Landscape-moderated biodiversity effects of ground herb cover in olivegroves: Implications for regional biodiversity conservation
Pedro J. Reya,d,⁎, Antonio J. Manzanedaa, Francisco Valerab, Julio M. Alcántaraa,d, Rubén Tarifab,Jorge Islaa, José L. Molina-Pardoa, Gemma Calvoa, Teresa Salidoa, J. Eugenio Gutiérrezc,Carlos Ruizc
a Dept. Biología Animal, Biología Vegetal y Ecología, Universidad de Jaén. E-23071 Jaén, Spainb Estación Experimental de Zonas Áridas, EEZA-CSIC, Ctra. de Sacramento s/n, La Cañada de San Urbano, E-04120 Almería, Spainc Sociedad Española de Ornitología, SEO-BirdLife, Oficina del LIFE Olivares Vivos, GEOLIT, Parque Científico y Tecnológico, c/ Sierra Morena, CTSA, E-23620, Mengíbar,Jaén, Spaind Instituto Interuniversitario del Sistema Tierra de Andalucía, Universidad de Jaén, E-23071 Jaén, Spain
A R T I C L E I N F O
Keywords:Agri-environmental schemesAntsBirdsHerbsLand use intensificationLandscape moderationMulti-diversity
A B S T R A C T
Studies assessing the effect of extensive versus intensive agricultural practices and addressing how biodiversitypatterns and the effectiveness of agri-environmental practices (AES) to recover biodiversity are moderated by thelandscape complexity (LMB framework), underlie large-scale biodiversity conservation programs and policies inanthropogenic landscapes. Such studies are numerous with annual crops and grasslands yet infrequent in morecomplex and structurally stable arboreal croplands, where high capacity to retain biodiversity is expected. Here,we explore to what extent landscape complexity and extensification of agricultural practices enhance biodi-versity in olive groves of Andalusia (southern Spain). We monitored birds, ants and herbs in paired olive farms(extensive versus intensive ground herb cover management) from 20 localities spread over Andalusia and along alandscape complexity gradient. For each biological group, we obtained gamma diversity (diversity at the olivefarm level), beta diversity (between the productive and unproductive areas located within the olive farm) andinfield alpha diversity (in the productive area within the olive farm). We tested for multi-diversity, and for eachgroup separately, three major hypotheses of the LMB: the intermediate-landscape complexity, the dominance ofbeta diversity, and the landscape species pool hypotheses. These hypotheses were corroborated with multi-diversity, which was affected by intensification of weed management and landscape simplification, suffering acombined impact of 26% of gamma biodiversity loss. The effectiveness of extensification to recover biodiversitydepended on the landscape context and peaked at intermediate-complexity landscapes. Beta multi-diversity andinfield alpha-diversity increased with landscape complexity. Birds, ants and weeds responded differently butwere negatively affected either by landscape simplification or by management intensification. Birds mirroredbetter than other groups the multi-diversity pattern and were the best candidates for a rapid indicator of theimpact of agriculture and land conversion on biodiversity. We provide recommendations for biodiversity con-servation in olive groves-dominated landscapes and show that, if adequately managed, olive groves’ landscapeshave potential for the conservation of biodiversity in the Mediterranean region. Our results illustrate the need toreformulate the future EU-Common Agricultural Policy and particularly, to adapt AES to each landscape.
1. Introduction
Agriculture is one of the most generalized land uses affecting ter-restrial ecosystems (Foley et al., 2005, 2011; Haberl et al., 2007). It hasdisplaced a considerable part of wildlife out of its original natural ha-bitats towards increasingly simplified and homogenized human-shapedlandscapes. Beyond habitat destruction, fragmentation and land
conversion, agriculture intensification has extended the use of pesti-cides and fertilizers and cropland homogenization at landscape level,causing worldwide losses of biodiversity at an unprecedented scale.Thus, it is irrefutably considered the main driver of global change andthe major cause of biodiversity loss (Tilman et al., 2001; Tscharntkeet al., 2005). It is increasingly recognized that large-scale biodiversityconservation programs will require efforts to maintain species in
https://doi.org/10.1016/j.agee.2019.03.007Received 9 November 2018; Received in revised form 4 March 2019; Accepted 8 March 2019
agricultural landscapes, as well as ecological processes in which theyare involved (Green et al., 2005; Kleijn et al., 2011; Mendenhall et al.,2014). This is crucial under the real scenario of increased demand ofland for agriculture in the next decades due to the forecasted increase ofhuman population in the world along the XXI century. In this sense,low-intensity land-use systems are important elements of large-scaleconservation programs, retaining biodiversity and providing wildlifemovement among areas (Tscharntke et al., 2005, 2012; Scherr andMcNeely, 2008).
The potential value for biodiversity conservation of an appropriatedesign of agricultural landscapes and practices, and the knowledge thatbiodiversity provides fundamental ecosystem services for crop sus-tainability, has permeated environmental and agricultural administra-tions at regional, national and supranational levels, leading to changesin agricultural directives. A clear example are the agri-environmentschemes (AES) of the European Union (EU) Common AgriculturalPolicy, CAP (see a summary of the history of EU-CAP and AES in Batáryet al., 2015). Likewise, the importance of integrating sustainable cropproduction and biodiversity conservation (Scherr and McNeely, 2008)has encouraged novel lines of scientific inquiry. To provide generalmanagement recommendations, considerable theoretical and empiricaleffort is being directed to model the consequences of the interplay be-tween agricultural management and landscape context on biodiversity,and to detect general effects of agricultural intensification/ex-tensification on biodiversity at local and landscape scales (Tscharntkeet al., 2005; Concepción et al., 2008, 2012; Kleijn et al., 2011; Díaz andConcepción, 2016). However, current knowledge is still far from gen-eralizations, and recommendations are far from being extensible to anyorganism and agroecosystem (Kleijn et al., 2006; Batáry et al., 2011,2015). Lack of generalization has stimulated the proposal of a set oftestable hypotheses (Tscharntke et al., 2012) concerning landscapemoderation of biodiversity patterns and functions (LMB framework,hereafter). These hypotheses seek to answer to what extent increasedlandscape complexity and agricultural extensification would enhancebiodiversity, ecological functions and ecosystem services. In practicalterms, they propose that landscape complexity constraints the effec-tiveness of AES to recover biodiversity and ecosystem services.
Many studies have evaluated the effects of extensive versus in-tensive practices in annual crops and grasslands, and the effectivenessof AES to recover biodiversity and its services in relation to landscapecomplexity (Tscharntke et al., 2007; Concepción et al., 2012; see alsothe reviews of Batáry et al., 2011; Scheper et al., 2013). They arehowever infrequent in woody crops and have been done mainly inNorth and Central America agrosystems (but see e.g., Klein et al., 2012with almonds; Pak et al., 2015 with coffee plantations, or Nicholsonet al., 2017 with highbush blueberry, among others). In Europe, thislack of attention to woody crops is even more marked and only veryrecently the topic has been approached in vineyards (Assandri et al.,2016; Froidevaux et al., 2017; Rusch et al., 2017). This sort of studiesare almost lacking in arboreal croplands, despite that olive, apple,cherry, citrics, pistacio or almond plantations are also well-extendedand of huge economic and ecological importance, both regionally andfor the EU. Unlike annual croplands, where the agroecosystem movesback to the initial bare ground state after harvesting, woody croplandsare structurally more complex and stable, since the cultivated trees orscrubs, and most of their associated animal biodiversity, live on formany years. The ability to retain biodiversity, and the response ofbiodiversity to the combined effects of landscape complexity andagricultural management might then differ substantially between an-nual and woody croplands (especially where forest canopy is retained,see for example Laliberté and Tylianakis, 2010). Therefore, manage-ment recommendations for biodiversity conservation should not betransferred uncritically from annual to woody croplands.
