The Multi-objective Spanish National Forest Inventory
Forest Systems26(2), e04S, 17 pages (2017)eISSN: nstituto Nacional de Investigación y Tecnología Agraria y Alimentaria O. A., M. P. (INIA)RESEARCH ARTICLEOPEN ACCESSThe multi-objective Spanish National Forest InventoryIciar Alberdi1, Roberto Vallejo2, Juan G. Álvarez-González3, Sonia Condés4, Eduardo González-Ferreiro3,5,6, Silvia Guerrero1, LauraHernández1, Maria Martínez-Jauregui1, Fernando Montes1, Nerea Oliveira1, Maria Pasalodos-Tato1, Elena Robla2, Ana D. RuizGonzález3, Mariola Sánchez-González1, Vicente Sandoval2, Alfonso San Miguel4, Hortensia Sixto1, and Isabel Cañellas1INIA, Centro de Investigación Forestal (CIFOR). Ctra. La Coruña, km 7.5. 28040 Madrid, Spain.2 Ministerio de Agricultura, Alimentación y MedioAmbiente. C/ Gran Vía de San Francisco 4. 28005 Madrid, Spain. 3 Universidad de Santiago de Compostela. Escuela Politécnica Superior. Dept. deIngeniería Agroforestal. Campus Universitario s/n. 27002 Lugo, Spain 4 Technical University of Madrid. School of Forestry. Dept. of Natural Systemsand Resources. Ciudad Universitaria s/n. 28040 Madrid, Spain. 5 Oregon State University, Dept. of Forest Ecosystems and Society (FES). 321Richardson Hall. O-97331 Corvallis, OR, USA. 6 US Forest Service. Laboratory of Applications of Sensing in Ecology (LARSE). Pacific NorthwestResearch Station, 3200 SW Jefferson Way. O-97331 Corvallis, OR, USA.1AbstractAim of study: To present the evolution of the current multi-objective Spanish National Forest Inventory (SNFI) through theassessment of different key indicators on challenging areas of the forestry sector.Area of study: Using information from the Second, Third and Fourth SNFI, this work provides case studies in Navarra, La Rioja,Galicia and Balearic Island regions and at national Spanish scale.Material and Methods: These case studies present an estimation of reference values for dead wood by forest types, diameter-agemodeling for Populus alba and Populus nigra in riparian forest, the invasiveness of alien species and the invasibility of forest types,herbivore preferences and effects on trees and shrub species, the methodology for estimating cork production , and the combination ofSNFI4 information and Airborne Laser Scanning datasets with the aim of updating forest-fire behavior assessment information with ahigh degree of accuracy.Main results: The results show the suitability and feasibility of the proposed methodologies to estimate the indicators using SNFIdata with the exception of the estimation of cork production. In this case, additional field variables were suggested in order to obtainrobust estimates.Research highlights: By broadening the variables recorded, the SNFI has become an even more important source of forest informationfor the development of support tools for decision-making and assessment in diverse strategic fields such as those analyzed in this study.Additional keywords: invasive species; forest modelling; dead wood; browsing impact; fire hazard; cork productionAbbreviations used: AIC (Akaike s information criterion); ALS (airbone laser scanning); CBD (canopy bulk density); CBH (canopybase height), CFL (canopy fuel load); DWV (dead wood volume); NFI (National Forest Inventory); SNFI (Spanish National Forest Inventory)Authors contributions Conception: IA, RV and IC. Analysis: IA, JGAG, SC, EGF, SG, LH, MMJ, FM, NO, MPT, ADRG, ER,MSG, VS, ASM, HS. Drafting of the manuscript: all authors. Supervising and coordinating the work: IA and IC.Citation: Alberdi, I.; Vallejo, R.; Álvarez-González, J. G.; Condés, S.; González-Ferreiro, E.; Guerrero, S.; Hernández, L.; MartínezJauregui, M.; Montes, F.; Oliveira, N.; Pasalodos-Tato, M.; Robla, E.; Ruiz-González, A. D.; Sánchez-González, M.; Sandoval, V.; SanMiguel, A.; Sixto, H.; Cañellas, I. (2017). The multi-objective Spanish National Forest Inventory. Forest Systems, Volume 26, Issue 2,e04S. https://doi.org/10.5424/fs/2017262-10577Received: 06 Oct 2016. Accepted: 31 Jul 2017Copyright 2017 INIA. This is an open access article distributed under the terms of the Creative Commons Attribution (CC-by)Spain 3.0 License.Funding: European Union’s Horizon 2020 Research and Innovation Programme (DIABOLO Project, Grant 633464), MAGRAMA(Contract EG12-0073), INIA (RTA 2014-00011-C06-04) and GEPRIF (RTA 2014-00011-C06-04).Competing interests: The authors have declared