European Farmland Bird Distribution Explainedby Remotely Sensed Phenological Indices

Eva Ivits & Graeme Buchanan &

Linda Olsvig-Whittaker & Michael Cherlet

Received: 8 June 2010 /Accepted: 26 January 2011# Springer Science+Business Media B.V. 2011

Abstract Birds are important components of biodiversityconservation since they are capable of indicating changes inthe general status of wildlife and of the countryside. ThePan-European Common Bird Monitoring Scheme(PECBM) has been launched by the BirdLife Partnershipin Europe, where the European Bird Census Council hasbeen collecting data from 20 independent breeding birdsurvey programs across Europe over the last 25 years.These data show dramatic declines in European farmlandbirds. We suggest that seasonal characteristics of vegetationcover derived from high temporal resolution remote sensingimages could facilitate the monitoring the suitability offarmland bird habitats, and that these indicators may be abetter choice for monitoring than climate data. We usedredundancy analysis to link the PECBM data of theestimated number of farmland birds in Europe to a set ofphenological and climatic indicators and to the biogeo-graphic regions of Europe. Variance partitioning was usedto account for the variation explained by the phenological

and climate variables and by the area of the environmentalstrata individually, to define the pure effect of the variables,and to extract the total explained variance. The analysisrevealed high statistical significance (p<0.001) of thecorrelations between species and environment. Phenologi-cal indices explained 38% of the variance in communitycomposition of the 23 farmland bird species, whereasclimate explained 30% of the variance. After partitioningthe other variables as covariables, the pure effect ofphenology, climate, and environmental strata were 16%,8%, and 16%, respectively. Based on the probability results,we suggest that phenological indicators derived fromremote sensing may supply better indicators for continentalscale biodiversity studies than climate only. In addition,these indicators are cost and time effective, are oncontinuous scale, and are readily repeatable on a largespatial coverage while supplying standardized results.

Keywords Phenology . Climate . Remote sensing .

Farmland birds . Redundancy analysis

1 Introduction

Most management decisions concerning the conservation ofspecies are made at the landscape or regional scale.Therefore, it is essential to identify indicators that determinespecies distributions and abundances at this level. At largespatial scales, species richness increases with environmentalenergy availability [26, 52]. The typical explanation for thispattern is that high-energy availability increases the numberof individuals that can be supported, allowing species tomaintain larger populations that have a reduced extinctionrisk [15]. Environmental energy available to consumers on alarge spatial scale has so far been calculated by two main

E. Ivits (*) :M. CherletJoint Research Centre, Institute for Environmentand Sustainability,TP 460, Via E. Fermi 1,21020 Ispra, Varese, Italye-mail: eva.ivits-wasser@jrc.ec.europa.eu

G. BuchananRSPB,2 Lochside View, Edinburgh Park,EH12 9DH Edinburgh, UK

L. Olsvig-WhittakerScience and Conservation Division,Israel Nature and Parks Authority,3 Am Ve Olamo Street, Givat Shaul,95463 Jerusalem, Israel

Environ Model AssessDOI 10.1007/s10666-011-9251-9

methods [16, 41]. One method addresses solar energymetrics such as temperature and ultraviolet radiation on thebasis that these either increase mutation rate leading to morespecies evolving in high energy areas (“evolutionary rateshypothesis”) or create states where higher temperatureenables endotherms to switch from keeping warm to growthand reproduction (“thermoregulatory load hypothesis”). Theother method approximates productive energy metrics thatrecord the amount of net primary productivity (NPP)available for consumers to turn into biomass. According tothe “more individuals hypothesis,” areas with high plantproductivity may be able to maintain larger populations thatreduce their extinction risk [61].

At large spatial scale NPP is easily approximated withthe satellite-derived Normalized Difference VegetationIndex (NDVI) by calculating the time integral over ayear of the index [5, 22, 27, 35]. The relationship betweenbiological diversity and such productivity measures assurface vegetation NDVI, NPP, and gross primary pro-ductivity (GPP) has been the subject of previous studies(among others [36, 48]). Oindo and Skidmore [48] forinstance found evidence for the link of the within-regionvariability of NDVI and the heterogeneity of habitats andshowed a positive relationship to species richness ofmammals and plants. These authors found that higheryearly average NDVI was correlated with lower speciesrichness of mammals and plants. Conversely, Bailey et al.[2] found positive relationships between maximum NDVIand the number of functional guilds of birds, speciesrichness of neotropical migrant birds, and species richnessof butterflies. Phillips et al. [51] correlated NDVI, NPP,and GPP measures with native landbird species richnessderived from the North American Breeding Bird surveyand found that the productivity measures explainedsubstantially more variation then the NDVI.

Differing patterns arise from the studies of biologicaldiversity and productivity measures, but results becomeeven more variable when temperature is considered as well.Evans et al. [16], for example, found that species richnessof the breeding avifauna of Britain correlated better withtemperatures than with the NDVI. Kaspari et al. [33] foundsimilar results when investigating ant assemblages. Haw-kins et al. [26] found it true at high northern latitudes butobserved the opposite at other areas. Evans et al. [16] alsostated that it was unclear why temperature was a betterpredictor of avian species richness than NDVI, while notingthe two measures were correlated. One possible explanationgiven was that in regions that are dominated by intensiveagriculture such as Britain, NDVI would be an imperfectmeasure of the amount of plant productivity available forconsumers. The equivocal results in studies correlatingproductivity measures and temperatures to species richnessmake further research essential. It is, therefore, very

important to investigate these results further and todetermine whether the better correlation of birds totemperature than to NDVI also holds when more elaboratedindices are used that measure vegetation productivity,biomass dynamics, and phenology.