Here, we test several important postulates of the LMB concerningtaxonomic diversity in olive groves from Andalusia (southern Spain). Inparticular, we investigate the role of landscape complexity in
moderating the effect of extensive management of the ground herbcover (the commonest AES in olive groves) on biodiversity. This will beuseful to provide recommendations for biodiversity conservation inolive cultivation landscapes at regional level. We consider three groupsof organisms that occupy different trophic levels: birds, ants and herbs.First, we analyze our results combined in a multi-diversity index ofoverall biodiversity, which will be the main focus of this paper.However, because different taxa perceive their environment differently(Wolters et al., 2006; Fahrig et al., 2011), and the response of groupslike plants, arthropods and vertebrates to landscape complexity and thescale and type of agricultural management can substantially differ(Atauri and Lucio, 2001; Gabriel et al., 2010; Dainese et al., 2015;Birkhofer et al., 2018), we also test the LMB hypotheses for plants, antsand birds separately. We also consider farm size effects on each group(Fahrig et al., 2015). Finally, the specific response of each taxa is usedto evaluate the consistency in the response among groups and to assesswhether some of them could be a good predictor of (i.e. correlate with)the multi-diversity response. This could help to pinpoint a simple eco-logical indicator for biodiversity assessment and conservation (Šáleket al., 2018).
Specifically, we test three hypotheses within the LMB framework:the ‘intermediate-landscape complexity hypothesis’, the ‘dominance ofbeta-diversity hypothesis’ and ‘the landscape species pool hypothesis’(Tscharntke et al., 2012). The intermediate-landscape complexity hy-pothesis (Tscharntke et al., 2005; Concepción et al., 2008) postulatesthat the effectiveness of AES to recover biodiversity will be maximum inintermediate complexity landscapes. This effectiveness would be smallor even null in extremely homogenized agricultural landscapes becauseof the lack of sources of biodiversity (i.e., natural or semi-natural ha-bitats surrounding fields where AES are applied). AES effectivenesswould also be small or null in complex landscapes, where the mosaic ofagricultural and natural habitats would already support large speciespools, due to frequent or continuous colonization of agricultural fieldsfrom their natural or semi-natural surroundings. In contrast, in inter-mediate landscapes with intermediate amounts of natural and semi-natural habitats, AES can contribute more efficiently to the persistenceand diversity of species, favoring an effective colonization of fieldswhere they are applied. We address several predictions associated tothis hypothesis: i) biodiversity in olive farms should increase non lin-early with increasing landscape complexity up to a saturation thresholdat intermediate to high landscape complexity levels, with such satura-tion point being reached before along the landscape complexity gra-dient in extensive than in intensive management (Concepción et al.,2008); ii) biodiversity should be higher under extensive than underintensive management, with the largest difference between manage-ment practices occurring in intermediate landscapes (Tscharntke et al.,2005; Concepción et al., 2008); iii) if so, the effectiveness of AES forrecovering taxonomic diversity at the olive farm level should peak atintermediate levels of landscape complexity, and should not differsubstantially between extremely simple and very complex landscapes(Tscharntke et al., 2005; Concepción et al., 2008, 2012; Tscharntkeet al., 2012). The second hypothesis under study, the ‘dominance ofbeta-diversity hypothesis’ (Tscharntke et al., 2007, 2012), poses thatsimplification of the landscape results in biodiversity loss at the farmlevel mainly due to loss of beta diversity between habitats. Based onthis postulate, we predict (iv) an increase of beta diversity betweenproductive and unproductive zones of the olive farmlands with in-creasing landscape complexity. The third hypothesis, ‘the landscapespecies pool hypothesis’ states that the size of the landscape-wide spe-cies pool moderates local (alpha) diversity (Tscharntke et al., 2012).Following this, we would expect that (v) infield alpha diversity (i.e., thediversity within the productive zone of the olive farms) will also in-crease with landscape complexity.
Mediterranean olive groves are an ideal study system to test LMBpostulates and their application to biodiversity conservation in arborealcroplands since they are the most important woody crop in Europe both
P.J. Rey, et al. Agriculture, Ecosystems and Environment 277 (2019) 61–73
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in extension and economically. Olive cultivation is considerably in-creasing in North Africa, North and South America, China, or Australia,reaching currently more than 11 million hectares worldwide, similar tothe land used for plantations of cacao or coffee and only surpassed byoil palm and yucca among the woody croplands (Pirker et al., 2016;FAO statistics for 2016; http://www.fao.org/faostat/es/#data/QC).More importantly, olive groves in the Mediterranean region can boostbiodiversity conservation for several reasons. First, they are distributedthroughout one important biodiversity hotspot in Europe (the Medi-terranean hotspot, Myers et al., 2000); second, their semi-forest naturemakes this culture a potential refuge for flora and fauna of naturalforests and scrublands; third, their structural complexity provides asofter matrix among natural habitats (allowing temporal settlement andmobility among patches for many animals and plants) than the oneoffered by annual croplands (Rey, 1995, 2011). Finally, cultivated olivetrees and its ancestor, the wild olive Olea europaea var. sylvestris, shareecological and evolutionary histories of interactions with native ani-mals and microorganisms in the Mediterranean region (Rey, 2011).Hence, unlike many other non-native crops grown worldwide, theecological relationships of olive trees, although simplified, are notshaped de novo; this again may provide stability to the olive grovebiodiversity and its function. Studies on olive groves biodiversity arerapidly accumulating (see Allen et al., 2006, for ground cover vegeta-tion; Rey, 1993, 2011; Castro-Caro et al., 2014, for birds; Carpio et al.,2016 for herpetofauna; Sánchez-Moreno et al., 2015, for soil nema-todes; Cotes et al., 2010; Santos et al., 2007; Gonçalves and Pereira,2012; da Silva et al., 2017, for soil arthropods; Carpio et al., 2018 fortree canopy arthropods; Paredes et al., 2013, for natural enemies ofolive pests; Scalercio et al., 2007, 2012; Tscheulin et al., 2011, for wildpollinators, among others). Nevertheless, these studies have been con-ducted in single groups of organisms, at local or small-scales, and noneof them have explored explicitly how the landscape moderates the ef-fects of agricultural management on biodiversity in this culture.
2. Material and methods
2.1. Selection of the olive groves
This study was conducted in olive-growing areas of Andalusia,southern Spain. Andalusia, with more than 1.5 million hectares of olivegroves, is the region of the world with the largest extension dedicated tothis culture, which in many sites is a centenary or even millenary cul-tivation. We selected 20 localities distributed along the areas with thegreatest extension of olive groves (Fig. 1). These localities were alsochosen to encompass a wide gradient of landscape complexity. In theselocalities, we periodically monitored biodiversity using birds (one ofthe most commonly used vertebrate indicators), ants (which representhere the ground-dwelling arthropod fauna, known to be particularlysensitive to soil management and agrochemicals, Lobry de Bruyn, 1999)and herbs (representing the basal trophic level, which in the last dec-ades was widely displaced from agricultural lands by the massive use ofherbicides), as indicator organisms sensible to the intensive manage-ment of herbaceous cover and landscape simplification. The olive treesin all the localities were more than 30-years old and were grown with aplantation frame of 7 x 8m or higher. Thus, we discarded young and themodern hedge-like plantations, which although rapidly proliferatingare still marginal in the region.