that no competing interests exist.Correspondence: should be addressed to Iciar Alberdi: Alberdi.iciar@inia.es; alberdi.asensio.iciar@gmail.comIntroductionThere has been a shift in the aims of forest policy andforest management in Europe from wood productionto sustainable ecosystem management, which shouldconsider all the goods and services provided by theforest. To address these increasing, new demands forinformation, intensive monitoring of the status of forestsis required. Forest information systems are needed inorder to formulate and implement global forest policyand forest management and in turn, depending on theaims of the forest policy, forest inventory systems aremodified to meet the demands for information (Fig.1). The main international processes or requirementsdemanding forest information are the United NationsFramework Convention On Climate Change (UNFCC)and the Kyoto protocol, the Global Assessment of ForestResources (FRA), required by the United Nations Food
2Iciar Alberdi, Roberto Vallejo, Juan G. Álvarez-González, Sonia Condés et al.Figure 1. Diagram of the role of forest information systems in forest policy processes where each case study topic analyzedin this article was framed.and Agriculture Organization (FAO) and the Criteriaand Indicators for Sustainable Management, reportedon the State of European forests (SoEF) requestedby the pan-European process for the protection offorests in Europe (FOREST EUROPE) (Vidal et al.,2016). These processes require comprehensive forestinformation, covering aspects as varied as growingstock, carbon pools and non-wood forest productsrelated with the green economy as well as informationon forest biodiversity, forest risks and disturbances, orsocial indicators.Additionally, according to the European foreststrategy (EC, 2013), the Commission and the MemberStates should set up of the Forest Information Systemof Europe by collecting harmonised Europe-wideinformation on the multifunctional role of forestsand forest resources integrating several modules, e.g.on forests and natural disturbances, forest and thebioeconomy, forests and climate change and forest andecosystem services. Therefore, there is a continued needfor assessment and estimation of forest indicators at EUlevel to support the development and implementation ofa number of European environmental policies as wellas to identify appropriate forest management practices(Vidal et al., 2016). National forest inventories (NFIs)are the primary source of forest data for nationaland large-area assessments due to the high degree ofmonitoring and the diversity of variables recorded atnational level. The scope of NFIs has been broadenedto include new variables to meet these increasinginformation requirements (Tomppo et al., 2010).In Spain, the necessity for homogenous and objectiveforest statistics for decision making at national levelForest Systemsprovided the impetus for undertaking the First NationalForest Inventory (SNFI1) between 1965 and 1974. Dueto circumstances related to the national infrastructure,the Second Spanish National Forest Inventory (SNFI2)did not commence until 1986. Since the NFI2, therehas been a continuous inventory with permanent plotsin most of the Spanish regions operating over a cycleof approximately 10 years. From the second cycleonwards the plots are permanent, located at the nodesof a 1 1 km grid, thus enabling comparisons. Since theThird National Forest Inventory (SNFI3, 1997-2007),land cover classification and forest area estimation aredescribed prior to the NFI using the National ForestMap, (E 1:50,000; NFM50). The Fourth National ForestInventory (SNFI4) is currently ongoing and the area ofeach NFI4 forest strata is estimated using the NFM25(E 1:25,000), adding the tessera (i.e. basic unit, havinga specific land use with homogeneous forest structureand forest type) belonging to each stratum (Vallejo &Sandoval, 2013).The main aim of this paper was to present applications(case studies) of the SNFI as a multi-objectiveinventory capable of responding to the demand forforest information regarding forest biodiversity andconservation, bioeconomy, forest hazards and forestdisturbances (as highlighted in the European ForestStrategy) (Fig. 1). The specific objectives of the casestudies, chosen for their relevance as well as novelty,were as follows: i) to quantify and characterize deadwood volume by forest type; ii) to model the relationshipbetween age and diameter for riparian species; iii) tocharacterize and analyze the spread of invasive species;iv) to estimate the impact of livestock and wildlifeAugust 2017 Volume 26 Issue 2 e04S
