linking land-use scenarios, remote sensing and monitoring to project impact of management decisions

Linking Land-Use Scenarios, Remote Sensing and Monitoring to Project Impactof Management Decisions

Nina Farwig1,6, Tobias Lung2,3, Gertrud Schaab2, and Katrin B€ohning-Gaese4,5

1 Department of Ecology – Conservation Ecology, Philipps-Universit€at Marburg, Karl-von-Frisch-Str. 8, 35032 Marburg, Germany

2 Faculty of Information Management and Media, Karlsruhe University of Applied Sciences, Moltkestr. 30, 76133 Karlsruhe, Germany

3 European Environment Agency, Integrated Environmental Assessment (IEA) programme, Kongens Nytorv 6, 1050 Copenhagen K, Denmark

4 Biodiversity and Climate Research Centre (BiK-F), Senckenberg Gesellschaft f€ur Naturforschung, Senckenberganlage 25, 60325 Frankfurt

am Main, Germany

5 Department of Biological Sciences, Goethe-Universit€at Frankfurt, Max-von-Laue-Straße 9, 60438 Frankfurt am Main, Germany

ABSTRACT

Large-scale modifications of natural ecosystems lead to mosaics of natural, semi-natural and intensively used habitats. To improve com-munication in conservation planning, managers and other stakeholders need spatially explicit projections at the landscape scale of futurebiodiversity under different land-use scenarios. For that purpose, we visualized the potential effect of five forest management scenarioson the avifauna of Kakamega Forest, western Kenya using different measures of bird diversity and GIS data. Future projections of birddiversity combined: (1) remotely sensed data on the spatial distribution of different forest management types; (2) field-based data on thebiodiversity of birds in the different management types; and (3) forest management scenarios that took into account possible views ofvarious stakeholder groups. Management scenarios based on the species richness of forest specialists were very informative, because theyreflected differences in the proportions of near-natural forest types among the five scenarios. Projections based on community composi-tion were even more meaningful, as they mirrored not only the proportions of near-natural forest types, but also their perimeter to arearatios. This highlights that it is important to differentiate effects of the total area of available habitat and the degree of habitat fragmen-tation, both for species richness and community composition. Furthermore, our study shows that an approach that combines land-usescenarios, remote sensing and field data on biodiversity can be used to visualize future biodiversity. As such, visualizations of alternativescenarios are valuable for successful communication about conservation planning considering different groups of stakeholders inspecies-rich tropical forests.

Key words: bird diversity; GIS; Kakamega Forest; management planning; visual assessment.

TROPICAL FORESTS SUPPORT MORE THAN HALF OF THE TERRESTRIAL

SPECIES AND ARE THUS CRITICAL FOR BIODIVERSITY CONSERVATION

(Laurance 1999, Dirzo & Raven 2003). Increasing human landuse leads to conversion of natural forests into cultivated landcausing biodiversity loss (Myers et al. 2000, Cardinale et al. 2012,Laurence et al. 2012). In other cases, secondary re-growth ortimber plantations replace natural forest (FAO 2009). Thesemodified forest management types (FMT) can be crucial for con-servation as they often retain many forest species (Barlow et al.2007, Farwig et al. 2009a,b, Berry et al. 2010, Edwards et al.2011, Schleuning et al. 2011, but see Phalan et al. 2011, Hulmeet al. 2013). However, community structure and composition canshow marked differences among FMT in modern landscapes(Barlow et al. 2007, Farwig et al. 2009b, Edwards et al. 2011).

Landscape-scale conservation planning is urgently needed(Angelstam et al. 2004), given the increasing loss of tropical for-ests (Wright 2005, FRA 2010) and considerable impact of thesurrounding landscape on forest biodiversity (Peh et al. 2006). In

most tropical countries, various types of protection and manage-ment are currently realized. These range from areas with the pri-mary aim of biodiversity conservation (IUCN categories I-II),areas managed mainly for the sustainable use of resources (IUCNcategories III-VI) to unprotected areas in which a range ofresource uses and intensities are carried out (IUCN 1994). Con-servation prioritization has so far mostly focussed on speciesrichness of an area (Wiersma & Urban 2005). In addition, com-munity level modeling has been integrated into conservation plan-ning to maximize the representation of complementarity and thusbiodiversity during area selection processes (Marsh et al. 2010).To date, only a few studies have applied community level model-ing for conservation planning on the ground (Fairbanks et al.2001, Wiersma & Urban 2005). Thus, visualizing the impact of arange of management decisions on future biodiversity seemspromising for landscape-scale conservation planning (Ewers et al.2010, Marsh et al. 2010).