2.2. Sampling design
Our sampling design considered the most frequent AES for olivecultivation under EU-CAP, the maintenance of the ground herb cover inthe olive farms. Thus, as it is common in the LMB tests (for instance,Concepción et al., 2012), our biodiversity assessments of birds, ants andherbs were conducted in two paired olive farms by locality (i.e., 40olive farms in total), each pair composed by farms with a different
management of the ground herb cover: (1) a farm with intensivemanagement which most frequently involved the use of pre-emergenceand/or post-emergence herbicides and/or recurrent plowing for coverelimination over the whole year; and (2) a farm with extensive man-agement of the cover, implying its maintenance during most of the yearand its eventual removal by mechanic mowing in late spring or by cattlegrazing (cows, horses or sheeps). The two farms in each locality werewithin a circle of 2 km radius which was the scale used to characterize acommon landscape. The landscape of each locality was initially clas-sified by visual inspection into three categories of complexity (Fig. A.1):(a) simple landscapes, in which olive groves predominate and where, ifelse, some other crop (frequently cereals) are intercalated but whereremnants of natural habitat are scarce; (b) intermediate landscapes, inwhich olives groves are interspersed with other crops and some naturalor semi-natural habitat remnants (often semi-natural forests, affor-estations, scrublands or grasslands); and (c) complex landscapes whereolive groves, which may not be the major land use, co-occur with adiverse representation of natural habitats (forests, scrublands andprairies). We used recent land use cartography of the region (SIOSE2013; http://www.siose.es) for estimating typical landscape composi-tional and configurational heterogeneity metrics (Fahrig et al., 2011)within the 2 km radius circle. In particular, we considered five com-positional heterogeneity indices – land use or patch richness, diversityand evenness, percentage of natural/semi-natural habitat cover andpercentage of olive groves in the landscape— and seven indices ofconfigurational heterogeneity: proportion of the total landscape occu-pied by the largest patch; edge density of the mean patch; mean patcharea; shape of the mean patch; euclidean distance between nearestneighbor patches of similar uses; contagion and interspersion/juxta-position index. These indices were obtained for each locality fromFRAGSTAT v4 (McGarigal et al., 2012). Classification and regressiontree analysis (CART, Urban, 2002) was used to validate our initialsubjective classification (i.e., simple, intermediate and complex land-scapes) of the study localities. CART also determined which of thelandscape heterogeneity metrics contribute most to discriminate amonglandscape complexity levels. The main advantages of this techniqueover other classification techniques are that it does not make any as-sumptions on error distribution or linearity, and that it defines cut-offpoints in metrics that optimize the discrimination among levels oflandscape complexity. CART analysis showed that the percentage ofnatural habitat cover (‘cobnat’), mean patch size, and the distance be-tween nearest neighbor patches of similar use (NND) correctly classified100% of the study localities into the three categories of landscapecomplexity originally defined, confirming that, overall, our perceptualclassification of landscape can be translated into objective metrics (Fig.A.2). Simple landscapes were characterized by low representation ofnatural habitat (‘cobnat’ approximately< 9%), intermediate land-scapes by ‘cobnat’ larger than 9% and NND higher than 85m, andcomplex landscapes where those with ‘cobnat’ larger than 9% and amosaic of uses with NND less than 85m. This reflects a more coarse-grained mosaic of land use in intermediate than in complex landscapes.
To explore the effect of the scale of herb cover management onbiodiversity (Rundlöf et al., 2008) and on the effectiveness of AES torecover biodiversity, we added a third factor to our sampling design,the olive farm size, which distinguishes between small (< 25 ha, mostfrequently< 10 ha, range 5.6–20 ha) and large farms (> 50 ha, mostfrequently> 100 ha). It was logistically unfeasible to consider otherintermediate-sized class farms. Nonetheless, our design is re-presentative of the effect of the scale of management, given that themean size of the olive farms in Andalusia is around 7.3 ha (Anuario deEstadísticas Agrarias y Pesqueras de la Consejería de Agricultura, Pescay Desarrollo Rural, 2015). Thereby, we covered the most frequent sizesof the properties (representing small-scale management) and comparedthem with management at a much larger scale. All combinations of herbcover management, landscape complexity and farm size were replicatedthree or four times.
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Biodiversity recording was conducted from April 2016 to March2017 by setting permanent monitoring stations (50m radius) in eacholive farm. In particular, we set six stations in small olive farms and tenin large farms for herb and bird censuses. In small farms, two samplingstations were located in unproductive zones (uncultivated areas likemargins, semi-natural habitat remnants, hedges, riparian vegetationstrips and galleries) and four in productive zones (within the matrix ofcultivated olive trees). In large farms, four stations were set in un-productive zones and six in productive zones. Birds were surveyedusing the point census method, considering the individuals seen orheard at each census stations. Herbs were censused in one 1 x 1msquare located in each station. In the case of ants, twelve stations wereset in all the olive farms, each one with a pitfall trap (four in un-productive and eight in productive zones) with all sampling stations ofherbs and birds being used also as ant monitoring stations. Pitfall traps(7 cm diameter x12 cm depth) were filled with a 1:1 mixture of waterand propylene glycol (non-toxic) and some soap drops. Ants were col-lected and determined in the laboratory by using a 10x-45x stereo-microscope. The classification of herbs was conducted in the field whenpossible, yet material was also collected for taxonomic corroboration inthe lab if needed. In total, we used 328 census stations for birds (tenmonthly censuses in the year) and herbs (3 monthly surveys fromMarch to June) with a total of 3280 bird censuses and 984 herb surveys,while the total effort for ants was 480 sampling stations with 3360 pitfall traps collected (7 months per locality, from April to November,when ants are active).
The distinction between unproductive and productive zones in theolive farm was used to obtain beta-diversity (see below) and infield(within the productive zone) alpha-diversity estimates in each olivefarm. All data used here is based on incidence, that is, on the occurrenceof the species in a sampling station, instead of on abundance. This isjustified because our aim is to compare patterns of diversity among
groups and obtain an overall biodiversity (multi-diversity) estimate. Tothis end, the abundance of herb and ant species is not as well char-acterized by our samplings as birds, since, for example, pitfall trapsmight overestimate abundance if the traps are close to the ant nests orintercept an ant trail (Lobry de Bruyn, 1999).
2.4. Biodiversity indices and effectiveness of the extensification (agri-environmental) practices
The biodiversity indices used here are based on species accumula-tion curves and use the information on species occurrence in samplingstations. We estimated indices of species richness for birds, ants andherbs separately. The information of all the periodic surveys from eachpermanent sampling station in each olive farm was pooled for the es-timation of such indices at the olive farm level. Following Chao et al.(2014), we used the point of the species accumulation curve corre-sponding to an extrapolation of twice the number of samples used toproject the curve as an unbiased estimate (projection) of species rich-ness. Therefore, we estimated species richness at the cut-off point oftwice the minimum sample size across study sites (olive groves). Suchsample size was 12 for birds and herbs and 24 for ants. At these samplesizes, the minimum species coverage on the species accumulation curveacross olive groves was 0.93 for birds, 0.93 for ants and 0.71 for herbs,which indicates that our estimates were overall close to the asymptote.These indices represent the species richness at the farm level (i.e., ourgamma diversity) for each group of organisms used as biodiversity in-dicator.
We were also interested in beta-diversity, which was estimatedfollowing Lande’s beta diversity index (Lande, 1996) considering twotypes of habitat in each olive farm: the productive and the unproductivezones of the farm. This index takes the form:
β-diversity = Σqj(ST-Sj)
where qj is the proportional weight of the habitat j (here the number of
Fig. 1. Location of the study sites within Andalusia (southern Spain) with characterization of the type of landscape and size of the olive farms under study. Thedistribution of olive plantations in Andalusia is also shown (green colour; data from SIOSE 2013 available at http://www.juntadeandalucia.es/medioambiente/site/rediam/).