Birds provide early warnings of environmental prob-lems, as they are capable to show changes in the broad stateof the wildlife and of the countryside [19]. They are thebest known and documented major taxonomic group,especially in terms of the sizes and trends of populationsand distributions. Agriculture-related faunal diversity isunder relatively high pressure as roughly two thirds of thethreatened and vulnerable bird species in Europe occur onfarmlands [60]. An assessment in 1994 estimated that 25%of all European bird species were undergoing substantialpopulation declines [47]. The Pan-European Common BirdMonitoring Scheme (PECBM) was launched by the Bird-Life Partnership in Europe and the European Bird CensusCouncil (EBCC). Their data have been collected from 20independent breeding bird survey programs across Europeover the last 25 years. The PECBM study confirmed thatcommon farmland birds are in decline throughout Europe,with the cumulative populations of all 33 species offarmland birds suffering a decline of 44% between 1980and 2005 [3]. Chamberlain et al. [10] showed evidence ofcausal links between farmland bird abundance and agricul-ture by illustrating how both have changed through time.Gates and Donald [20] addressed the long-term risks oflocal extinction among farmland species and showed howlosses have been more likely in less suitable habitats andleast likely in traditional lowland arable locations. Theonset of farmland bird population declines has beendescribed [55], and this change has been linked to changingagricultural management [10]. However, a spatially con-tinuous, standardized, readily repeatable, and low-costindicator system for common farmland bird distributionsis missing.

Birds surveys are done in many cases by thousands ofvolunteer field ornithologists. Although the value of thiswork is undoubted, remotely sensed indicators maycontribute considerably to the monitoring work. In thepresent study, we identify correlations that suggest the useof phenological indices derived from time series ofremotely sensed images, which may be useful in monitor-ing the distribution of European farmland birds. We usephenological metrics such as the annual NPP, annual startof season (SOS), season length (SL) plus a combination ofderived permanent and cyclic components of vegetationcover. The decomposition of the NDVI time series curveinto phenological metrics may yield additional informationon various aspects of vegetation and land cover functionalcomposition in relation to dynamics of ecosystem func-tioning and land use. We suggest, similar to Evans et al.

E. Ivits et al.

[16], that in regions dominated by intensive agriculturesuch as Europe, NDVI may be an imperfect measure of theamount of plant productivity available for consumers andthat phenological metrics might better describe ecosystemdynamics than NDVI alone. Evans et al. [16] and Kaspari etal. [33] state that species data correlate closer to temper-atures than to productivity measures derived from theNDVI. We show by means of linear ordination and byvariance partitioning that the decomposition of the NDVItime series curve into its phenological component providesa better indicator of European farmland bird distributionthan climate and might better evaluate the species–energyrelationship. Furthermore, remote sensing methods comple-ment traditional ground-based phenological observations(recording phenophases and first occurrences of individualspecies) and overcome the restrictions related to limitedgeographical ranges [28, 31]. Remote sensing-derivedphenological indicators might also find a broader usage toindicate other taxa of biodiversity conservation and eco-system state importance.

2 Methods

2.1 Test Site and Data

The study area covered the European countries within thegeographic limits of the environmental stratification ofEurope ([44]; Fig. 1). This Stratification was produced byselecting 20 of the most relevant available environmentalvariables based on experience from literature research.Principal components analysis (e.g., [32, 56, 57]) was usedto explain 88% of the variation into three dimensions. Thefirst principal component mean values of the classificationvariables were used to aggregate the strata into environ-mental strata and to provide a basis for consistentnomenclature (see [44] for the analysis). The environmentalstratification consists of 84 strata aggregated into 13environmental zones with a 1-km spatial resolution. Forthis study, the 84 strata were used as observation units inorder to account for most of the climatic and geographicalvariation in Europe.

Bird data came from the EBCC Atlas of EuropeanBreeding Birds (also referred to as European OrnithologicalAtlas or ‘EOA’) which was published in 1997 [24]. Theatlas integrates 25 years of sampling in more than 40countries, producing data on breeding certainty andestimates of the number of breeding pairs per square for495 species. These estimates are given in seven classes withexponentially increasing ranges on a 50 by 50-km grid(Fig. 2). For each geographic point, the species sampled,the number of breeding pairs coded according to theestimate class, and the geographic coordinates are reported.

Out of the reported species the common European farmlandbirds (23 species) specified by Gregory et al. [23] wereselected for this study.

In order to assess the link between breeding birds andphenological indicators, SPOT VEGETATION time seriesdata were acquired in dekads format (10 days maximumcomposite) for 10 years covering the period from 1999 to2008 for the extent of the environmental strata. TheVEGETATION Programme allows daily monitoring ofterrestrial vegetation cover at regional through globalscale on a 1-km spatial resolution. These data havesuccessfully been applied in several regional and globalscale studies dealing with vegetation biophysical proper-ties, land cover assessment, monitoring of forest ecosys-tems, desertification and land degradation, and theassessment of NPP.

Analyses that exclude climatic variables when exam-ining species–area relationships across large extents arelikely to confound the effects of area and climate andthus cannot be interpreted unambiguously [26]. The mostcomprehensive high-resolution climate dataset availablefor Europe is the CRU_TS1.2 [45], developed by theClimatic Research Unit (CRU) at the University of EastAnglia. It has a 10′×10′ resolution (approximately 16×16 km) and contains monthly values for five variablesduring the period 1900–2000. These data are reported withthe environmental strata averaged within the spatial unitsof the data (for an overview of processing see [44]). Weselected the monthly minimum and maximum temper-atures and the monthly precipitation values for the presentstudy.