One way to project and to visualize the consequences ofmanagement decisions is to combine scenarios portraying desir-able futures that are plausibly achievable (Nassauer & Corry2004) with remote sensing and field-based biodiversity data. The

Received 1 July 2013; revision accepted 24 January 2014.6Corresponding author; e-mail: [email protected]

ª 2014 The Association for Tropical Biology and Conservation 357

BIOTROPICA 46(3): 357–366 2014 10.1111/btp.12105

integration of remotely sensed land-cover data with biodiversitydata has been well established (e.g., Kerr & Ostrovsky 2003,Turner et al. 2003, Ewers et al. 2010). Remotely sensed data pro-vide coverage of large areas that cannot otherwise be easily sur-veyed. In contrast, detailed and fine-grained field surveys ofdifferent land-cover types are needed to relate species richness andcommunity composition to land-management regimes (Leyequienet al. 2007, Acevedo & Restrepo 2008, Marsh et al. 2010).

Spatially explicit scenarios are often used to provide visual-izations of alternative land-cover futures and to assess the conse-quences of management decisions associated with them (Petersonet al. 2003, Nelson et al. 2009). Management scenarios are oftendeveloped within participatory forest management initiatives(Ostrom & Nagendra 2006, Blomley et al. 2008), during whichthe needs and interests of different stakeholders are collated(Peterson et al. 2003, Nassauer & Corry 2004, Nelson et al.2009). However, scenarios have been underutilized to exploreecological consequences of management decisions in conservationplanning (Bohensky et al. 2006, Coreau et al. 2009). To date,scenarios have been applied to study the potential impact ofclimate or land-use change on biodiversity on a global or regionalscale (e.g., Sala et al. 2000, Bomhard et al. 2005). However,most land-use decisions are made at the local or landscape scale(Theobald et al. 2000). Several studies used scenarios to explorethe potential consequences of land-use decisions on ecosystemservices and human well-being (Bohensky et al. 2006, Nelsonet al. 2009). In tropical countries, the consequences for biodiver-sity and conservation are rarely assessed beyond, for example,measuring the real extent of natural forest (Kassa et al. 2009).

We combine the use of forest management scenarios (FMS),remote sensing and field-based data on bird diversity to visualizealternative options for conservation planning and to provide anexample of how the dialog about alternative management optionsamong various stakeholders could be facilitated. For this purpose,we projected the potential consequences of forest managementdecisions on the spatial distribution of avian biodiversity in a spe-cies-rich tropical forest, Kakamega Forest (henceforth Kakamega)in Kenya. We focus on bird diversity, as birds respond quickly toforest management and are cost-effective to survey (Barlow et al.2007, Gardner et al. 2008). In Kakamega, birds respond to differ-ent FMT much quicker than trees or recruiting seedlings (Farwiget al. 2009a,b). As such, in this paper, we addressed the followingquestions: (1) what are the consequences of different stakeholder-informed forest management scenarios for the avifauna of Kaka-mega? (2) Which bird diversity measure (i.e., species richness ofdifferent forest dependency groups, community composition) ismost informative for visualizing management effects? And (3) towhat extent do maps of projected biodiversity patterns allowstakeholders to assess the effects of management?

METHODS

STUDY AREA.—The study area is located in western Kenya(00°080–00°220N, 34°460–34°570E) about 50 km north-east ofLake Victoria. Kakamega is Kenya’s only remaining mid-altitude

tropical rain forest (1460–1765 m asl) and is considered to bethe easternmost remnant of the Congo-Guinean rain forest belt(Kokwaro 1988). Annual precipitation in Kakamega is around2000 mm and average annual temperature is 18.7°C (Farwig et al.2006). The forest’s species composition indicates a transitionalposition between the lowland Congolian rain forests and the Afr-omontane forests east of the Rift valley (White 1983, Kokwaro1988). Kakamega is an Important Bird Area with 488 birdspecies (Mitchell et al. 2009) including two globally threatened(Chapin’s Flycatcher Muscicapa lendu and Turner’s EremomelaEremomela turneri [BirdLife International 2006,]) and 15 regionallythreatened avian species (Bennun & Njoroge 1999).