3Multi-objective Spanish National Forest InventoryFigure 2. Relationship between age (yr) and diameter (cm) for the entire dataset. Populus albavalues are represented by red dots and its model by a red line, and Populus nigra values arerepresented by green dots and its model by a green line.browsing; v) to estimate cork production at nationallevel, and vi) to develop models to estimate the maincanopy fuel complex characteristics needed to assesscrown fire potential using airbone laser scanning (ALS)metrics as regressors.Material and methodsIn Spain, land cover classification and forest areaestimation are described prior to the SNFI using theNational Forest Map (Vallejo & Sandoval, 2013). TheSNFI covers all forest land in Spain. From the secondcycle onwards, the plots are permanent, enablinggrowth comparisons and stratification to be undertakenpost-sampling. Sample plots are established at theintersections of a 1 1 km UTM grid (Alberdi et al.,2010). Field plots consist of four concentric circularfixed areas with radii of 5, 10, 15 and 25 m (Alberdi etal., 2016).All the different datasets of the case studies wereextracted from SNFI2, SNFI3 and overall, from SNFI4,which started in 2008 and is currently ongoing, with fielddata collection and data processing work progressingsimultaneously.Dead wood volume quantificationData from 2,396 SNFI4 plots in Navarra provincelocated in Northern Spain were used in this study. In thisarea, the three bio-geographical regions, Mediterranean,Atlantic and Alpine, are well represented.Dead wood components data were recorded in theSNFI 15 m radius subplot. The dead wood componentsForest Systemsconsidered were as follows: dead standing and downedtrees; dead standing and downed saplings; downedcoarse wood pieces; stumps and accumulations. Treesas well as shrub species were recorded and the fivedecay classes proposed by Hunter (1990) and Guby &Dobbertin (1996) were considered. Downed trees andsaplings are recorded when the stump or thickest endis within the plot. Other dead wood components arerecorded when more than 50% of the piece is inside theplot (Alberdi et al., 2014). The methodology to estimatetree volume (either standing or downed) was describedin Crecente-Campo et al., (2015). The aim of this studywas to present the dead wood volume reference valuesin different forest ecosystems.Species growth modelsCores of the dominant trees of the three main speciesper plot are extracted in the SNFI4 plots (25 m radius).With this information, two nonlinear mixed agediameter models have been developed for the speciesPopulus alba L. and Populus nigra L. (Eq. [1]) in orderto estimate the tree age depending on its diameter.They incorporate a random effect for each individualtree. Cores were air dried, mounted and sanded andmeasured with a LINTAB measuring table (Rinntech,Heidelberg, Germany) with an accuracy of 0.01 mm.A total of 1,233 records of a total of 32 trees have beenused (Fig. 2) to fit the mixed models with the statisticalpackage nlme (nonlinear mixed-effects models) of the Rsoftware program (R Development Core Team, 2014).These trees were distributed in the Mediterraneanbiogeographical region in plots located in CentralNorthern Spain (La Rioja and Madrid regions).August 2017 Volume 26 Issue 2 e04s
4Iciar Alberdi, Roberto Vallejo, Juan G. Álvarez-González, Sonia Condés et al.Table 1. Invasive species list selected from the additional biodiversity inventory of Galicia SNFI4 (2008). The functionaltype (FnT), mean density (individuals/ha) of the plots in which they are present, total number of plots where they occurand the total percentage per province in the whole forest area studied (Galicia) are detailed by species.ProvincesSpeciesFnT[1]DensityLa CoruñaLugoOrensePontevedraGalicia (%)Total number of plotsAcacia dealbata LinkT4,226.0371870422.76Acacia longifolia (Andr.) Willd.T318.31---0.02Acacia mearnsii De Wild.T2,562.3---30.05Acacia melanoxylon R.Br.T1,456.0542331113.15Arundo donax L.H9,549.3--110.03Baccharis halimifolia L.H------Cortaderia selloana Schult.