2.2 Satellite Data Processing

Following the method of Reed et al. [53], the SPOTVEGETATION data were smoothed using a 5-intervalrunning median filter in order to adjust for eventual cloudcontamination of the pixels. Consecutively, the originaltime series were overlaid with smoothed forward andbackward lagged curves, which were determined from theoriginal dataset itself by means of a forward and backwardcomputed moving average (MA) algorithm (Fig. 3). Mov-ing averages are used to smooth out short-term fluctuations,thus highlighting longer term trends or cycles. A simplemoving average (SMA) is the unweighted mean of theprevious n data points. For example, a 10-day simplemoving average of phenological values is the mean of theprevious 10 days’ phenological values. If those values arep, p−1... p−9 then the formula is:

SMA ¼ pþ p�1 þ :::þ p�9

10

Remotely Sensed Phenology and Farmland Birds Distribution

abbreviation species name common name

AlaArv Alauda arvensis Skylark

AthNoc Athene noctua Little Owl

CarCan Carduelis cannabina Linnet

CarCar Carduelis carduelis Goldfinch

ColPal Columba palumbus Woodpigeon

CorCor Corvus corone spp Carrion Crow

CorMon Corvus monedula Jackdaw

CotCot Coturnix coturnix Quail

EmbCit Emberiza citrinella Yellow Hammer

EmbSch Emberiza schoeniclus Reed Bunting

FalSub Falco subbuteo Hobbz

FalTin Falco tinnunculus Common Kestrel

HirRus Hirundo rustica Swallow

LanCol Lanius collurio Red-backed Shrike

MilCal Miliaria calandra Corn Bunting

MotFla Motacilla flava Yellow Wagtail

PasMon Passer montanus Tree Sparrow

PicPic Pica pica Magpie

SaxRub Saxicola rubetra Stonechat

StrTur Streptopelia turtur Turtle Dove

StuVul Sturnus vulgaris Starling

SylCom Sylvia communis Whitethroat

VanVan Vanellus vanellus Lapwing

(a)

(b)

E. Ivits et al.

When calculating successive values, a new value comesinto the sum and an old value drops out, meaning a fullsummation each time is unnecessary:

SMAtoday ¼ SMAyesterday � p�nþ1

nþ pþ1

n

A moving average lags behind the latest data point,simply from the nature of its smoothing. The period of thelag selected depends on the kind of movement considered,such as short, intermediate, or long-term.

In the method of Reed et al. [53], the number of decadesduring the non-growing period was used to calculate thelag. We argue that over a whole continent with very diverseclimatic regions, ecosystems, land use, and land cover, thisnumber cannot be generally applied. Therefore, we com-puted the lag as two standard deviations from the bary-center of the area under the NDVI curve averaged over allthe years. Thus, the time series dynamics of each pixel

under the different regions are incorporated in the deriva-tion of the phenological measures. The cross points of theoriginal time series and the MA curves were used to definetwo points on the original time series for each year thatrepresent the start and the end of the vegetative growingseason days (SOS and EOS, respectively) with thecorresponding NDVI values. Additionally, the days andthe values of the yearly first and last absolute minima days(fMIN and lMIN, respectively) were defined. Variations inthe calculated SOS and fMIN days are, in first instance, dueto fluctuating meteorological conditions. However, longervegetation growing seasons or more systematic changecould be also attributed to land cover change indicatingland cover functional composition in relation to dynamicsof ecosystem functioning and land use.

NPP was approximated by the area under the NDVIcurve (total biomass, TB), delimited by the calculatedyearly SOS and EOS of the vegetation growing season.This area can be disaggregated into a fraction that is moresignificantly related to a permanent vegetative cover and acyclic fraction. This, in turn, represents the yearly cycle ofgreen matter production. The approximation of the amount

ESTIMATE LAT LON Env. ZoneSPECIESAlauda arvensisColumba palumbusEmberiza citrinellaEmberiza schoeniclusFalco subbuteoFalco tinnunculusHirundo rusticaLanius collurioMotacilla flava sspCorvus corone sspPica picaSaxicola rubetraStreptopelia turturSturnus vulgarisVanellus vanellus

234322334334022

64.22831464.22831464.22831464.22831464.22831464.22831464.22831464.22831464.22831464.22831464.22831464.22831464.22831464.22831464.228314

30.47732930.47732930.47732930.47732930.47732930.47732930.47732930.47732930.47732930.47732930.47732930.47732930.47732930.47732930.477329

BOR3BOR3BOR3BOR3BOR3BOR3BOR3BOR3BOR3BOR3BOR3BOR3BOR3BOR3BOR3

SPECIES ESTIMATE LAT LON Env. ZoneAlauda arvensisAthene noctuaCarduelis cannabinaCarduelis carduelisCorvus monedulaCoturnix coturnixEmberiza citrinellaEmberiza schoeniclusFalco subbuteoFalco tinnunculusHirundo rusticaLanius collurioMiliaria calandraMotacilla flava sspCorvus corone sspPasser montanusPica picaStreptopelia turturSturnus vulgarisSylvia communisVanellus vanellus

422032263143234442403

44.9065454944.9065454944.9065454944.9065454944.9065454944.9065454944.9065454944.9065454944.9065454944.9065454944.9065454944.9065454944.9065454944.9065454944.9065454944.9065454944.9065454944.9065454944.9065454944.9065454944.90654549

29.2181738929.2181738929.2181738929.2181738929.2181738929.2181738929.2181738929.2181738929.2181738929.2181738929.2181738929.2181738929.2181738929.2181738929.2181738929.2181738929.2181738929.2181738929.2181738929.2181738929.21817389