Kakamega is surrounded by agricultural land and is situatedin one of the most densely populated parts of Kenya with ca 720people/km² in 2009 (Mitchell et al. 2009). In 1933, the forestcovered 23,777 ha; by 2001, the protected area had been reducedto 11,108 ha (Mitchell et al. 2006). Currently, Kakamega is recov-ering, which can be linked to the eviction of people from withinthe protected forest area in 1986. The largest losses in forestcover were experienced before that time, mainly due to commer-cial timber exploitation (Mitchell 2004, 2011, Schaab et al. 2010).Today, Kakamega Forest consists of a mosaic of different forestmanagement types (FMT), i.e., near-natural forest, mixed-indigenous plantations, indigenous monocultures, exotic monocul-tures, secondary forest as well as some open areas (cp. with mapon FMT for 2003 in Fig. 1; Farwig et al. 2008, Lung & Schaab2010). Near-natural forest stands show low levels of selective log-ging and high tree diversity (Bleher et al. 2006, Farwig et al.2009a). Mixed-indigenous plantations have been planted since the1940s (Mitchell et al. 2009) showing high levels of selective log-ging and relatively high tree diversity (Bleher et al. 2006, Farwiget al. 2009a). Most indigenous and exotic monocultures wereplanted in the 1960s, while much of today’s secondary forests(Fig. 1) developed from bushland of the 1970s and 1980s (Lung& Schaab 2010). Due to increasing population, current threats toKakamega comprise selective logging, collection of firewood andmedicinal plants, debarking of trees, grazing and charcoal burning(Bleher et al. 2006, Schuldenzucker 2010, Mitchell 2011). Forestdisturbance depends on the forest management authority (KenyaWildlife Service [KWS], Kenya Forest Service [KFS]; Bleher et al.2006) and the distance of a site to the forest-farmland edge(Schuldenzucker 2010). Other threats often reported for tropicalforests such as human encroachment, colonization, fires orspread of invasive species are currently no threat to Kakamega(Mitchell 2004, Berens et al. 2008). The Kenyan government aimsat a 10 percent forest cover, planned originally for 2010 (GoK2008), which might be most easily achieved by restoring gazetted,but heavily degraded forest areas such as in Kakamega.

BIRD SURVEY DATA.—Monthly bird surveys were conductedbetween March 2005 and March 2006 in three 1-ha plots perFMT. The minimum distance between plots was 500 m and allplots were situated at least ca 100 m away from the edge of therespective FMT to control for edge effects. Nine point-count sta-tions per plot were used in the early morning to survey all birds

358 Farwig, Lung, Schaab, and B€ohning-Gaese

seen and heard for a period of 10 min within a radius of 20 m(Farwig et al. 2008, 2009b).

In all previous studies (Farwig et al. 2008, 2009b) and thispresent study, we took into account overall species richness. We dif-ferentiated species richness among three forest dependency groups,and we quantified bird community composition (Farwig et al. 2008,2009b). For the analysis, we pooled all bird species and individualsfor each monthly survey over the nine locations per plot. We thencalculated the mean number of species and the mean number ofindividuals (log-transformed) over the 13 surveys per plot (Farwiget al. 2008, 2009b). In addition, bird species were classified accord-ing to their forest dependency (Bennun et al. 1996): forest special-ists are bird species that live and breed in the interior of closed-canopy or little disturbed forests. Forest generalist species canoccur and breed in undisturbed and disturbed forest. Forest visitorspecies are recorded in forests but are more common in non-foresthabitats. We then quantified the number of species for the forestdependency groups for each FMT.

We compared the similarity in composition of the bird com-munities among the five FMT using principal component analysis

(PCA) based on the correlation matrix of 83 species (rare speciesexcluded; Farwig et al. 2009b). A PCA extracts the dominantpattern of similarity of the matrix in terms of a complementaryset of new orthogonal principal components (PC). As such, birdspecies with the highest positive and negative loadings on thePC-axes represent the species with the greatest differences inoccupancy among the FMT. For analyzing bird community com-position, we used the values on the first axis (PC1) as a measureof the naturalness of the forest bird community. Mean PC1-values also included negative values (range of �13.62 for second-ary forest for 2003 to 7.16 for near-natural forest for themaximal scenario). For easier data processing we added 13.62 toeach PC1-value to have only positive values.