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sampling stations in productive and unproductive zones of the farm), STis the total number of species registered in the farm, and Sj is thenumber of species detected in habitat j.
Regarding the estimation of the effectiveness of extensification ofagricultural management in recovering biodiversity, we calculated foreach pair of olive farms within a locality the increase in biodiversity asthe difference between biodiversity in farmlands with extensive andintensive management.
Finally, because we are concerned not only with the biodiversity ofeach group of indicator organisms but more importantly with effects oflandscape complexity and agricultural management on overall biodi-versity, we obtained a multi-diversity estimator as the mean z-scoresacross groups (Maestre et al., 2012; Birkhofer et al., 2018). Z-scoreswere obtained separately for each taxonomic group from the mean andstandard deviation of the normal distribution of its estimated speciesrichness across the 40 olive farms under study. In this way, diversity ofeach group was standardized to contribute similarly to multi-diversity,independently of its intrinsic scale of variation since diversity of allgroups was rescaled in standard deviation units (Maestre et al., 2012).
Information for each olive grove on gamma, beta and infield alphadiversity of each taxonomic group and for multi-diversity may be foundin Mendeley data repository for this article, together with data onlandscape heterogeneity metrics and geographic coordinates for thelocalities of study (see also Table B1). All species detected in this studyare also listed in such repository and in Tables B2-B4.
2.5. Statistical analyses
Before testing LMB we explored, by fitting General Linear Models,the existence of geographic effects on species richness. To this end, therelationship between the estimated species richness at the olive farmlevel (gamma diversity) and the geographic coordinates was testedthrough multiple regression conducted separately in olive farms withextensive and intensive management for multi-diversity and diversity ofeach taxonomic group. Species richness at the olive farm level wasunrelated to geographic coordinates in all organisms, considered se-parately or together (Table A.1). Consequently, we discarded anygeographic effect in the patterns here shown.
Provided the paired design (extensive versus intensive farming
within each locality), we conducted a repeated measures ANOVA to testwhether variation in estimated species richness (gamma diversity), betadiversity and infield alpha diversity were influenced by management,landscape complexity and farm size. Locality was the subject in thisdesign and type of management represented the within-subject effect,while landscape complexity and farm size were considered fixed fac-tors. Effectiveness of extensification to recover biodiversity was testedusing Generalized Linear Model with normal error distribution andidentity link-function and with landscape complexity and farm size asfixed effects.
Finally, we tested for correlations across localities between multi-diversity and diversity of each taxonomic group and between taxo-nomic groups using Pearson’s correlation. Inspection of residualsshowed that in all analyses they can be assumed to be normal withouttransformation. All analyses were conducted with STATISTICA 8.0(StatSoft Inc., 2007).
3. Results
In total we recorded 165 bird species (ranging from 37 in Cañadadel Duz intensive management to 79 in Ojuelos extensive management)belonging to 119 genera and 52 families, 58 ant species (varying be-tween 16 in Cañada del Duz intensive management and 33 in Virgen delos Milagros extensive management) comprising 18 genera and 3 sub-families, and 549 herb species (ranging from 29 in Gascón intensivemanagement to 117 in Ardachel extensive management) of 271 generaand 59 families. Considering the three groups of species pooled, thetotal number of species detected in surveys ranged from 98 species in LaQuinta (an intensively managed small farmland in a simple landscape)to 206 species in Ardachel (an extensively managed small farmland in acomplex landscape). Thus, substantial variation in species richnessamong olive farms exists.
3.1. Testing LMB with multi-diversity
Species richness estimated by accumulation curves and averagedacross biodiversity groups, varied substantially among olive farms(range of variation: 39.02–73.71 species/group; mean=55.32 species/group).
Table 1Summary of ANOVA and GLMM analyzing the variation in olive farming biodiversity. a) Gamma multi-diversity according to landscape, management and farm size;b) Effectiveness of AES (estimated as relative increase in multi-diversity by changing from intensive to extensive management) according to landscape complexityand farm size; and c) beta multi-diversity according to landscape, management and farm size. Significant effects are in bold type (italics for marginally ones,0.05 < P < 0.1). Separate tests are shown for multi-diversity and the diversity of birds, ants and herbs.
MULTI-DIVERSITY BIRD BIODIVERSITY ANT BIODIVERSITY HERB BIODIVERSITY
a. GAMMA DIVERSITY df F P F P F P F PFarm size (S) 1 0.76 0.399 0.23 0.636 0.64 0.437 1.95 0.185Landscape complexity (L) 2 13.31 0.0005 2.38 0.129 5.54 0.017 2.87 0.090S x L 2 2.00 0.173 1.35 0.291 0.60 0.561 0.30 0.744Management (M) 1 18.59 0.001 6.29 0.025 4.15 0.061 5.92 0.029M x S 1 2.04 0.175 0.057 0.815 3.07 0.102 0.05 0.833M x L 2 2.26 0.141 3.63 0.054 0.40 0.677 0.88 0.436M x S x L 2 1.86 0.192 1.83 0.196 0.57 0.579 0.21 0.813Error 14b. EFFECTIVENNESS OF AESFarm size (S) 1 2.85 0.091 0.082 0.775 4.38 0.036 0.07 0.798Landscape complexity (L) 2 6.54 0.038 10.36 0.006 1.15 0.563 2.52 0.283S x L 2 5.19 0.075 5.24 0.073 1.63 0.444 0.60 0.741c. BETA-DIVERSITYFarm size (S) 1 4.54 0.051 1.76 0.206 0.31 0.589 3.53 0.081Landscape complexity (L) 2 4.51 0.030 2.43 0.124 1.46 0.266 1.39 0.282S x L 2 0.85 0.447 0.67 0.527 0.32 0.734 0.24 0.788Management (M) 1 0.79 0.389 0.005 0.945 2.95 0.108 0.22 0.644M x S 1 1.69 0.215 0.06 0.814 1.37 0.262 0.65 0.535M x L 2 3.89 0.045 6.16 0.012 0.27 0.767 0.37 0.698M x S x L 2 0.61 0.056 0.72 0.506 0.20 0.825Error 14
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Our predictions regarding the ‘intermediate-landscape complexityhypothesis’ were corroborated to a large extent. Multi-diversity wassignificantly affected by the management practices and landscapecomplexity (Table 1a). In fact, univariate tests showed that landscapecomplexity affected multi-diversity both in extensively and in in-tensively managed farms (F2,14 = 9.86, P=0.002; F2,14 = 9.21,P=0.003, respectively). The mean species richness per group in-creased with landscape complexity (Least-square means± 1SE=48.10 ± 2.35, 56.41 ± 2.57, and 60.42 ± 2.51 species in simple,medium and complex landscapes, respectively) (prediction 1) and wasalso higher in extensive (58.14 ± 1.66 species) than in intensive
management (51.81 ± 1.63). Although the extensive managementcurve of multi-diversity was always above the curve in intensive man-agement (Fig. 2a), post-hoc tests showed that there was no significantdifference in biodiversity between management practices at simple andcomplex landscapes (Tukey test, P= 0.34 and 0.64, respectively). Yet,such difference was significant at intermediate landscapes (P= 0.008)(prediction 2). The joint contribution of the extensification of groundherb cover management and increased landscape complexity resulted inthe gain of 16.3 species per group, from 46.31 ± 2.92 in intensivefarmlands in simple landscapes to 62.64 ± 2.92 in extensive farmlandsin complex landscapes. In other words, on average one out of fourspecies is lost per group by severe agricultural intensification andlandscape simplification. No significant effect, simple or in interactionwith other factors, was detected for olive farm size, suggesting thatthere is no effect of the scale of the management practice on overallbiodiversity.