& Schult. f.H11,5652.631-10.08Phyllostachys spp.H-21Phytolacca americana L.H7,957.71-20.085-0.10Prunus laurocerasus L.T294.43712170.94Reynoutria japónica Houtt.H38,197.2-1-10.03Tradescantia fluminensis Vell.H----20.03Tritonia crocosmiflora (Lemoine)N.E.Br.H99,471.841--0.08Total (%)--6.013.727.5314.747.36[1]T: trees; H: herbaceous[1]where tij is the response variable (age expressed in years),j is the observations for every ring of the tree, i is the treelevel, dij is the predictive variable (diameter expressed incm), β is the fixed estimated parameter, bi is the mixedestimated parameter and εij is the error term. Ψ1 is theerror correlation structure and σ2 is the variance.The significance level considered was 0.05 and thenonlinear mixed models were fitted through maximumlikelihook. The Akaike s information criterion (AIC)was used to compare alternative nonlinear mixedmodels (Zhao et al., 2005; Wang et al., 2007) and selectthe better models for each species (data not shown). Thesuitability of the models was also evaluated through thesignificance of the parameters of the models, the analysisof the model residuals and to evaluate the goodness offit of the model we also used the root mean square error(RMSE) according to Willmott (1982).Invasiveness and invasilibilityThis case study was based on SNFI4 information forthe four provinces which comprise the region of Galicia,in the north-west Iberian Peninsula. This region presentsa dominant Atlantic climate, acidic soils and a complexForest Systemstopography with altitudes ranging from sea level up to2,124 m.To study this indicator, prior to the field work, a listof invasive species likely to be found in forested areasof the monitored province is drawn up for the additionalbiodiversity monitoring plots (Table 1; Sanz Elorza etal., 2004; Fagúndez & Barradas, 2007; Romero Buján,2007). The invasive tree, shrub and herbaceous speciespreviously listed were then recorded in 10 m, 5 m and1 m radius subplots respectively. In addition, theirpresence in the 25 m radius SNFI plot was registered.To analyze the characteristics of the invasive speciesin the study area, the alien species were classified byfunctional type and their mean density in the study areaassessed based on SNFI field plot data. To examine therate of spread of each species, we then assessed the degreeof invasion of the forested study area as a whole and byforest type, based on the presence/ absence informationfrom the SNFI records for Galicia (5,993 plots). Finally,the vulnerability to the invasion of different forest typeswas analyzed through the total number and proportionof plots invaded as well as by the number of the differentspecies hosted.Browsing impactData from 900 plots in the SNFI4 corresponding toBalearic Islands, located to the East of Spain, were usedAugust 2017 Volume 26 Issue 2 e04S
5Multi-objective Spanish National Forest InventoryFigure 3. Degree of browsing impact per NFI plot and the distribution of forest types in the Balearic Islands(from Spanish Forest Map 1:50000).in this study (Fig. 3). In the SNFI4, browsing impactdata are recorded in the 10 m radius subplot for trees,saplings and shrub species and in the 5 m radius subplotfor tree regeneration. For each species, crown cover wasestimated as a proxy of browse availability. Averagebrowsing degree, indicating browse utilization, wasalso recorded by species according to the classificationby Fernández-Olalla et al. (2006), based on Etiènne etal. (1995) and Aldezábal & Garín (2000). The 6-rankbrowsing degree classification was as follows: (1) nobrowsing evidence; (2) slight browsing evidence: only afew twigs browsed; (3) low browsing intensity: plentyof twigs browsed but clearly under 50% of potentialbrowsing biomass; (4) intense, although sustainable,browsing: plenty of twigs browsed, around 50% ofpotential browsing biomass; (5) high browsing intensity:consumption over 50% of potential browsing biomassand clear shaping of the original plant form; and (6)maximum browsing intensity: no or almost no greenparts remain.To study browsing preferences in the study area, theutilization of each woody species was compared with itsavailability through the forage ratio index (Krebs, 1999;Fernández-Olalla et al., 2006):[2]where wij: forage ratio or preference (selection) indexfor species i in plot j; oij: browsing utilization of speciesi in plot j; pij: browsing availability of species i in plotj, and n: number of woody species present in plot j.Forest