraitsandtypes,seeTable1forspecifichypotheses.

F IGURE  2 Marginaleffectsoffixedeffectspredictingtheprobabilityofencounteringsevereerosion

0 10 20 30 40 50 60 70

0.0

0.2

0.4

0.6

0.8

1.0

Sand content (%)

Pre

d. p

rob.

of s

ever

e er

osio

n

0 1 2 3 4 5

0.0

0.2

0.4

0.6

0.8

1.0

Vegetation cover score

Pre

d. p

rob.

of s

ever

e er

osio

n

0 1 2 3 4 5 6

0.0

0.2

0.4

0.6

0.8

1.0

Above-ground biomass (Mg/ha)

Pre

d. p

rob.

of s

ever

e er

osio

n

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Rel. basal area invasive spp.

Pre

d. p

rob.

of s

ever

e er

osio

n

64  |    Journal of Applied Ecology LOHBECK Et aL.

Our results substantiated that functional traits affect soil carbon(deDeynetal.,2008)anderosion (Lorenz&Lal,2005;Stokes,Atger,Bengough,Fourcaud,&Sidle,2009).Ourfindingssuggestthatthemech-anismbywhichthefunctionalpropertiesinfluencesoilhealthdependsontheindicator;wefoundthaterosionresistanceisdrivenbythetraitsofthedominantspecies(mass-ratioeffect),whileSOCwasdrivenbythediversityoftraitsintheecosystem(nichecomplementarityeffect).

4.1.1 | Erosion

Above-groundvegetationquantity(coverandbiomass)isdirectlyre-latedtobelow-groundvegetationquantityand,notsurprisingly,root

quantityanddistributioninthesoilareofhugeimportancetopreventerosion (e.g.DuránZuazo&RodríguezPleguezuelo, 2008;Gyssels,Poesen,Bochet,&Li,2005;Stokesetal.,2009).Therearelargeinter-specificdifferencesineffectsonsoilstability(Berendse,vanRuijven,Jongejans,&Keesstra,2015;Stokesetal.,2009),whichmaybedrivenbydifferences inspeciestraits.Wefoundthathigherabundanceofinvasive species was associated with increased erosion, suggestingthatthetraitsofthedominantspecies,andnotthediversity,explainerosion. Increased erosion under invasive species has been repeat-edly documented (Grover & Musick, 1990; Kourtev, Ehrenfeld, &Häggblom,2002;Vågen&Winowiecki,2014).Possiblemechanismsincludethatinvasivespeciestendtoinvestlessinsoil-stabilizingroot

F IGURE  3 Marginaleffectsoffixedeffectspredictingsoilorganiccarbon

0 10 20 30 40 50 60 70

010

2030

40

Sand content (%)

Soi

l org

anic

car

bon

(g/k

g)

0 1 2 3 4 5

010

2030

40

Vegetation cover score

Soi

l org

anic

car

bon

(g/k

g)

0 1 2 3 4 5 6

010

2030

40

Above-ground biomass (Mg/ha)

Soi

l org

anic

car

bon

(g/k

g)

0.00 0.02 0.04 0.06 0.08 0.10 0.12

010

2030

40

Functional diversity (Rao's Q)

Soi

l org

anic

car

bon

(g/k

g)

TABLE  3 Fixed-effectsstatisticsfortheoptimalmodelsexplainingsoilhealth:(a)severeerosionprevalenceand(b)SOC(soilorganiccarbon).Givenarethebetaestimates,theoddsratioandassociatedconfidenceintervals(forerosion)orstandardizedbetaestimateandassociatedconfidenceintervals(forSOC).p-valuesreflectthez-associatedp-value(forerosion),orthet-associatedp-value(forSOC).Site(#=5)wasincludedasarandomeffectforallmodels,totalN=745

(a) Severe erosion (R2conditional

0.40, R2marginal

0.32)

Predictor Estimate Odds ratio CI p

Intercept 3.73 41.59 15.79to109.57 <.001

Sandcontent 0.005 1.01 0.99to1.02 .546

Vegetationcover −0.708 0.49 0.39to0.62 <.001

Above-groundbiomass −0.536 0.59 0.16to2.19 .427

CWMinvasives 0.919 2.51a 0.62to10.13 .197

(b) Soil organic carbon (SOC) (R2conditional

0.56, R2marginal

0.11)

Predictor Estimate CI Std. estimate CI p

Intercept 22.4 16.41to28.43 <.001

Sandcontent −0.28 −0.33to−0.23 −0.35 −0.42to−0.29 <.001

Vegetationcover 0.89 0.32to1.47 0.17 0.06to0.28 .014

Above-groundbiomass 3.89 0.22to7.57 0.05 0.00to0.10 .038

Rao’sQ 4.12 −18.97to27.20 0.01 −0.04to0.06 .726

aProbabilityoferosionunderinvasivespeciesisthen(41.59×2.51)/(1+41.59×2.51)=0.99.