As predicted by the intermediate landscape-complexity hypothesis,the effectiveness of the extensification practices for biodiversity re-covery depended on the complexity of the landscape surrounding thefarmlands (Table 1b), peaking at intermediate landscapes (prediction 3)(Fig. 2b), where the augmentation in species richness was 11.0 ± 2.5species per group. Furthermore, the effectiveness of extensification didnot differ between simple (mean increase in species richness per groupof 3.6 ± 2.3 species) and complex landscapes (4.4 ± 2.4). There wasalso a marginally significant effect of the size of the farm on the ef-fectiveness of extensification but it depended on the landscape com-plexity (Table 1b). In particular, the comparison of model parameterestimates showed that farm size was only influential in simple land-scapes, where the effectiveness of extensification in recovering biodi-versity was higher for small than for large farms (Fig. A.3).
Regarding the ‘dominance of beta-diversity hypothesis’, beta multi-diversity between productive and unproductive zones of the olivefarmland varied significantly among landscape complexity levels(Table 1c) with a general trend to increase with the complexity of thelandscape (prediction 4), although this effect depended on the man-agement practice (Table 1c, Fig. 2c). Univariate tests showed an effectof complexity in both extensive and intensive farms (F2,14= 5.98,P= 0.01; F2,14 = 3.51, P=0.058, respectively) but the increase ofbeta-diversity with landscape complexity was more pronounced in in-tensive management (Fig. 2c).
We also found that the overall infield alpha diversity decreasedconsistently with the simplification of the landscape, as predicted bythe ‘landscape species pool hypothesis’ (prediction 5) (Table 2). It wasalso significantly higher in extensive than in intensive management.
In short, predictions derived from the LMB are verified to a largeextent in olive groves when all biodiversity indicators are consideredtogether.
3.2. Testing LMB separately with birds, ants and herbs
Bird biodiversity was affected by ground herb cover management.Species richness was higher in extensive than in intensive management(63.61 ± 2.11 species versus 58.53 ± 1.69, respectively), while theeffect of landscape depended to some extent on the managementpractice as denoted by a marginally significant interaction of bothfactors (Table 1a). While bird species richness increased consistentlywith landscape complexity in intensive management (univariate testF2,14= 4.78, P= 0.026), it did not vary significantly in extensivemanagement (univariate test, F2,14= 1.22, P= 0.275, Fig. 3a). Thejoint contribution of management extensification and landscape com-plexity at farm level resulted in a gain of ca. 15 species, from53.28 ± 2.77 in intensive management in simple landscapes to68.05 ± 3.79 in extensive management in intermediate landscapes.This means that on average one out of five bird species is lost by thecombined effect of agricultural intensification and landscape simplifi-cation. As a consequence, the effectiveness of extensification practices
Fig. 2. Variation in farm overall diversity. a) Gamma multi-diversity is plottedaccording to landscape and herb management; b) Effectiveness of AES (esti-mated as the difference in multi-diversity between farms in extensive and in-tensive management) is plotted according to landscape complexity and sig-nificant differences between complexity levels based on model parameterestimates are identified with different letters; and c) beta multi-diversity isplotted according to landscape and herb management. The corresponding tests(interaction effects between factors in a and c) are shown above each plot.Means and 95% CI are shown.
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for the recovery of biodiversity significantly varied with landscapecomplexity (Table 1a). As predicted by the intermediate landscape-complexity hypothesis, it peaks in intermediate landscapes, where itscores ca. 11.3 ± 3.0 species (21% of relative increase in bird species),although it remains high in simple landscapes, while it was significantlylower (in fact, null) at highly complex landscapes (Fig. 3b). Similarly tomulti-diversity, there was also some influence of the farm size on theeffectiveness of extensification that depended on the landscape com-plexity (Table 1b); in particular, the comparison of model parameterestimates showed that farm size was only influential in complex land-scapes, where the effectiveness of extensification in recovering birdbiodiversity was higher for large than for small olive farms (Fig. A.3).The effect of landscape on bird beta-diversity depended on agriculturalmanagement (Table 1c). As predicted by the dominance of beta-di-versity hypothesis, beta diversity consistently increased with the com-plexity of the landscape in intensive management. However, it peakedat intermediate complexity in extensive management farms, mirroring
to a large extent the pattern found for the species richness estimated forthe whole olive farm (Fig. 3c). Infield bird alpha diversity remainedstatistically invariant with landscape complexity, and only depended onmanagement, being higher in extensive than in intensive management(Table 2). Overall, it may be concluded that the results on variation ofthe bird species richness at the farm level, the effectiveness of ex-tensification, and the beta diversity were analogous to those found formulti-diversity (compare Figs. 2 and 3).
Ant biodiversity varied with landscape complexity (Table 1a). In-termediate landscapes held significantly higher species richness(30.15 ± 1.67 species) than simple ones (22.79 ± 1.52 species) butnot more than complex landscapes (27.51 ± 1.63 species) (Tukey post-
Table 2Tests (ANOVA) of variation in alpha diversity within productive zones of theolive farms (infield alpha diversity). Only means for the effects of major interest(landscape complexity and management) are shown and significant differencesin post-hocs tests are indicated with different superscript. Separate tests areshown for multi-diversity and the diversity of birds, ants and herbs.
df F P Factors levels Mean±1SE
MULTIDIVERSITTYFarm size (S) 1 2.34 0.15Landscape complexity
(L)2 7.75 0.005 Simple
IntermediateComplex
−0.55 ± 0.16a
0.08 ± 0.18b
0.35 ± 0.17b
S x L 2 2.23 0.14Management (M) 1 17.54 0.001 Extensive
Intensive0.24 ± 0.14a
−0.32 ± 0.09b
M x S 1 0.41 0.53M x L 2 0.62 0.55M x S x L 2 1.22 0.33Error 14BIRD BIODIVERSITYFarm size (S) 1 3.19 0.10Landscape complexity
(L)2 1.33 0.30 Simple
IntermediateComplex
45.50 ± 2.25a
47.35 ± 2.47a
50.83 ± 2.41a
S x L 2 1.50 0.26Management (M) 1 8.12 0.013 Extensive
Intensive50.22 ± 1.71a
45.57 ± 1.47b
M x S 1 1.56 0.23M x L 2 0.38 0.69M x S x L 2 0.42 0.66Error 14ANT BIODIVERSITYFarm size (S) 1 1.88 0.19Landscape complexity
(L)2 5.49 0.017 Simple
IntermediateComplex
15.90 ± 0.91a
20.28 ± 1.00b
18.75 ± 0.98ab
S x L 2 0.84 0.45Management (M) 1 1.63 0.22 Extensive
Intensive18.84 ± 0.76 a
17.77 ± 0.61 a
M x S 1 4.85 0.045M x L 2 0.18 0.84M x S x L 2 1.61 0.23Error 14HERB BIODIVERSITYFarm size (S) 1 7.91 0.014Landscape complexity
(L)2 8.17 0.004 Simple
IntermediateComplex
30.90 ± 4.01a
38.86 ± 4.40ab
54.41 ± 4.29b
S x L 2 1.83 0.20Management (M) 1 9.60 0.008 Extensive
Intensive49.20 ± 3.48a
33.59 ± 3.54b
M x S 1 0.0003 0.99M x L 2 0.52 0.60M x S x L 2 0.18 0.84Error 14
Fig. 3. Variation in farm bird diversity. a) Gamma multi-diversity is plottedaccording to landscape and herb management; b) Effectiveness of AES (esti-mated as the difference in species richness between farms in extensive andintensive management) is plotted according to landscape complexity and sig-nificant differences between complexity levels based on model parameter es-timates are identified with different letters; and c) beta diversity is plottedaccording to landscape and herb management. The corresponding tests (inter-action effects between factors in a and c) are shown above each plot. Means and95% CI are shown.