SystemsCork productionTo estimate cork production at national level, plotsfrom the SNFI2 and SNFI3 were used. Since corkrelated variables inventoried in SNFI changed fromthe SNFI2 to the SNFI3, two different approaches wereperformed to obtain cork production. In the SNFI2 thevariables recorded were debarking height and corkthickness. Debarking height was measured in all thetrees in the plot and cork thickness was inventoried on1 to 6 trees per plot. The cork thickness of the rest ofthe trees in the plot was assumed to have the same corkthickness which was equal to the arithmetic mean of thecork thickness of the trees measured. With these datathe volume of cork for an individual tree (Vcork) wasestimated through the following equation:[3]where dh is the debarking height (cm), ct is the corkthickness (cm) and pbh is the stem perimeter at breastheight (cm). By multiplying Vcork (cm3) by the averagecork density, the weight of cork for every tree wasobtained. The cork of each plot was calculated as thesum of the cork weight of all the trees in the plot. Thenumber of plots considered was 2109, with a total of10,543 cork oaks.In the case of SNFI3, only the debarking height wasmeasured. Since no direct estimation of the cork volumewas possible with these data, a different approachwas adopted based on the application of a diameterincrement model to data from SNFI2 plots. Hence, toAugust 2017 Volume 26 Issue 2 e04s
6Iciar Alberdi, Roberto Vallejo, Juan G. Álvarez-González, Sonia Condés et al.estimate the cork thickness in the trees from the SNFI3,the variables used were the diameter under cork andthe cork thickness of all the cork trees from the SNFI2.Using this information, the annual diameter increment ofthese trees was estimated through the model developedby Sánchez-González et al. (2006). All the variablesincluded in this model were directly given for SNFI plotsexcept for site index, which is estimated by the dominantheight model developed by Sánchez-González et al.(2007). With this increment data, the theoretical diameterunder cork at the SNFI3 was estimated by adding theannual diameter increment in SNFI2 multiplied by thenumber of years elapsed between SNFI2 and SNFI3 to therecorded diameter at breast height under cork in SNFI2.The difference between the estimated diameter at breastheight under cork in SNFI3 and the diameter over corkgiven for the plots in the SNFI3, is the cork thickness (ct)for the trees in the SNFI3 plots. Using the estimated corkthickness data and the dh reported in the SNFI3 plots, theweight of cork per plot was estimated according to Eq.[3]. In order to allow comparisons between SNFIs, thesame plots were analyzed in each. In accordance with thiscriterion 2109 plots (10,543 cork oaks).Canopy fuel modelingIn this study, models to estimate the main canopy fuelcomplex characteristics in maritime pine (Pinus pinasterAit.) and radiata pine (Pinus radiata D. Don) plantationsin Galicia (NW Spain) were obtained by combining 554sample plots from the SNFI4 with information based onALS over the same plots. These variables, related to theavailable fuel in the aerial layer, were canopy base height(CBH), canopy fuel load (CFL) and canopy bulk density(CBD). CBH is considered the most important variablein estimating the potential of surface fires to transitionto crown fires, and CBD in estimating the potential foractive crown fire and crown fire intensity. CFL is usedto estimate the amount of canopy material consumedin a crown fire and it is useful not only in fire behaviorsimulations but also in fire effects simulations (Keane,2015).ALS information was provided by the SpanishNational Aerial Photography Program (Plan Nacional deOrtofotografía Aérea, PNOA). In total, 39 metrics relatedto canopy cover and height distribution were extractedfrom ALS pulses and used as regressors for statisticalanalyses (see Tables 2 and 3). For further details of theprocedure used to obtain the ALS metrics, see the stepsoutlined in González- Ferreiro et al. (2013).In this study, needles and fine twigs (up to 5 mm atthe thick end) were considered as available fuel (i.e.the fuel that is consumed within the flaming front ofa crown fire), and the “load over depth method” firstproposed by Van Wagner (1977) was used to calculateCBH and CBD. According to this approach, CFL (thedry mass of available canopy fuel per unit groundarea) was assumed to be homogeneously distributedTable 2. Potential airbone laser scanning (ALS) explanatory variables related to heightdistributionALS metrics related withheight distribution (m)Forest dianMedianhSDStandard deviationhCVCoefficient of variationhskwSkewnesshkurtKurtosishIDInterquartile distancehAADAverage absolute deviationhMADmedianMedian of the absolute deviations from the overall medianhMADmodeMode of the absolute deviations from the overall modehL1, hL2, , hL4L-momentshLskwL-moment of skewnesshLkurL-moment of kurtosish05, h10, h20, , h90, h95, h99Percentilesh25 and h75First and third quartilesAugust 2017 Volume 26 Issue 2 e04S