GEO-SPATIAL PREDICTORS.—We assessed a broader spectrum ofgeo-spatial predictors that might also affect the spatial distribu-tion of bird diversity based on literature (e.g., Graham & Blake2001). Of those predictors, we selected patch size, patch shape,plot-patch-closest-boundary and distance to the closest forest-farmland edge as well as testing for effects of FMT as in Farwig

FIGURE 1. Spatial distribution of the five forest management types, corresponding bird community composition and uncertainties in projection for the reference

forest state in 2003 as well as the five different forest management scenarios within the officially gazetted area of Kakamega Forest, western Kenya. For bird

community composition, visualization uses a color ramp from light yellowish hues for the lowest PC1-values (most disturbed bird community), to dark green hues

for the highest PC1-values (near-natural bird community). Color version available in online Supporting Information.

Linking Conservation and Land-Use Planning 359

et al. (2008, 2009b). We predicted that larger patches, more con-nected patches, and more natural FMT would support higher spe-cies richness and more natural community composition of birdsin Kakamega. All calculations were performed in ArcGIS 9.3.Patch size quantifies the continuous area of each FMT, whilepatch shape reflects the perimeter to area ratio of the respectiveFMT. Plot-patch-closest-boundary depicts the shortest distancefrom the plot center to the edge of the respective patch. Distanceto the closest forest-farmland edge is quantified from the plotcenter to the respective edge. We tested for the effects of FMTand the other chosen geo-spatial predictors on bird communitiesin linear models with stepwise deletion of non-significant terms(P > 0.05). All statistical analyses were carried out with R 2.14.0(R Development Core Team 2011).

QUANTIFICATION OF THE FOREST STATE OF 2003.—We derived thespatial distribution of the different FMT for 2003 as a referencestate using Landsat satellite imagery (Lung et al. 2012). This wasdone due to 2003 being the closest year to a published land-coverclassification available for Kakamega, which related to the birdsurveys of 2005–2006. A supervised multispectral classificationapplying the maximum likelihood classifier enabled us to distin-guish four forest and two bushland classes from the satellite imag-ery at a spatial resolution of 30 9 30 meters (for details see Lung& Schaab 2006, 2010). Two of the forest types, mixed-indigenousplantations and indigenous monocultures, could not be distin-guished as separate classes by supervised multispectral classifica-tion and were manually delineated by visually interpreting theLandsat images with recent QuickBird imagery originating from2005, a 1:10,000 forestry map of the former Forest Departmentand ground truth information as ancillary information (Lung et al.2012).

FUTURE MANAGEMENT SCENARIOS.—The responsible governmentalauthorities have recently developed a forest management plan forKakamega covering 2012 to 2022 (KWS & KFS 2012). Based onin-depth knowledge and stakeholder consultation, a set of norma-tive forest use scenarios were developed. These reflect possibleviews of various stakeholder groups, i.e., the local community, themanaging authorities and scientists. The stakeholder groups havedifferent interests such as firewood and medicinal plant collec-tions, charcoal burning, or timber extraction, eco-tourism andforest conservation. Hence, the forest management scenarios(FMS) aim to reflect the different needs and interests of allstakeholder groups.

Based on the land-cover data of 2003 (Lung & Schaab2010), five FMS were developed for the officially gazetted forestarea of 1933, including two extreme scenarios, the minimal andmaximal scenario (Fig. 1, top row). (1) The minimal scenario hasa strict focus on forest utilization. In this scenario, it wasassumed that the entire forest will be clear-felled and subse-quently replanted with exotic monocultures, with the exceptionsof Shikusa Prison land (see blank area in westwards stretchingforest arm, 412 ha, used for agriculture) and historic glades thathave never been covered with forest (Mitchell 2004). (2) The