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hoc tests, P= 0.013 and 0.15, respectively). There was also a margin-ally significant effect of management on ant biodiversity that was in-dependent of the landscape. Ant species richness was consistentlyhigher in extensive than in intensive management across the landscapegradient (Fig. 4a). The joint contribution of extensification of groundherb cover management and increase in complexity of the landscapewas a rise of 9 ant species, from 22.25 ± 1.52 under intensive man-agement in simple landscapes to 31.77 ± 2.36 under extensive man-agement in intermediate landscapes. This means that on average almostone out of three ant species is lost by agricultural intensification andlandscape simplification. The effectiveness of ant species recovery byextensification did not depend on the level of landscape complexity
(Fig. 4b). In fact, it was unrelated also to any continuous metrics of thelandscape heterogeneity (Table A.2). However, it depended on the olivefarm size: in large olive farms effectiveness was virtually nil(0.37 ± 1.5 species) while it scored 4.98 ± 1.64 in small ones, ren-dering 22% of increase in species number in these farms. Ant beta-di-versity between productive and unproductive zones in the farmlandswas unaffected by landscape complexity or herb cover management(Table 1c, Fig. 4c). This was endorsed by the lack of relationship withany landscape heterogeneity metric (Table A.2). In contrast, infieldalpha diversity was significantly affected by landscape complexitybeing higher at intermediate level of complexity than in simple land-scapes (Table 2).
As expected, herb cover biodiversity depended on management and
Fig. 4. Variation in farm ant diversity. a) Gamma multi-diversity is plottedaccording to landscape and herb management; b) Effectiveness of AES (esti-mated as the difference in species richness between farms in extensive andintensive management) is plotted according to landscape complexity; and c)beta diversity is plotted according to landscape and herb management. Thecorresponding tests (interaction effects between factors in a and c) are shownabove each plot. Means and 95% CI are shown.
Fig. 5. Variation in farm herb diversity. a) Gamma multi-diversity is plottedaccording to landscape and herb management; b) Effectiveness of AES (esti-mated as the difference in species richness between farms in extensive andintensive management) is plotted according to landscape complexity; and c)beta diversity is plotted according to landscape and herb management. Thecorresponding tests (interaction effects between factors in a and c) are shownabove each plot. Means and 95% CI are shown.
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on landscape complexity (though marginally) but no interdependencebetween both factors was found (Table 1). Herb species richness washigher in extensive (82.67 ± 4.40 species) than in intensive manage-ment (71.42 ± 5.15) and it tended to increase in both types of man-agement across the gradient of landscape complexity (Fig. 5a). Yet,univariate tests showed that this trend was significant only in extensivemanagement (F2,14= 3.76, P=0.049 in extensive; F2,14= 1.21,P=0.28, in intensive management). The joint contribution of man-agement extensification and landscape complexity was an augmenta-tion of ca. 32 species, from 63.40 ± 8.44 in intensive management insimple landscapes to 95.22 ± 7.72 species in extensive management incomplex ones. This means that, on average, one out of three species ofherbs is lost by the combined action of agricultural intensification andlandscape simplification. Neither the effectiveness of recovery of herbbiodiversity with extensification nor beta-diversity varied significantlywith landscape complexity or management practices (Table 1,Fig. 5b,c), although beta-diversity kept quadratic relationships withboth the percentage of natural habitat cover (peaking at intermediatevalues) and NND (with a minimum at landscapes with intermediatedistance between patches of similar land use) (Table A.2). However,infield herb alpha diversity was affected by landscape complexity, witha consistent trend to decrease with the simplification of the landscape(although post-hoc tests were only significant between simple andcomplex landscapes, Table 2).
3.3. Assessing the best single indicator group for multi-diversity
Multi-diversity correlated significantly with the species richness inthe three taxonomic groups considered, the highest correlation beingwith birds (Table 3). At the olive farm level, species diversity (gammadiversity) of the three taxonomic groups was uncorrelated with eachother and this was true even considering extensive and intensivemanagements separately. When considering infield alpha diversity, wedetected a significant correlation only between birds and herbs but thiscorrelation was in fact significant only in extensive farms (Table 3).
4. Discussion
Studies on the combined effects of intensification of agriculturalpractices and landscape heterogeneity on biodiversity, devoted to im-prove the effectiveness of AES to retain biodiversity, are scarce inwoody crops and particularly in arboreal croplands, despite their as-sumed value for biodiversity conservation (Kehinde and Samways,2012, 2014; Froidevaux et al., 2017). Using the LMB framework, weshow here that the combined effect of intensification of agriculturalpractices and landscape complexity is indeed of much relevance for theamount of biodiversity retained in olive groves from Andalusia, and
that the landscape context should be considered to enhance the effec-tiveness of the AES (i.e., maintenance of a herbaceous cover) to recoverbiodiversity (Díaz and Concepción, 2016).
4.1. Testing LMB hypotheses
Examination of overall biodiversity confirmed largely the postulatesof the intermediate-landscape complexity, the dominance of beta-di-versity, and the landscape species pool hypotheses (Tscharntke et al.,2012). First, both landscape simplification and management in-tensification impacted negatively on multi-diversity in Andalusian olivegroves. There are studies showing that multi-diversity increased withlandscape complexity in cereal farmlands (Birkhofer et al., 2018) orthat land-use intensification affected multi-diversity in grasslands(Allan et al., 2014). However, to our knowledge, this is the first studyexploring how the landscape moderates the effects of agriculturalpractices on multi-diversity. More importantly, we found that the dif-ference in multi-diversity between extensively and intensively managedolive farmlands peaked at intermediate landscapes, while it was muchreduced in simple and complex landscapes, as predicted by the inter-mediate-landscape complexity hypothesis. Therefore, the shift fromintensive to extensive management would be particularly effective inrecovering biodiversity in landscapes with intermediate complexity.Moreover, our results confirmed to a large extent the underlying theorygiving rise to this pattern. There were differences between extensiveand intensive management in the pattern of non-linear increase ofbiodiversity with the increase of landscape complexity (Concepciónet al., 2008, 2012). While extensive agricultural management mademulti-diversity raise until a saturation threshold at intermediate land-scapes (Fig. 2b), diversity steadily increased with landscape complexityin intensive management.
On the other hand, as proposed, beta-multi-diversity between pro-ductive and unproductive zones of the olive farms decreased with thesimplification of the landscape, meaning that it contributed more togamma diversity as landscape complexity increases (see Santana et al.,2017, with birds). This pattern tended to hold both in extensive andintensive farms although in the former the differences were due to theerosion of beta diversity especially in the most simplified landscapes,while in intensive farms the decrease in beta diversity with landscapesimplification is continuous. In any case, it illustrates the importance ofmaintaining vegetated margins, hedges, grasslands, forested habitatremnants and other unproductive zones for the biodiversity of thewhole olive farmland, which is congruent with findings from studies onannual croplands and grasslands (Schmidt et al., 2005; Molina et al.,2014). Finally, as proposed by the landscape pool hypothesis, we alsofound that the diversity within the productive zone of the olive farm-lands (i.e., overall infield alpha diversity) also decreased consistently
Table 3Pearson’s correlations of gamma diversity (diversity at the olive farm level) and infield alpha multi- and taxonomic group-specific diversities with diversities of birds,ants and herbs at same levels. Correlations are shown for all olive groves (N=40) and separately for extensively managed and intensively managed olive groves(N=20 in each case). Significant correlations at P < 0.05 are in bold type.