PAN3PAN3PAN3PAN3PAN3PAN3PAN3PAN3PAN3PAN3PAN3PAN3PAN3PAN3PAN3PAN3PAN3PAN3PAN3PAN3PAN3

CODE ESTIMATE 1234567

1-910-99100-999 1000-999910 000-99 999100 000 - 999 999 >= 1 000 000

Fig. 2 Sampling design of the EBCC Atlas data. For each geographic location the sampled species, the breeding pairs, the geographic location,and the environmental strata (limiting lines on the map) is reported

Fig. 1 Extent of the study. a Environmental classification of Europe(reproduced from [44]). b Farmland bird breeding pair classesaggregated in the environmental strata within the extent of the study

�

Remotely Sensed Phenology and Farmland Birds Distribution

of vegetation permanently present on the area wascalculated as the integral under the SOS and EOS points(permanent integral, PI) while the seasonal vegetationfollowing the yearly growing cycle was the integral abovethese points (SI). The ratio of the values of this permanentand cyclic fraction relates to functional land cover types orcombination of them. This hypothetically sheds light ontransitions in land use, which, combined with ancillaryinformation, can (to some extent) be attributed to shifts inhuman activity. The SL was also derived and calculated asthe number of days between the corresponding SOS andEOS points on the NDVI curve. The season length reflectsthe length of time of cyclic photosynthetic activity andhence represents the fundamental growth cycle of thevegetation cover, which mostly is a mixed signal of theindividual cycles of the various vegetation components.Its variation may be attributed to interannual meteorolog-ical fluctuation but can be also triggered by compositionalchanges, e.g., in case of land use change. The integraldelimited by the first and last minimas (MI, not shown) aswell as PI and SI were divided by TB to account for theirproportion in the total biomass.

The yearly values of the phenological indices weresummarized in temporal mean values. These statisticalvariables were consecutively aggregated within the strata ofthe environmental stratification of Europe (84 classes)calculating the average value of all pixels belonging to onestrata. Climatic variables such as the mean monthly minimumand maximum temperatures and mean monthly precipitationwere also acquired and aggregated within the environmentalstrata as reported in Metzger et al. [44]. In order to adjust tothe spatial extent of the phenological indices and biophysicalvariables, the estimated number of breeding bird pairs wasalso summed within the strata. We decided on aggregatingall the datasets within the limits of the environmentalclassification to present the results on a spatial scale that isreadily comprehensive, and that provides a scale where allthe input data can be assessed. This is necessary since the

satellite data, the climate data, and the avian observations allwere extracted from different scale of observation.

2.3 Statistical Data Analysis

Redundancy analysis (RDA, [57]) was used for the statisticalanalyses to reveal species–environment relationships. RDAwas chosen because detrended correspondence analysis(DCA) revealed that species expressed a linear response tothe environmental gradient (length of first DCA axis=1.798SD), and with linear response the variance explained by RDAis the same as in conventional multiple regressions, hencemore clearly interpretable [42]. The species data was log-transformed and centered before the analysis but notstandardized since abundances of the recorded species werein the same units [42]. Due to the aggregation of theestimated number of breeding pairs within each environmen-tal zone the species matrix was not zero inflated; therefore,we did not use the Hellinger transformation as suggested byLegendre and Gallagher [39] and Peres-Neto et al. [50].

Peres-Neto et al. [50] and Blanchet et al. [4] described anovel procedure for forward selection of explanatoryvariables in regression or canonical redundancy analysis inorder to overcome highly inflated type I errors (i.e., the rateof false positives) and the overestimation of the amount ofexplained variance. A two-step procedure was proposedstarting with a global test using all explanatory variables. Incase the global test is significant, one can proceed with theforward selection with two stopping criteria: (1) the usualalpha significance level and (2) the adjusted coefficient ofmultiple determination (Ra

2) calculated using all explanatoryvariables. The Ra

2 statistic corrects for the number ofexplanatory variables, and it provides an unbiased estimateof the real contributions of the independent variables to theexplanation of a response data [50].

It is generally not recommended to use a stepwise selectionprocedure in situations in which there are collinear variables[11, 18]. Instead, it is recommended to test for collinearity

1 SD 1 SD

bary center

1 SD 1 SD

MAMA

NDVI

Dec.Jul.Jan.decadestime in

bary center

Jan.

SL

time in decades

fMIN lMIN

Dec.

MAMA

SOS EOS

TB PI SI

SL

Jul.

Fig. 3 Schematic explanation of the phenological indices calculated from an NDVI 1 year time–series curve using MA

E. Ivits et al.

among the variables and remove the variables that are veryhighly collinear with other variable(s) in the explanatorydataset [4]. The variance inflation factor [46] often used toscreen collinearity only allows the identification of highly(but not totally) collinear variables [4]. As stated in Blanchetet al. [4], the fundamental problem lies in the forwardselection procedure itself because it inflates the Ra

2 statisticby capitalizing on chance variation. This can be the result oftwo factors: (1) the degree of collinearity among theexplanatory variables and (2) the number of predictorvariables [13]. With 30 phenological and 36 climaticvariables in the analysis it was expected that some ormany variables will be highly collinear. Therefore (and inorder to maintain the control over the selection ofexplanatory variables), prior to the RDA with forwardselection we have orthogonalized the phenological andclimatic explanatory datasets by means of principalcomponent factor analysis. The number of factorsselected was based on the eigenvalues of the components,and we kept those components in the analysis for whichthe eigenvalue was greater than 1.