negative scenario also focuses on timber production, but to someextent also on conservation. In this scenario, it was assumed thatthe current nature reserves and the national reserve will bestrictly protected (therefore mapped with near-natural forest, seeFig. 1 negative scenario) and that the rest of the forest will befelled and replanted with exotic monocultures. These reserveareas are spatially isolated patches of a near-natural forest inter-spersed by some historical glades. All historic glades and ShikusaPrison land will remain open. (3) In the realistic scenario the aimwas a balance between forest utilization and conservation. It wasassumed that areas that were bushland in 2003 will turn to sec-ondary forest within the next 15 yr and a secondary forest willturn to a near-natural forest within the next 40 yr (Lung et al.2012). All historic glades and Shikusa Prison land will remainopen and forest plantations will stay unchanged. The remainingopen areas of the officially gazetted forest area will be replantedwith either of three plantation types (mixed-indigenous planta-tions, indigenous monoculture, exotic monoculture) in equal pro-portion. The spatial allocation of each plantation type wasdecided according to the following rules: (i) open areas within thenature reserves, the national reserve, and of the Yala-Ikuywa cor-ridor, which is an important area for conservation (Lung &Schaab 2006) will be replanted as mixed-indigenous plantations,the plantation type most valuable for conservation (Farwig et al.2009a,b). (ii) Areas easy to access will be replanted by fast grow-ing exotic monocultures for timber production. Areas furtheraway from roads or from the forest boundary will be planted asmixed-indigenous plantations. (iii) Other areas will be planted asindigenous monocultures. (4) The positive scenario has anemphasis on conservation, but also allows for forest utilization.In this scenario, a strict protection of the nature reserves and thenational reserve was assumed for decades. This resulted in aclosed cover of a near-natural forest. In addition, the presentmonocultures, open areas and Shikusa Prison land were assumedas replanted with mixed-indigenous plantations. Areas of bush-land and secondary forest in 2003 will turn to a near-natural for-est within the next 40 to 60 yr. (5) In the maximal scenario itwas assumed that all of the present forest plantations will beremoved and that Kakamega will subsequently be strictlyprotected for decades, a scenario with a strict focus on con-servation. Unlike the positive scenario (4), only natural successionis assumed to operate and projected to result in a closed cover ofa near-natural forest for the entire area within the next 60 to80 yr.

SPATIAL EXTRAPOLATION OF BIRD DIVERSITY.—We linked the birdsurvey data to future land-management scenarios and the 2003distribution of FMT by spatial extrapolation. We used the linearmodel functions for species richness (all birds as well as differen-tiated by forest dependency group) and community compositionto predict their spatial distribution in each of the scenarios andthe reference forest state (see Results). These measures of birddiversity were calculated on the plot basis referring to an areaof 11,310 m² (pr2 for 9 point counts with a radius of 20 m,henceforth unit area).


As FMT was confirmed to be the single, best predictor ofbird species richness (see Results), the spatial extrapolation of birdrichness simply reflects the re-coding of the different FMT foreach unit area (converted to the 30 9 30 m pixel size) to therespective mean species richness values for each of these types.For the reference forest state and the scenarios, all land-coverclasses besides the FMT (e.g., ‘Agricultural land’, ‘Grassland’) wererecoded to ‘No data’.

In addition, we summarized the information inherent in thespatial distribution of bird diversity by calculating means and ameasure of uncertainty over the whole forest. For each FMS andthe reference forest state, we calculated a mean species richnessby averaging the species richness of each unit area over the wholeforest, for total species richness as well as for forest specialists,generalists and visitors. Similarly, we calculated a mean ‘birdcommunity composition’ for each scenario by averaging thePC1-values for each unit area over the complete forest area.

Uncertainty in projection was calculated as the 95% confi-dence intervals of the linear model functions for the differentmeasures of bird diversity. Here again, we calculated an overalluncertainty for the modeling results of each scenario and the ref-erence forest state by averaging the confidence intervals for eachunit area over the complete forest area. It should be noted thatthis approach is not a statistical test, but a measure to summarizethe information over the whole forest.

RESULTS

PREDICTORS FOR THE SPATIAL BIRD DISTRIBUTION.—As the previousfindings on bird diversity of Kakamega are crucial to the under-standing of the following analyses, we repeat the most importantresults. In total, we recorded 115 bird species comprising 41forest specialists, 41 forest generalists and 33 forest visitors(Farwig et al. 2009b).

We tested the importance of forest management type (FMT)and the additional geo-spatial predictors for the spatial distribu-tion of bird species richness. None of the new geo-spatial predic-tors contributed significantly to the models (Table S1). Mean birdspecies richness differed strongly among the five FMT, whichexplained 76 percent of the variation in species richness for allbird species, 94 percent for forest specialists, 82 percent for for-est generalists and 88 percent for forest visitors (Table S1).