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with the simplification of the landscape. This is, to some extent, con-gruent with results on other woody croplands, like coffee plantations,almond and apple orchards, highbush blueberries or vineyards, whichshowed that proximity to native forest or semi-natural habitats re-presents a source for infield biodiversity (Klein et al., 2012; Karp et al.,2013; Pak et al., 2015; Froidevaux et al., 2017; Nicholson et al., 2017;García et al., 2018). In any case, our results highlight the importance ofnon-productive zones as sources of biodiversity, not only for the bio-diversity at farm and landscape level but also for infield diversity. Theyalso raise concerns about the ability of olive groves themselves (i.e., byits semi-forest or arboreal nature) to retain much of the biodiversity ofthe natural habitats remnants. In contrast, our results show the strongdependency of the biodiversity in the olive orchards on the presence ofnatural elements in the landscape.
Many studies have considered simultaneously several groups oforganisms to scan the wide variation of biodiversity in response toagriculture intensification, land conversion and landscape transforma-tion (Concepción et al., 2012; Wood et al., 2017; Šálek et al., 2018).They have shown that plants and different groups of animals responddifferently to the environment, and that the scale to which this responseis produced is also typically different (Gabriel et al., 2010; Daineseet al., 2015; Froidevaux et al., 2017). As many others, we found taxo-nomic group-specific responses to landscape heterogeneity and agri-culture intensification.
Bird species richness was mainly affected by the intensity of agri-cultural practices (see also Castro-Caro et al., 2014), while the effect oflandscape depended to some extent on management. This contrastswith studies on other crops (Assandri et al., 2016, for vineyards;Redlich et al., 2018, for arable lands; Froidevaux et al., 2017, for bats invineyards) that found that landscape heterogeneity was more importantfor bird species richness or diversity than management practices or cropdiversity, as could be expected from their high mobility and dispersalability. In any case, our results are interpretable attending to the un-derlying theoretical non-linear relationships between biodiversity andlandscape complexity (Concepción et al., 2008, 2012). Thus, extensiveagricultural management raised bird diversity until the theoretical sa-turation threshold (landscape bird species pool) at intermediate land-scapes. However, under intensive management, bird diversity steadilyincreased with landscape complexity until the bird species pool, whichsuggests that landscape complexity compensates the impact of intensivemanagement, allowing more bird species to persist by having access tosemi-natural, non-productive habitats (i.e. by increased beta diversity,as shown in Fig. 3c). The pattern of bird species richness (and the ef-fectiveness of extensification for bird recovery) is congruent with the‘compensation effect’ of landscape complexity on local managementmodel by Concepción et al. (2008) (see model b on Fig. 2 in her study)that renders a negative effect of landscape complexity on effectivenessof AES. This is illustrated in our Fig. 3b, showing that effectiveness ofrecovery of bird diversity by extensification was similar in simple andintermediate landscapes but decreased substantially in complex ones.
Ant richness in olive groves varied according to herb cover man-agement (being consistently higher in extensive management) andlandscape complexity, but these effects were not significantly inter-dependent. In fact, ant richness showed a parallel, non-linear trend inboth managements (Fig. 4a), with species richness apparently satur-ating at intermediate landscapes (agreeing again with the underlyingnon-linear theory by Concepción et al., 2008, 2012). Consequently,effectiveness of AES was invariant with landscape complexity (model ain Concepción et al., 2008). This means that, for ants, increasing groundherb cover and landscape heterogeneity had additive value untilreaching a saturation threshold, probably close to the landscape antspecies pool, at intermediate landscape complexity. This is largelyconsistent with previous studies of ground-dwelling arthropods in olivegroves (Paredes et al., 2013; Gkisakis et al., 2014, 2015; da Silva et al.,2017; Carpio et al., 2018) which found that both cover crops andlandscape diversity favor arthropod diversity and/or abundance.
Overall, studies on the effects of local agricultural management andlandscape heterogeneity on arthropod richness in woody crops showdisparate results. While some found larger effects of local managementcompared to the ones of landscape heterogeneity (e.g. Froidevaux et al.,2017, in vineyards), congruent with the relatively low dispersal abilityof this group, others found an opposite pattern in the same agroeco-system (Isaia et al., 2006).
As expected, herb species richness in our olive groves was higher inextensive than in intensive management and tended to increase withlandscape complexity in both types of management. Both effects werenot interdependent but additive. In contrast to the previous organisms,there was not a saturating point, meaning that increased landscapeheterogeneity still would favor herb species richness by supportingmore plant niches. Many studies have shown that plant biodiversity(herbaceous flora among them) is not only affected by local manage-ment but also by landscape heterogeneity (Roschewitz et al., 2005;Rundlöf et al., 2010; Gabriel et al., 2010; Otto et al., 2012), whichsuggests that AES should consider the management of fields and land-scape together for herb biodiversity enhancement.
4.2. Farm size effects: the scale of management matters
Several studies have shown that the scale at which agri-environ-mental schemes are practiced matters to biodiversity (e.g. Rundlöfet al., 2008 for organic agriculture) and that farm and field size mayaffect biodiversity through configurational heterogeneity effects(Fahrig et al., 2011, 2015). Farm size effects on biodiversity and on theeffectiveness of extensification of ground herb cover management torecover biodiversity were not particularly remarkable in olive groves(frequently marginally significant), but some effects did exist on multi-diversity and tended to be different for birds and ants. While effec-tiveness of extensification to recover bird biodiversity was higher inlarge farms (although only in complex landscapes), ant biodiversityrecovery by extensification was favored in small ones. This probablyreflects different scales of perception of heterogeneity of management(presumably fine-grained in ants and coarser in birds), linked to thedifferent mobility and dispersal ability of these groups (Concepciónet al., 2008, 2012; Froidevaux et al., 2017; Reynolds et al., 2018). Inparticular, Fahrig et al. (2015) showed that reduction of field sizes canconsistently enhance species richness of many groups of species, in-cluding insects, spiders, birds and plants. This agrees with our results onmulti-diversity and ants, what may reflect the easier access of organ-isms to well-vegetated (semi-natural) field boundary habitats as fieldsize decreases (see Fahrig et al., 2015; Merckx et al., 2009). Design ofappropriate field configuration is an emergent field for crop diversifi-cation as an AES in oversimplified landscapes (Fahrig et al., 2015). Herewe suggest that field size is relevant for conservation in olive grovelandscapes and that multi-diversity and some groups of organismsbenefit from small field sizes.