Three components with an eigenvalue >1 were selected forboth the phenological and the climatic variables with 98% and95% of the total variance explained respectively, indicatinghigh collinearity between the environmental variables. Theselection of an additional factor would have only addedanother 1.1% and 1.8% to the explained variance in thephenological and climatic data respectively; therefore, wehave considered the three factor solution final. The threefactor solution gives a reduced set of explanatory variableswith three potential orthogonal variables for both thephenological and the climate variable matrix. With such alow number of explanatory variables, a forward selectionbecomes superfluous therefore we have diverted from themethod of Peres-Neto et al. [50] and Blanchet et al. [4].Initially, we selected all the variables with loadings >0.6 onthe individual factor components and considered them aspotential candidates for the RDA model. A variable withvery high loading on a factor component will be orthogonalthus uncorrelated to the other variables but will notnecessarily be a good predictor of the species assemblage.Therefore, we ran a partial RDA on the farmland bird datawith the logarithm of the area of the environmental strata(lA) as a covariate using all the phenological variables as theexplanatory matrix and another partial RDA using all theclimatic variables as explanatories reporting their conditionaleffects (alpha, significance, F ratio). The significance and Fvalue of each variable in structuring the bird community datawas determined with the Monte Carlo permutation test with999 permutations. To produce a reduced set of ecologicallymeaningful and orthogonal explanatory data, we haveselected from each factor group the variable that fulfilledthe three criteria: (1) highest loading, (2) highest signifi-

cance, and (3) highest F value. This procedure resulted inthree phenological and three climatic variables, respectively.

Spatial structure in ecological studies is considered afunctional parameter of ecological models [37, 38, 40].Spatial structure arises on multiple scales and in addition tomatching sampling strategies the statistical method appliedshould also match the scale of the investigated ecologicalprocess. For the quantification of spatial patterns over a widerange of scales, Borcard and Legendre [7] developedPrincipal Coordinates of Neighbour Matrices (PCNM). Thiswas a new methodology for studying spatial variation. It wasshown in Borcard et al. [9] and in Peres-Neto et al. [50] thatthe efficiency of the variation partitioning method will begreatly improved by replacing the traditional polynomialfunction by the spatial variables resulting from the PCNMmethod. The PCNM method has obvious advantages inspatial models where the structure of the spatial data plays adecisive role. However, we have simplified the spatialstructure of our study by aggregating species and explana-tory data matrices within the extent of the environmentalzones (Fig. 1), and we used the log area to account for thedifferent spatial extent within which the species andenvironmental data were aggregated. Consequently, we arguethat at this stage of our explanatory analysis the applicationof the sophisticated PCNM method would be superfluousand we kept the log area of the environmental zone ascovariate in the analysis. Spatial autocorrelation in the datamay invalidate the assumption of independent errors distort-ing classical tests of association and rendering significancetests misleading. For the present study, the goal was notmodel building for the purpose of forecasting but simply todescribe the association within the environmental and thespecies data and to place this onto a European gradient.Therefore, test statistics were not adjusted by fitting, e.g., aspatial covariance matrix to the data. We argue that theaggregation of species, climate, and phenological data withinthe environmental zones, which are defined by summarizing20 of the most relevant environmental variables createsspatial observation units independent enough to draw validconclusions on the species–environmental relationship.

We used variance partitioning as presented by Borcard etal. [8] and Borcard and Legendre [6] mainly as anexploratory tool to develop a hypotheses about the pheno-logical and climatic determinants of the distribution offarmland birds. In variance partitioning, variation in theresponse data is decomposed into a number of primarycomponents through the use of covariables whose influenceis partialed out of the analysis. This technique allows theexplanatory power of the variables to be quantified individ-ually as well as selection of the covariation terms (i.e., toestablish how much of the variance in the response variablecan be accounted for by different combinations of variables)and the proportion of the variance remaining unexplained

Remotely Sensed Phenology and Farmland Birds Distribution

[34]. The interaction of two variables is defined by using thethird variable as a covariable, and the interaction is the extentto which variation in, e.g., phenology covaries with thevariation in, e.g., climate. Three direct RDAs were run,constraining the species data on the phenological variables,climate variables, and the area of strata, respectively. Further,the three groups of environmental variables were used inturn as explanatory or covariables in nine partial RDAs. Foreach run, the sum of all canonical eigenvalues was recorded.The proportion of the total variation that this sum repre-sented was calculated by multiplication by 100 to obtain thepercentage of explained variation.

With three subsets of environmental data, the total variationof farmland birds was partitioned into eight components,including the covariance terms. As explained in, e.g., Totlandand Nylehn [59] or Anderson and Gribble [1], the totalvariation explained by all variables and the variation thatremained unexplained was calculated. Furthermore, the purelyphenology-driven variation and the purely climate- and area-driven variations were derived and also their interaction termswere defined. Results were visualized in the form of RDAtriplots displaying the species, the environmental strata, andthe explanatory variables. Gilbert and Bennett [21] argued thatvariance partitioning might fail to correctly model spatial andenvironmental components of variation and in some casesmight produce biased estimates of the relative importance ofthe components. Therefore, we use variance partitioning onlyas an approach for understanding the relative influence ofphenological and climatic variables driving farmland birdcommunity assembly and supported variance partitioning withresults from partial RDAs and from the interpretation of RDAtriplots. Furthermore, we note that the adjusted coefficient ofmultiple determination [50] would be the correct choice tocompare RDA models with different number of predictorsand sample sizes. However, in our analysis, the phenologicaland climate models had the same number of predictors and

sample size; therefore, we chose to compare the modelsdirectly. All RDA analyses were run using the FORTRANprogram CANOCO version 3.10 [56].