In the previous study, bird communities of the five FMTwere clearly separated along the first axis (PC1) of the PCA.Near-natural forest bird communities were placed at the one endof PC1, followed by communities in mixed-indigenous planta-tions, indigenous monocultures and exotic monocultures, andfinally, in secondary forest plots at the opposite end of PC1. Birdcommunities of near-natural forest and mixed-indigenous planta-tions were characterized by small forest specialists such as Ugan-dan Woodland Warbler Phylloscopus budongoensis or Olive-greenCamaroptera Camaroptera chloronota toroensis. Exotic monoculturesand secondary forests comprised particularly forest visitors suchas Common Wattle-eye Platysteira cyanea nyansae or Grey-backedCamaroptera Camaroptera brachyura (Farwig et al. 2008).

The best model for predicting the bird community composi-tion comprised FMT and patch shape (Table S1). Bird communitieswere not only significantly separated by FMT (96% of the varia-tion) but also by patch shape (2% of the variation). The resultingspatial patterns of bird composition thus change according to thevariability of FMT and patch shape. Patches with a circular shape(small perimeter to area ratio) showed high PC1-values while areaswith an irregular shape (high perimeter to area ratio) had lowPC1-values. The projected value of a bird community on PC1thus represents a gradient with high values for a near-naturalforest bird community in a circular patch and low values for adisturbed forest bird community in an irregularly shaped patch(Fig. 1, second row of maps).

FOREST MANAGEMENT SCENARIOS.—As expected, scenarios in whichKakamega contained extensive exotic monocultures (especially theminimal scenario, Fig. 2) showed a decrease in the mean number ofbird species per unit area compared to 2003, with a 40 percentdecrease in forest specialists, but a 33 percent increase in forest visi-tors (Table 1; Fig. 3A). The more compact shape of the forest inthe minimal scenario resulted in no change in the total forest meanvalue for bird composition from that of 2003 (Table 1), though theminimal scenario only contained exotic monocultures (Fig. 3B).The negative scenario would lead to a 28 percent decrease in forestspecialists, 20 percent increase in forest visitors and 10 percentincrease in the total forest mean value for bird composition fromthat of 2003 (Table 1). However, the visualization of the values perunit area demonstrated a clear change toward disturbed bird com-munities for those areas that turned from near-natural forest in2003 to exotic monocultures (Fig. 1, second row of maps). In con-trast, scenarios in which Kakamega contained a large proportion ofnatural forest or mixed-indigenous plantations (the positive and themaximal scenarios, Fig. 2) resulted in a large increase in forest spe-cialists (18% and 19%, respectively) and the most natural forestbird communities (Table 1, Figs. 1 and 3), and a decrease in themean number of forest visitors (30% for both scenarios) as com-pared to 2003. In the realistic scenario bird species richness wasprojected to resemble the one of 2003 (Table 1; Fig. 2). However,bird composition was projected to shift toward more natural birdcommunities (Figs. 1 and 3) as a result of a more compact spatialarrangement of FMT.

While total species richness was projected to change margin-ally among the different scenarios, species richness of forest spe-cialists, forest visitors and bird composition was projected to bevery sensitive to forest management (Table 1; Figs. 1 and 3).These projections showed a high variation in uncertainty valuesacross bird diversity measures, with lowest values for species rich-ness of forest specialists and highest uncertainties for communitycomposition (Table 1; Figs. 1 and 3).

DISCUSSION

The combination of land-use scenarios, remote sensing-basedland-cover data and field data on bird diversity provided a valu-able approach for visualizing and communicating conservation


planning alternatives. In addition to summarized mean values,spatially explicit extrapolations allowed stakeholders to visuallycompare the outcome of alternative scenarios. With regard to dis-tinguishing the impact of the different scenarios on bird biodiver-sity, species richness of forest specialists and communitycomposition turned out to be the most informative measuresand comparisons with a reference forest state served as valuablereference point.

For the minimal and negative scenario, the projectionsrevealed an expected decrease in mean bird species richness par-ticularly, due to a projected loss of forest specialists (Fig. 3A).The spatial distribution of bird communities in the minimal sce-nario was projected to be most dissimilar to the reference year2003 (Fig. 1), though the total mean value for PC1 within the