7Multi-objective Spanish National Forest InventoryTable 3. Potential airbone laser scanning (ALS) explanatory variables related to canopyclosureALS metricsDescriptionPFRAhmeanRatio of the number of the first laser returns above hmean to the number of firstlaser returns for each plotPFRAhmodeRatio of the number of the first laser returns above hmode to the number of firstreturns for each plotPARAhmeanRatio of the number of the all laser returns above hmean to the number of all laserreturns for each plotPARAhmodeRatio of the number of the all laser returns above hmode to the number of all laserreturns for each plotPFRA4Ratio of the number of the first laser returns above 4 m height to the total numberof first laser returns for each plotPARA4Ratio of the number of the all laser returns above 4 m height to the total number offirst laser returns for each plotCRRCanopy relief ratio: (hmean - hmin)/( hmax - hmin)Table 4. Descriptive statistics for the main tree and stand variables corresponding to the sample plotused in this study. SD, standard deviation; d, tree diameter; h, total tree height; cl, crown length; N, standdensity; dg, quadratic mean diameter; G, basal area; and H, dominant height.Maritime 4522.5516.98SD11.286.203.86532.869.8613.405.70(n 436)Radiata pine(n 118)throughout the aerial layer of the stand; CBH wasthe vertical distance between the ground surface andthe height of the mean crown base of the stand; andthen CBD (the amount of burnable canopy fuel bycanopy volume) was estimated by dividing the CFLby the canopy length (CL), which was estimated asthe difference between the mean height (h̅ ) and CBH.These definitions of CBH and CBD are compatiblewith the canopy fuel stratum characteristics used in thecrown fire initiation and propagation model developedby Van Wagner (1977) and Cruz & Alexander (2010,2012), which has been implemented in most wildlandForest Systemsfire simulation systems. The compatible systems of treebiomass equations developed for maritime pine andradiata pine in Galicia (reported in Diéguez-Aranda etal., 2009) were used to estimate the dry weight of thefine aboveground tree fractions. The systems requiremeasurements of tree diameter and height as inputvariables. The mean, maximum and minimum valuesand the standard deviation (SD) for the main tree andstand variables are shown in Table 4.Potential and linear models to estimate thecanopy variables for each species from differentcombinations of ALS metrics were fitted and theAugust 2017 Volume 26 Issue 2 e04s
8Iciar Alberdi, Roberto Vallejo, Juan G. Álvarez-González, Sonia Condés et al.Table 5. Dead wood (DW) volume in the different biogeographical regions and forest types (with a number of plots greater than 30) of Navarra. SD: standard deviationVolume (m3/ha)DWSDMax DWMin DW% DWGrowing 812.75143.740.005.37103.691140Mixed mautochtonousbroadleaved and coniferous forest of the Alpinebiogeographical region20.5639.01231.110.00258.067.3842Flood plain forest19.3122.38113.970.00108.7715.0781Mixed forest in the Atlanticbiogeographic region14.0131.65312.810.00139.999.10123Scots pine forest10.9516.55143.740.00191.455.41334Beech forest10.2917.04168.990.00229.974.28723Oak forest of Quercusrobur and/or Quercuspetraea10.0713.2056.070.00139.226.7581Mixed autochtonousbroadleaves and coniferousforest of the Mediterraneanbiogeographical region7.9116.64134.830.0084.318.5892Austrian pine forest7.1817.01117.720.00174.423.9596Oak forest of Quercushumilis3.516.8236.620.00121.532.8144Oak forest of Quercusfaginea3.257.0846.660.0076.034.10119Mixed broadleaves forestin the Mediterranean biogeographical region3.104.7924.280.0072.624.0967Productive stands of poplartree and Platanus hispanica2.348.2547.950.0098.652.3233Aleppo pine