3 Results

3.1 Selected Phenological and Climate Variables

Table 1 presents the three climatic and three phenologicalvariables that significantly (p<0.05) structured farmland birdcommunity composition together with the statistics of thepartial RDA models. The strata’s average minimum Apriltemperature was the most important climatic variable (p<0.001, F=26), explaining 15% variation in farmland birdassemblages shown by the conditional effect (the effect ofthe variable adjusted to the effect of the other variables in theRDA model). The mean January precipitation and the meanMay precipitation of the environmental strata were weakerbut nonetheless significant (p<0.001) predictors explaining5% and 3% of the variation. Mean season length was themost important phenological variable (p<0.001, F=31) withthe highest conditional effect and its explanatory value wasalso higher than that of the climate variables. This variablealone explained 26% of the variance in the farmland birddata. The portion of the 8-year maximum biomass from thetotal biomass (MI/TB) was a strong and significant predictor(p<0.001, F=21) explaining 12% of the total variation,whereas the first minimum day was added somewhat lessexplanatory to the RDA model (Λ=0.01).

3.2 Variance Partitioning

Table 2 illustrates the eigenvalues, model significance, and Fratio of the single RDA models used for calculating the three-component variance partitioning together with the variables

Table 1 Phenological and climatic variables significantly structuring farmland bird community composition

Partial RDA with climatic parameters and log area as covariables Λ F ratio PVariable names and codes

AprminT 0.15 25.8 0.001

JanP 0.05 10.9 0.001

MayP 0.03 8.15 0.001

Partial RDA with phenological parameters and log area as covariables Λ F ratio PVariables names and code

Mean SL 0.26 31.2 0.001

Mean MI/TB ratio 0.12 21.5 0.001

Mean fMINd 0.01 3.43 0.025

Variance explained (Λ) is calculated in addition to all other variables in the model (conditional effect). Significance calculated with Monte Carlotest running 999 permutations

AprminT minimum April temperature, JanP January mean precipitation, MayP May mean precipitation

E. Ivits et al.

and covariables used in the analyses. Table 3 illustrates theprocedures to acquire the three-component variance partition-ing. With the combination of the three environmentalvariables phenology, climate, and area of the environmentalzones, 67% of the variance in the farmland bird compositionpattern could be explained (Table 3), thus only 33% of thevariance remained unexplained. The RDA runs suggestedthat farmland bird data had a greater statistical associationwith phenology than climatic variables, with the phenologicalvariables explaining 38% variation in the species data,whereas the climatic variables explained 33% (Table 2).The area of the environmental strata explained another 21%of the variation, reflecting the diversity of the environmentalzones (e.g., larger areas will host a larger number ofindividuals and greater species richness).

Variance partitioning indicated the unique varianceexplained by the phenological indices and also by the areawas about 16% (Table 2, steps 6 and 12, respectively). Thepure climatic effect on the European farmland birds wasonly ca. 8% (step 9) after partialing out the effect of theother variables. The covariance between phenology andclimate variables was 22% (Table 3, P+C) indicating that22% of the explanatory value of one variable group couldbe replaced by the other variable group. Since the uniqueexplanatory value of phenology was higher than the

explanatory value of climate, we conclude that phenolog-ical variables might be a better choice for modeling thedistribution of farmland birds. There was only 2%interaction between phenology and the area of the environ-mental zones (Table 3, P+lA), whereas the interaction effectbetween climate and area amounted to 5%.

3.3 Ordination Constraining Farmland Birdson Phenological and Climatic Variables

Table 4 shows the statistics of the partial RDA modelsconstraining the farmland bird species on the significantphenological and on the significant climatic variables,respectively with the area of the environmental zones ascovariates. Monte Carlo permutation tests of the modelsshowed that both the first axes and the overall models werestatistically significant (p<0.001). The RDA model con-straining the species matrix on the phenological variablesexplained 38% of variance in the distribution of the 23 birdspecies (sum of all canonical eigenvalues). The highimportance of phenology in explaining farmland birdabundances is also shown in the variance partitioningresults in Tables 2 and 3. The eigenvalues of the threecanonical axes were 0.251, 0.120, and 0.008 respectively(the latter not shown), suggesting that the first two captured

Variance unique to: Calculation Λ (%)

P Λ[6]×100 15.4%

C Λ[9]×100 7.9%

lA Λ[12]×100 15.7%

P+C (Λ[5]−Λ[6])×100 22.5%

P+lA (Λ[4]−Λ[6])×100 2.3%

C+lA (Λ[7]−Λ[9])×100 4.9%

Total explained variation (Ω) Λ[1]+Λ[7]+Λ[12] or Λ[2]+Λ[4]+Λ[12] or Λ[3]+Λ[5]+Λ[9] 66.8%

Unexplained variation 100−Ω 33.2%

Table 3 Computation ofexplanatory power of eachcomponent and each covariationterm in the variance partitioningmodel

P phenology C climate, lAlogArea

Steps Constraining variable Covariable Trace Significance F

[1] P None 0.383 0.001 14.9

[2] C None 0.335 0.001 12.1

[3] lA None 0.210 0.002 19.7

[4] P C 0.177 0.001 8.3

[5] P lA 0.379 0.001 21.8

[6] P C+lA 0.154 0.001 10.5

[7] C P 0.128 0.001 6.1

[8] C lA 0.304 0.001 14.8

[9] C P+lA 0.079 0.001 5.4

[10] lA P 0.206 0.001 35.6

[11] lA C 0.180 0.001 26.3

[12] lA P+C 0.157 0.001 32.3

Table 2 RDA models used forvariance partitioning with modelstatistics

P phenology, C climaticvariables, lA log area

Remotely Sensed Phenology and Farmland Birds Distribution

the majority of the variation in the species–environmentrelationship. The first two RDA axes explained 98% ofvariance in the fitted responses and had high species–environment correlation indicating strong relation betweenfarmland bird species and phenology (Table 4). The secondRDA model constraining the bird data on the climatevariables explained 30% variation in the species data, 8%less than the phenological indices. The first two axesexplained over 97% of variance in the fitted responses,indicating that further RDA axes would not indicate muchin the species data. Indeed, while the eigenvalues of the

first two axes were 0.172 and 0.122, respectively, theeigenvalue of the third axes was only 0.010 (not shown)thus only explained 1% variation. The second RDA axis ofthe climate ordination had moderate species–environmentalcorrelations, much behind the performance of the pheno-logical indices.