gazetted forest area for bird composition did not change muchfrom that of 2003. Likewise, the communities of the negative sce-nario were projected to be relatively disturbed on the one hand(Fig. 1), but showed an overall mean PC1-value that is slightlyhigher than that of 2003 on the other hand. These discrepancieswere caused by strong differences in patch shapes between thescenarios and the reference state of 2003. Previous studies dem-onstrated that patch characteristics such as size or shape com-plexity might strongly affect bird assemblages (Graham & Blake2001, Lindenmayer et al. 2002, Ewers & Didham 2006). Forinstance, the amount of patch area exposed to edges has beenshown to determine the influence of the surrounding habitat onbird communities (Restrepo & Gomez 1998, Banks-Leite et al.2010). Though patch shapes in the minimal and negative scenariohave a perimeter to area ratio favorable for birds, under both sce-narios, the bird composition of the overall forest area will differconsiderably from that of a natural tropical forest. This demon-strates that statistics aggregated over a whole forest area (as givenin Fig. 3 and Table 1) might have limited value as measure forcommunicating management decisions, though such values mayhelp to illustrate extreme contrasts, e.g., selection of alternativeareas for conservation prioritization (Marsh et al. 2010). Instead,the spatially explicit representation of projected bird diversity perunit area (Fig. 1) allows for deriving a more differentiated pictureof potential consequences of forest management decisions forbird communities and, in so doing, emphasizes its value forconservation planning.

Bird species richness in the realistic scenario was projectedto highly resemble the one of 2003 with a slight increase of forestspecialists (Fig. 3A). A more natural forest bird community wasprojected for this scenario because of more compact shapes offorest management types (FMT; Fig. 3B). Management decisionsfollowing this realistic scenario are therefore likely to conserveand improve the current status of Kakamega by extending forestcover through a mixture of plantations. In particular, largerpatches of these plantations with a smaller perimeter to area ratioare expected to contribute to this improvement in bird composi-tion (Fig. 1). For the positive and maximal scenario, mean birdspecies richness was projected to increase with more forest spe-cialists compared to 2003 (Fig. 3A). This was caused by theassumed complete coverage of the gazetted forest area with near-natural forest or mixed-indigenous plantations resulting in mostlylarge patches with small perimeter to area ratio (Fig. 1). Conse-quently, community composition of both scenarios was projectedto attain PC1-values reflecting more natural forest bird communi-ties than in 2003 (Fig. 3B). While the mean number of bird spe-cies was expected to show little differences between these twoscenarios, bird composition was projected to be highest in themaximal scenario due to the single patch of near-natural forest(Fig. 1). Thus, both scenarios will substantially contribute to birdconservation. In considering the temporal component, the loggedarea in the maximal scenario will probably take 60 to 80 yr toreach the status of near-natural forest, whereas reforestation withmixed-indigenous species of open areas will reach the final statusfaster, that is to say, after 40 to 60 yr. Therefore, the positive sce-

TABLE 1. Proportional differences in species richness per forest dependency group and

community composition between five alternative scenarios and the reference

forest state in 2003 that are inherent in the spatial distribution of bird

diversity. Shown are proportions of mean differences (� 1SD); SMin,

minimal scenario; SNeg, negative scenario; SReal, realistic scenario; SPos,

positive scenario and SMax, maximal scenario.

Forest

specialists

Forest

generalists

Forest

visitors

Community

composition

SMin vs. 2003 �40 � 6% �5 � 6% +33 � 19% �1 � 40%

SNeg vs. 2003 �28 � 3% �4 � 6% +20 � 18% +10 � 69%

SReal vs. 2003 +2 � 1% +2 � 6% �5 � 16% +21 � 61%

SPos vs. 2003 +18 � 5% +4 � 5% �30 � 15% +44 � 59%

SMax vs. 2003 +19 � 4% <1 � 4% �30 � 15% +57 � 62%

FIGURE 2. Proportion of forest management types for the reference forest

state in 2003 as well as the five different forest management scenarios, SMin,

minimal scenario; SNeg, negative scenario; SReal, realistic scenario; SPos, positive

scenario and SMax, maximal scenario. Color version available in online Sup-

porting Information.


nario seems to be the most effective, particularly as the presiden-tial logging ban of 1999 prohibits harvesting also of plantationswithin gazetted forest areas. Indeed, the Kakamega EcosystemManagement Plan is presenting a slightly modified version of thepositive scenario, that is to say, with the historical glades keptopen (KWS & KFS 2012).

To help decision processes, bird diversity, species richness offorest specialists and in particular community composition turnedout to be the most informative measures for visualizing differ-ences among the alternative scenarios (Figs. 1 and 3). Speciesrichness of forest specialists reflected the distribution of foresttypes with high numbers of forest specialist species in scenariosassuming a large proportion of near-natural forest or mixed-indigenous plantations (Figs. 2 and 3A). The loss of forest spe-cialist species and gain of forest visitor species was reflected inthe change of community composition. While biodiversity valuesaveraged over the entire area might contain only limited informa-tion, if used as the only metric (see discussion above), their com-bined use with spatially explicit visualizations (Fig. 1) appears tobe most suitable for illustrating consequences of forest manage-ment decisions to the different stakeholders.