     |  65Journal of Applied EcologyLOHBECK Et aL.

biomasscomparedwithnoninvasivespecies(vanKleunen,Weber,&Fischer,2010)andthat invasivespecies inhibitunderstoreyvegeta-tioncover.Althoughtheeffectof invasivespecieswasby itselfnotstatisticallysignificant,theeffectssizesuggestedthataninvadedsitehasa99%chancetobeseverelyeroded(Table3).Thisisnoteworthygiventhatinvasivepropertiesofaspecies,asfoundintheliterature,reflectthespecies’potentialtoinvadeandnotwhetheritisactuallyinvadingthesite.Besidesbeingapotentialdriverofdegradation,in-vasivespeciescanalsobeasymptomofdegradation.Possibleposi-tivefeedbackmechanismsregardinginvasivespeciesanddegradationcouldpotentiallyleadtoirreversibledegradationifrestorationeffortsare not implemented in time. Our result confirms that decreasingtheabundanceofinvasivespeciesshouldbeapriorityinrestorationefforts.

4.1.2 | Soil organic carbon

Soilcarbonstocksresultfromthebalancebetweencarboninputviaprimary productivity and carbon output via decomposition, volatili-zation (e.g. by charring or burning), leaching and erosion of topsoil(Amundson,2001).Wefoundthatvegetationcoverandbiomassin-creasedSOC.Indeed,coverandbiomassreduceerosion,asdiscussedintheprevioussection.Above-groundbiomassisadriverofprimaryproductivity (Lohbeck etal., 2015), although it may also acceleratedecomposition by enhancing soilmoisture by reducing evaporation(Lebrija-Trejos, Pérez-García, Meave, Poorter, & Bongers, 2011).Further,morebiomassgenerallyproducesmorelitter(Lohbecketal.,2015), providingaprimary input forSOC.Wealso foundaneffectof FD on SOC, suggesting that resource-use complementarity in aplantcommunity,possiblyincombinationwithfacilitation,enhancesSOCcontent.Previousresearchsimilarlyreportedthenichecomple-mentarityeffecttobeamajordriverofSOCinexperimentalgrass-lands (Fornara&Tilman,2008)and inagroforestrysystems in India(Hombegowda etal., 2016). In contrast, a recent study in Chinesesubtropical forest showed that SOC was mainly influenced by thecommunity-weightedmaximumheight of the trees, and less byFD(Linetal.,2016).

Consistentwithfunctionalecologytheory(Díazetal.,2007),ourresults suggest that functional traitsplay a role in carbondynamicsbymediatingspeciesdifferences inproductivityanddecomposition.Empirical evidence supports that niche complementarity drives pri-maryproductivityintropicalforest(Haggar&Ewel,1997)aswellasin temperate grasslands (Wilsey & Potvin, 2000). Other studies in-steadsupportthemass-ratiohypothesisshowingthatthefunctionaltraitsofthedominantspeciesdriveproductivity(Paquette&Messier,2011;Warren,Topping,&James,2009).Similarlyforlitterdecompo-sition,studieshavefoundbothdiversityeffects(Finertyetal.,2016;Scherer-Lorenzen,2008)andeffectsofthetraitsofdominantspeciesondecompositionrates(Garnieretal.,2004;Tardif&Shipley,2013).Probably both mechanisms matter for ecosystem function (Handaetal.,2014;Lohbecketal.,2015).Ourdiversity-effectcouldindicatea direct diversity-effect of vegetation on SOC through productivityanddecomposition (Hooperetal.,2005),butalsoan indirecteffect

mediatedbysoilbiota(Zak,Holmes,White,Peacock,&Tilman,2003).Thissuggeststhatwhenfarmersdecidetoplanttreesontheirfields,itisbeneficialtochoosespeciesthatarefunctionallycomplementarytotheonesalreadyestablished.

4.2 | Small marginal effects of functional properties

The variances explainedby the fixed effectswere quite small, par-ticularlyforerosion(Rm0.11).Highlevelsofsevereerosionacrossourlandscapes (67%–99%) reduced the variation in which vegetation-effectscouldbedetected.Ouralternativemodelsweredesignedtoreflect increasinglydetailedaspectsofthevegetation,takingacon-servative approach to themarginal effects of functional properties,whichpartlyexplainswhyeffectsweresmallandstatisticallynotsig-nificant(Table3).Itisimportanttorecognizethatthisobservationalstudyrepresentsalargevariationof landscapesshapedbydifferentpeopleandlandmanagementpractices.Thereisagreatneedtotestwhetherfunctional-traiteffectsonsoilfunctionscanbedetectedindynamichuman-modified landscapes,andwhat the implicationsareforrestoration,whichiswhatweexploredinthisstudy.Althoughthemarginaleffectsof functionalpropertiesare small,weconsiderourfindingsimportantbecausefunctionalpropertiesofthevegetationcaneasilybemodifiedbyselectingspecieswithsuitablefunctionaltraitswhenplantingtreesonfarmland.Thisapproach,thus,contributestoamuch-neededevidence-baseforrestoringagriculturallandscapes.