Figures 4 and 5 display the RDA triplots of theordinations constraining the bird data on the phenologicalindicators and on the climatic variables, respectively. Thegradients extracted from the phenological variables resultedin a clear distribution of the environmental zones, whereas

Table 4 RDA with farmland bird community composition in Europe constrained on the significant phenological indices and on the significantclimatic variables

P axis 1 P axis 2 C axis 1 C axis 2

Eigenvalues 0.251 0.120 0.172 0.122

Species–environment correlations 0.756 0.750 0.937 0.533

Cumulative percentage of variance of species data 31.8% 47.0% 21.8% 37.2%

Cumulative percentage of variance of species environment relation 66.2% 97.9% 56.6% 96.6%

Sum of all canonical eigenvalues 37.9% 30.4%

F ratio/significance 21.8/0.001 14.8/0.001

P phenology, C climate

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Fig. 4 RDA triplot of species(crosses), environmental zones(icons) and the significantphenological indicators (arrows)

E. Ivits et al.

the gradients extracted from the climatic variables con-structed a strongly clustered (thus not readily interpretable)pattern. This is also reflected in the higher eigenvalue,higher species–environment correlation, and higher explan-atory power of the first axis of the RDA extracted from thephenological indicators. The first RDA axis of thephenological ordination positively correlated with theMediterranean and the Lusitanian regions and negativelywith the Continental, Alpine South, Atlantic Central, andPannonic regions (Fig. 1, location of the regions). Thesecond RDA axis revealed a northern–southern gradientfrom the Alpine North, boreal and nemoral regions throughthe Continental areas to the Pannonic lower Danube Plainsof the former Yugoslavia, Bulgaria, and Romania. All thespecies showed a negative association to the first RDA axisof the phenological ordination, whereas the second RDAaxis clearly separated farmland bird species expressingnegative and positive association to the axes. The first RDAaxis extracted from the climatic variables structured thepreviously seen northern–southern gradient, whereas thesecond RDA axis constructed an environmental gradient ofthe Mediterranean regions. However, this pattern was lessapparent than the gradients of the phenological ordination

and the association of the farmland bird species to theenvironmental zones was less clear compared to theordination with the phenological variables.

The 10-year mean SL was positively correlated with allthe 23 bird species, as shown by the same position on thefirst RDA axis of the species and the arrow of the variable(Fig. 4). However, this correlation was strongest to thespecies located in the nemoral regions of Sweden, Finland,western Estonia, Latvia, Lithuania, Belarus, and Poland andin the boreal regions of Finland, Russia, Latvia, and Estonia(Fig. 1, location of the regions). This index showed a strongnegative correlation of the first RDA axis and especially tothe northern Mediterranean regions in Spain and Greeceand to the southern Mediterranean regions in Greece, Spain,and Sicily in Italy. The portion of the 10-year total biomassfrom the maximum biomass (MI/TB) indicates the amountof seasonally changing vegetation over the area. The 10-year mean of this index correlated positively only to thespecies breeding in the northern latitudes and negatively tofarmland bird species in the southern latitudes. This indexalso showed a strong correlation to the boreal regions in thesouthern Finnish Lapland and central Finland, northwesternRussia, Sweden, and Norway. The day of the first NDVI

0.6-1.0

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Fig. 5 RDA triplot of species(crosses), environmental zones(icons), and the significantclimatic indicators (arrows)

Remotely Sensed Phenology and Farmland Birds Distribution

Minimum (fMINd) correlated positively to species breedingin the southern latitudes and negatively to species breedingin the north indicating that the earlier the vegetation seasonstarts, the greater the number of species found in northernlatitudes.

The amount of precipitation in May correlated positivelyto most of the farmland bird species and expressed somecorrelation to the nemoral regions in Estonia, Latvia, andLithuania and to the nemoral regions of southern Swedenand southern Finland, northern Belarus, and westernRussia. The minimum temperature in April showed apositive correlation to species in the southern latitudes anda negative correlation to species of the northern regions.This shows that where the April temperature is higher,species breeding in southern latitudes are more abundantand where the April temperatures are lower species ofnorthern latitudes are found, thus this relationship is ratherindicative to the climatic zones than to the biology of thebird species. The third climatic index, the mean Januaryprecipitation had a weaker explanatory value shown by theshorter length of the arrow. This index showed positivecorrelation to some environmental zones, namely theMediterranean north regions of Spain, Italy, Greece,Bulgaria, and Turkey; the Continental region of theCarpathian foothills and Transilvanian uplands; and theMediterranean mountain regions of Spain, the MassifCentral, and the Apennines. This relationship perhaps isbetter considered as relating to the higher winter and lowersummer precipitation of the Mediterranean regions than tothe biology of farmland birds.