The combination of field data and remote sensing enabledus to extrapolate spatially and to project bird diversity into thefuture via alternative management scenarios. Our projectionsshow that such an approach is feasible also for tropical forestswhere biodiversity inventories are usually less frequent (Nagendra& Rocchini 2008). Sufficient field data and understanding of suchdiverse systems represent the baseline to achieve confidence insuch spatial extrapolations. Only few studies have linked remotesensing with faunistic data in tropical forests (Ewers et al. 2010,Marsh et al. 2010, Lung et al. 2012, Palminteri et al. 2012), given

their importance for biodiversity conservation (Laurance 1999,Dirzo & Raven 2003). Maps of spatially explicit projections ofbird diversity under different forest management scenarios (FMS)can help to visualize landscape-scale forest changes and the con-sequences of management decisions on biodiversity. Thus, theycan stimulate landscape-scale planning approaches and canenhance the dialog among the stakeholders (Blomley et al. 2008,Nagendra et al. 2008). This is of particular importance for coun-tries like Kenya, where the use of spatial depictions is not gener-ally prevalent and maps are not well integrated into decisionprocesses.

Our approach has a number of limitations. Firstly, weassumed that plot-based data on bird communities could beextrapolated over larger areas with no other factors influencingspecies richness and community composition. Secondly, we con-sider a constant species pool over space and time and no changeswithin species in terms of habitat selection, resource use or abun-dance. This is simplified, as a time lag between forest conversionand local extinction of species has been reported even for Kaka-mega (Brooks et al. 1999). Thus, our mean values should be inter-preted with caution as they simply represent measures forcomparative information inherent in the spatial distribution ofbird diversity and are not able to project extirpations of individualspecies effectively. Thirdly, we are aware that our extrapolations inspace and time do not consider major changes in species interac-tions such as competition or predation. Hence, our approach iscertainly simplistic. Nevertheless, our approach allows visualizingdifferences among alternative scenarios in bird diversity and com-position for decision processes at the landscape level.

The study was based on stakeholder interests havingemerged from several years of a science stakeholder dialog for

A B

FIGURE 3. (A) Mean (�1SD) species richness for all birds and per forest dependency group and (B) mean (�1SD) bird community composition for the refer-

ence forest state in 2003 as well as the five different forest management scenarios, SMin, minimal scenario; SNeg, negative scenario; SReal, realistic scenario; SPos,

positive scenario and SMax, maximal scenario.


conservation planning in the Kakamega study area (KWS & KFS2012) rather than explicit consultations for scenario construction(cf. Nassauer & Corry 2004). Therefore, we believe that the dis-tinct stakeholder views and interests are well reflected by the rangeof scenarios developed. An explicit consultation session dedicatedto develop contrasting scenarios was not foreseen and would haveadded further challenges to the stakeholder negotiations.

To conclude, we are confident that our approach is valuablefor on-the-ground conservation management. Firstly, it includesscenarios that have been developed considering the interests ofall relevant stakeholders. Secondly, it is based on remote sensingand on bird data that are relatively easy and cheap to collect forother areas. Thirdly, it is based on simple and transparent rela-tionships between forest types, their shape and biodiversity mea-sures, of which species richness of forest specialists andcommunity composition were most informative for conservationprioritization. Lastly, it generates maps and summaries that arerelatively easy to understand for the different stakeholders. Theseprojections and visualizations do not focus on single species, buton response guilds and community composition. Thus, ourapproach might serve as a valuable tool for integrative conserva-tion planning in other species rich tropical forests.

ACKNOWLEDGMENTS

We thank the KWS and the KWS for permission to work in Ka-kamega. We thank four anonymous reviewers for their construc-tive comments on the manuscript. Financial support wasprovided by BMBF (BIOTA East Africa, subprojects E11[01LC0625E1] and E02 [01LC0625D1]).

SUPPORTING INFORMATION

Additional Supporting Information may be found in the onlineversion of this article:

TABLE S1. Estimates and summary statistics for the minimum ade-quate models describing patterns of species richness per forest dependencygroup and community composition of birds in Kakamega Forest, westernKenya.

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