4.3. Assessing the best predictor of multi-diversity
Because different groups of organisms are expected to be inter-dependent and it is the overall biodiversity which confers multi-func-tionality to the ecosystems (Maestre et al., 2012; Allan et al., 2015;Birkhofer et al., 2018) we have emphasized here multi-diversity. In-terestingly, we found that the relationship between multi-diversity andagricultural intensification and landscape simplification was closer tothe expectancy of LMB postulates than it was for each group of or-ganisms. In this regard, although the effectiveness of extensification ofherb cover management to recover overall biodiversity depended onlandscape complexity (as predicted by the intermediate landscape-complexity hypothesis), this was not consistent among taxonomicgroups. Only birds reflected the pattern of maximum effectiveness ofAES at intermediate landscapes found for multi-diversity (although itwas not significantly different from simple landscapes). Few studies
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have assessed the intermediate-landscape complexity hypothesis inwoody croplands. In vineyards, Froidevaux et al. (2017) failed to cor-roborate it with bats and arachnids. Likewise, only birds seem to mirrorthe pattern of multi-diversity regarding the dominance of beta-diversityhypothesis, showing that beta diversity between productive and un-productive zones of the olive farm decreases with landscape simplifi-cation (see also Santana et al., 2017). Moreover, the correlation be-tween bird richness and multi-diversity was higher than the onebetween ants or herbs richness and multi-diversity. Finally, bird speciesrichness correlated positively with herb species richness in the pro-ductive areas, while ants were not correlated with herbs or birds. Thismay be because many ants require bare-ground for nesting in olivegroves and, in any case, they show species-specific responses to soilmanagement treatments and ground cover (Redolfi et al., 1999; Camposet al., 2011) which may obscure this relationship. In fact, Campos et al.(2011) did not find effects of ground cover treatments (maintained orremoved at the end of the growing season) on ant biodiversity withinorganic olive groves. It is likely that our result could vary if workingwith other arthropods more directly linked to the ground vegetationcover. For example, Carpio et al. (2018) detected a positive effect ofcover crops on arthropod diversity in olive groves. Alternatively, antsmight have higher time-lagged responses than birds (a more mobilegroup) to the shifts provoked by agricultural intensification or landconversion. Such delay has been shown for carabids in relation to thepast management in field margins and to landscape context (Alignierand Aviron, 2017). If so, the abundances of herbs and ants could be innon-equilibrium, hampering the detection of correlation. In any case,our results suggest that birds are good indicators of the combined im-pact of landscape simplification and intensification of agriculturalpractices on overall biodiversity, and good indicators to assess the ef-fectiveness of AES in olive groves.
4.4. AES implementation and biodiversity conservation in olive grovelandscapes
We have shown that the combined action of landscape simplifica-tion towards olive-grove dominated landscapes and the systematic re-moval of the ground herb cover has resulted on average in the loss ofone out of four species per group of organisms (1/ 5 species in the caseof birds, 1/3 of species in the case of ants and herbs) compared to olivefarms in complex landscapes with extensive management where herbcover is maintained throughout most of the year. Other woody cropstypically cultivated in biodiversity hotspots also suffer a decrease intheir capacity to retain biodiversity under intensive agriculture andsimplified landscapes with substantial removal of the native forests(Roubik, 2000; Pak et al., 2015, in coffee plantations; Isaia et al., 2006;Assandri et al., 2015; Froidevaux et al., 2017, in vineyards; Mendes-Oliveira et al., 2017, in oil palm plantations). In fact, the biodiversitylosses described in our study are even higher than those described inother woody croplands (Winter et al., 2018, for vineyards; Philpottet al., 2008, for coffee plantations). Therefore, intensively managedolive groves in simple landscapes suffer a quite significant loss of bio-diversity that occurs consistently in very different groups of organisms.This is an important point since the application in Spain of the currentCAP assumed the premise that woody croplands (such as olive culti-vation) inherently possess some ‘green’ nature, and relaxed the re-quirements to achieve the subsidy by environmental conditionality andgreening practices in this culture. Our results alert about the clear in-adequacy of such policy, at least in terms of biodiversity conservation.
Our results further indicate that similar extensification practicesmay render disparate results in different landscape contexts(Tscharntke et al., 2005; Concepción et al., 2012); hence, AES shouldnot be applied identically in simple, intermediate and complex olivelandscapes. Again, this strongly contradicts how AES are being im-plemented under the subsidy policy of the CAP in the olive cultivation.For instance, maintenance of herb cover between tree rows during most
of the year is recommended and subsidized as a general AES in the olivegroves. It is more frequently practiced in low productivity olive orch-ards in complex landscapes where, beyond other important advantagesagainst soil erosion, its effect on biodiversity recovery is relativelyminor compared to other olive grove landscapes where maintenance ofherb cover is still poorly extended. Similarly, organic farming is moreextended in low productivity olive groves within complex landscapes,while it is infrequent in highly-productive olive groves at simple andintermediate landscapes where it would likely enhance biodiversitymore efficiently. Several meta-analytical reviews focusing on the ef-fectiveness of AES to recover biodiversity and ecosystem services inEurope and North America have shown the need to reformulate AES toachieve their objectives (Kleijn et al., 2006; Batáry et al., 2011, 2015;Scheper et al., 2013). Our results add to that view and show the need toreformulate the subsidy to AES also in stable arboreal croplands.
4.5. General recommendations for enhancing biodiversity in olive groveslandscapes
The main results of this study ascertain some fundamental postu-lates of the LMB framework (Tscharntke et al., 2005, 2012) and shedlight on the way to manage olive grove landscapes and on AES topreserve/recover biodiversity. Provided our main results with multi-diversity, the general recommendations to enhance overall biodiversityin olive groves are: (i) Maintenance of a ground herb cover during mostof the year should be included in cross-compliance to receive agri-cultural subsidies, since extensive management of herbs increased thelocal species richness of all groups examined (see also Winter et al.,2018 in vineyards). (ii) Diversification of the landscape by protectingexisting natural elements and restoring them (“green infrastructures”)in unproductive areas between and within olive plantations. Thegreatest efforts of native vegetation (grassland, scrublands, woodlandsand forest) restoration should be carried out in olive groves located inextremely simple landscapes. This heterogeneity will foster the positiveeffect of herb cover maintenance on biodiversity. The unproductivezones (margins, roadsides, banks of rivers and streams, hedges, etc.) arepropitious to increase landscape heterogeneity in olive groves withoutreducing productivity. (iii) Maintenance and enrichment of groundherb cover should be a priority for olive groves located in landscapes ofintermediate complexity, where the maximum biodiversity recoverydue to a well-developed herb cover is achieved.
Birds, ground-dwelling ants and herbs responded differently tochanges in local management and to the complexity of the landscape.Hence, implementation of a multi-scale approach is recommended tobenefit a wide range of species (e.g., Dainese et al., 2015 in arablelands; Froidevaux et al., 2017, for Mediterranean vineyards; Reynoldset al., 2018 in agricultural mosaics). Moreover, specific actions for eachgroup will boost biodiversity and the effectiveness of AES. Some in-stances are diversification of foraging substrates and installation ofgreen infrastructures, nest and perching sites for birds, and diversifi-cation of soil management and seed sowings within olive farms for antsand herbs.
An increasing body of studies is shedding light to the potential ofolive grove landscapes as refuge and reservoir of biodiversity in theMediterranean region (see references in the Introduction).Unfortunately, olive grove agriculture is changing quickly in a worri-some trend, from traditional arboreal croplands with a hundred ofcentenary trees per hectare to intensive and super-intensive olivegroves with thousands of olives per hectare conforming hedges of quickplanting rotation (10–20 years) that require increased amount of inputs(water and agro-chemicals). It will be important to evaluate in the fu-ture the consequences of such changes for biodiversity and ecosystemservices at landscape and regional level and translate these con-sequences to the future CAP and AES application in the olive cultiva-tion. In the meantime, the take-home message of this study is thatadequate olive agricultural practices and reasonable landscape
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transformation policies will become crucial to harbor in the futuremuch of the Mediterranean biodiversity in landscapes of this millenaryculture.
Declaration of interest
None.
Acknowledgements
We thank Francisco Camacho for his collaboration in the field andlab work. Ana Belén Robles, Julián Fuentes, Gabriel Blanca, MiguelCueto and Esther Giménez helped us with herb species determination.We also thank to all the owners and farmers of the olive groves forallowing and facilitating our work. This study was funded by the LIFEproject OLIVARES VIVOS (LIFE14 NAT/ES/1001094) of the EuropeanCommission, the project CGL2015-68963-C2-1-R of the Ministerio deEconomía y Competitividad (MINECO, Spain Government) and FEDER.
Appendix A. Supplementary data
Supplementary material related to this article can be found, in theonline version, at doi:https://doi.org/10.1016/j.agee.2019.03.007.
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