forest2.285.4728.640.0037.775.69120Oak forest of Quercus ilex1.322.7221.850.0067.101.93175Juniper species raneanForest typebest models were selected by comparing the valuesof the RMSE and the model efficiency (ME)(Willmott, 1982).ResultsDead woodThe average dead wood volume (DWV) for Navarraprovince was 8.83 m3/ha, presenting a high SD dueto its high amount of variation (18.11 m3/ha). Thisvolume represents 6.34% of all the wood volume offorests.Forest SystemsStanding and downed trees present in Navarra accountfor an equal percentage of DWV (35% each), while thepercentage of standing saplings is slightly higher thandowned saplings (3% and 2% respectively). Furthermore,the large percentage of branches (18%) in this regionshould also be mentioned. The dead wood was highlydecompose, as classes 4 and 5 account for 60% of the totalDWV.The DWV in the three ecoregions of Navarra differed;from 14.05 m3/ha (SD 24.10 m3/ha) in the Alpine regionto 10.55 m3/ha (SD 20.18 m3/ha) in the Atlantic regionand 5.88 m3/ha (SD 12.97 m3/ha) in the Mediterraneanregion (Table 5). However, the average percentage of deadAugust 2017 Volume 26 Issue 2 e04S
9Multi-objective Spanish National Forest Inventorywood for the three ecoregions represents between 5.4 and5.6% of the total wood volume.Mixed forest types generally presented higher averageDWV. This is the case of “Mixed autochthonous broadleafand coniferous forests in the Alpine biogeographical region” (20.56 m3/ha), “Flood plain forest” (19.31 m3/ha)and “Mixed forests in the Atlantic biogeographic region”(14.01 m3/ha). In contrast, in forests dominated by Mediterranean species, such us “Juniper forests (Juniperusspp.)” (0.34 m3/ha), “Oak forests of Quercus ilex” (1.32 m3/ha) or in managed forests, the figures are lower (Table 5).Species growth modelsFor the P. alba the best model (Table 6) among thetested ones had an AIC 2,767.09, being the numberof observations 554. For the P. nigra the best modelpresented (Table 6) had an AIC 3,290.31 and thenumber of observations 679. The residuals analysisshows a RMSE of 2.66 years for the P. alba model and2.50 years for the P. nigra model. The simplicity of bothmodels and the fact that they showed a good distributionof residuals (Fig. 4) were considered for their selection.Invasiveness and invasibilityEven though in terms of composition there were asimilar number of invasive herbaceous (7) and invasivetree (5) species recorded, our findings suggests that thereis a greater potential for invasion by invasive tree species(mainly Acacia spp.) in forest ecosystems of NW Spain(Table 1). A. melanoxylon and A. dealbata displayed thegreatest invasiveness, being present in 3.2% and 2.8%respectively of the total forested area of the study region andexhibiting a high number of trees/ha. Among herbaceousspecies, Cortaderia selloana, Phyllostachys spp. andPhytolacca americana were the most widespread in NWforest ecosystems with a high number of individuals/ha. This trend according to functional type was patent ifwe examine the total percentage of forest area
Third National Forest Inventory (SNFI3, 1997-2007), land cover classification and forest area estimation are described prior to the NFI using the National Forest Inventory (SNFI4) is currently ongoing and the area of each NFI4 forest strata is estimated using the NFM25 i.e. basic unit, having a specific land use with homogeneous forest structure
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Chính Văn.- Còn đức Thế tôn thì tuệ giác cực kỳ trong sạch 8: hiện hành bất nhị 9, đạt đến vô tướng 10, đứng vào chỗ đứng của các đức Thế tôn 11, thể hiện tính bình đẳng của các Ngài, đến chỗ không còn chướng ngại 12, giáo pháp không thể khuynh đảo, tâm thức không bị cản trở, cái được
Le genou de Lucy. Odile Jacob. 1999. Coppens Y. Pré-textes. L’homme préhistorique en morceaux. Eds Odile Jacob. 2011. Costentin J., Delaveau P. Café, thé, chocolat, les bons effets sur le cerveau et pour le corps. Editions Odile Jacob. 2010. Crawford M., Marsh D. The driving force : food in human evolution and the future.
Le genou de Lucy. Odile Jacob. 1999. Coppens Y. Pré-textes. L’homme préhistorique en morceaux. Eds Odile Jacob. 2011. Costentin J., Delaveau P. Café, thé, chocolat, les bons effets sur le cerveau et pour le corps. Editions Odile Jacob. 2010. 3 Crawford M., Marsh D. The driving force : food in human evolution and the future.