4.3 | Synthesis and applications

Based on our findings,we are able to draw recommendations thatwill advance the fieldof functional ecology inmanagedagriculturallandscapes.We showed that (nonwoody) vegetation cover stronglyinfluencedsoilproperties,suggestingthat includingfunctional traitsof nonwoody vegetation will increase our understanding of trait-mediated effects of vegetation on soils. Besides the direct effectsthatplantsexertonsoilfunctions,therearesomeimportantindirectlinkages between plants and the soil, mediated through manage-ment, symbionts and soil biota.Managementpractices, such as till-age,theuseoffireandfertilizers,werenotincludedinouranalyses.Managementdirectlyaffectssoilfunctionbutalsoindirectlythroughthe vegetation. We were constrained to functional traits availablefromonline sources and floras,which is a limited subset of above-ground traits and limited towoodyvegetation.Below-groundplanttraits(relatedtorootbiomassandturnover)areofparticularimpor-tance for soil functions (McCormack etal., 2015; Prieto, Stokes, &Roumet,2016;Schroth,1995).Futureresearchonfunctionalecologyinagriculturallandscapeswillneedtoincludetraitsofnonwoodyandcultivatedspecies,andmoreexplicitlyincludethedirectandindirecteffectsofmanagementonplantcommunitiesandonsoilhealth.

Understanding the functionalecologyofmanagedsystems isanimportant step towards making informed decisions on restorationplanning,bothattheplot-levelandatlandscape-scale.ApplyingthisapproachtodegradedEastAfricanlandscapes,wesuggestthatinad-ditiontoavoidingexposedsoilandpromotingtreesonfarms,priority

66  |    Journal of Applied Ecology LOHBECK Et aL.

shouldbegiventotheremovalofinvasivespeciesandpromotionofhigherFDoftreesonfarmsforrestoringimportantsoilfunctionssuchasSOCandincreasedresistancetoerosion.

ACKNOWLEDGEMENTS

Wearegratefultothecommunitiesandlocalofficialsfortheirsup-portandforgrantingpermissiontocollectdataontheirproperties.AcknowledgementsareextendedtoJohnThiongoMaina,HumphreyWanjohi, Kabonesa Bernadette, Sam Chemusto and TesmesgenYohannesduringfieldcampaigns,aswellastothestaffoftheICRAFSoilandPlantSpectroscopyLaboratory inNairobi forprocessingofsoilsamples.WethankFaithMusiliforassistancewiththetraitdata-baseandDanielLüdeckeandEdwinLebrija-Trejosforstatisticalad-vice.FieldworkandsoilanalysiswerefundedbyIUCN(IUCN-1124),ACIAR (Trees for food security; FSC/2012/014), IFAD (Restorationof Degradation Lands for Food Security and Poverty Reduction inEastAfricaandtheSahel;2000000520)andtheCGIARProgramonForests, Trees and Agroforestry (FTA). M.L. was supported by re-searchprogrammeALW (863.15.017), financedby theNetherlandsOrganisationforScientificResearch(NWO).

AUTHORS’ CONTRIBUTIONS

M.L.,T.-G.V.andL.W.conceived the ideasanddesigned themeth-odology.M.L., T.-G.V., L.W., E.A. andC.O. collected thedata.M.L.,T.-G.V.,L.W.analysedthedata,M.L.ledthewritingofthemanuscript.Allauthorscontributedcriticallytothedraftsandgavefinalapprovalforpublication.

DATA ACCESSIBILITY

LDSF sites data are available from Harvard Dataverse: Ethiopia:https://doi.org/10.7910/dvn/29652 (Aynekulu & Shepherd, 2015),Kenya: https://doi.org/10.7910/dvn/sbl27o (Winowiecki & Sinclair,2016), Uganda: https://doi.org/10.7910/dvn/eqfm2n (Vågen etal.,2017).

ORCID

Madelon Lohbeck http://orcid.org/0000-0002-3959-1800

Leigh Winowiecki http://orcid.org/0000-0001-5572-1284

Ermias Aynekulu http://orcid.org/0000-0002-1955-6995

Tor-Gunnar Vågen http://orcid.org/0000-0002-8699-731X

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How to cite this article:LohbeckM,WinowieckiL,AynekuluE,OkiaC,VågenT-G.Trait-basedapproachesforguidingtherestorationofdegradedagriculturallandscapesinEastAfrica.J Appl Ecol. 2018;55:59–68. https://doi.org/10.1111/1365-2664.13017