4 Summary and Discussion

Phenological variables performed better than climate inexplaining the variance of bird species distribution as theyaccounted for 38% of the variance in the avian data,whereas climate accounted for 8% less. The first two RDAaxes of the ordination with phenological indices had highereigenvalues and higher species–environmental correlationsthan the RDA axes derived after ordination with the climatedata. Variance partitioning showed that after partialing outthe effect of climatic variables and the area of theenvironmental strata phenology explained 16% of thevariation of farmland bird species while the pure climateeffect was only around 8%. Furthermore, the covariancebetween phenology and biophysical variables was only22% indicating that only some explanatory power of remoteobserved phenology can be replaced by climate. Remotelysensed phenological indices are readily available on acontinuous spatial and temporal scale and on high spatialresolution. Meteorological observations on the other handare supplied on coarser spatial resolutions and need to

undergo interpolation methods in order to deliver a spatiallycontinuous cover reducing the accuracy of the dataset.Moreover, unlike remote sensing indices climate data donot inform on habitats and a comprehensive dataset has tobe compiled from different measurement sources.

Our findings contradict the results of Evans et al. [16] andKaspari et al. [33] who state that species data correlate closerto temperatures than to productivity measures derived fromthe NDVI. It is important to note that we studied farmlandbirds over the whole European continent, whereas Evans etal. [16] observed the avifauna in Britain. Furthermore, it isessential to emphasize that we partitioned the NDVI timeseries curve and by this de-convolution of the original NDVItime series into phenological metrics, we provided additionalinformation on various aspects of vegetation and land coverin relation to dynamics of ecosystem functioning. We state,similar to Evans et al. [16], that in regions dominated byintensive agriculture such as Europe, NDVI might imper-fectly measure the amount of plant productivity available forconsumers, and we suggest that phenological metrics mightbetter describe ecosystem dynamics than NDVI only.

Our results somewhat confound a study of global avianforest assemblages, which found that species richness andabundance all increased with increasing NPP [49]. In ourstudy, MI/TB (the approximation of the NPP during thevegetation growing season) only correlated with farmlandbird species in the boreal and nemoral regions. In theabove-mentioned study, however, NPP was estimated basedon mean temperature and precipitation values, whereas ourdata are derived from observed vegetation productivityvalues. Our proposition that seasonally varying NPP has ahigh indicative value is somewhat supported by theobservation of Evans et al. [17] that breeding residentspecies richness responds more to annual NDVI, while thenumber of migrant species is associated more closely withsummer NDVI. Similarly, Hawkins [25] demonstrated thathigher summer plant productivity in northern parts ofeastern North America can explain the region’s aviandiversity gradient. Also, Hurlbert and Haskell [29] demon-strated that an estimate of productivity during the breedingseason is more appropriate than an annual estimate forbirds, which migrate in response to seasonal variation inavailable energy. These findings are further supported hereby the positive correlation of the Season Length index tothe bird species breeding in the nemoral regions and borealregions. This shows that the actual length of the seasonmight be more important for species breeding in northernEuropean habitats; as the end of the season approaches,they migrate to southern or African territories to winter.

Over the last century, birds have exhibited a variety ofresponses that could be considered to be consistent withwarming trends in mean surface air temperatures, includingearlier breeding dates [43]. Tracking future changes in both

E. Ivits et al.

climate and response is essential. We found evidence for theindicator value of vegetation start of season phenologicalmeasures (fMINd), confirming the value of de-convolutingthe NDVI time–series curve into its phenological measures.The fMINd correlated negatively to farmland bird breedingin the northern regions, indicating that the earlier thevegetation season starts the more species will be found inthe area. The somewhat contradicting positive correlation ofthis index to some species of the southern latitudesindicates a strongly varying spatial structure in vegetationphenology and a quite diverse response of farmland birds tothis structure. This is in line with Marra et al. [43] statingthat global warming patterns vary spatially and temporallywith particular reference to migratory birds whose annualranges extend to temperate areas where elevated springtemperature may have advanced spring phenology. Whileearlier spring arrival is evident across many studies onbirds’ migration, both advanced as well as delayed autumndepartures have been reported [58]. This increases theimportance of phenological indicators as they might help tobetter explain variations in species migratory status as aresponse to global warming patterns.

Linkages between bird migration and climate have beenreported for many years, with migratory birds potentiallyadjusting to match variations in ambient temperatures [43].Both temperature and vegetation phenology contribute tothe migration, distribution, and biology of birds in aninterrelating manner. The northward progression of leafemergence (“green-wave”, [54]) is closely related totemperature affecting habitats and prey development rates.General increase in spring temperatures have been linked tomigratory birds, whereas autumn temperatures in Europehave not increased accordingly and only small changes inthe timing for autumn migration was observed for species[14]. Our results support these findings as only the Apriltemperatures proved to be significant predictors of theabundance and distribution pattern of farmland birds in thesouthern latitudes. However, we found that the explanatoryvalue of temperature was inferior to vegetation phenology(conditional effect of Λ=0.15, F=25.8 for minimum Apriltemperature and Λ=0.26, F=31.2 for season length). On asimilar matter, Marra et al. [43] found evidence that theimpact of temperature on plant phenology is three timesgreater than on bird phenology as plants are synchronizedto local variability in ambient temperatures. Therefore, wesuggest that vegetation phenology and its spatial variabilitymight be a better and more sensitive indicator of birdabundances than climate.

Birds are valuable indicators for biodiversity conservationas they are capable to show changes in the broad state of thewildlife and of the countryside. We suggest that the linkbetween farmland bird species and phenology described herecould improve the assessment of farmlands, helping identify

high nature value in an objective manner. Biodiversity datacollection networks need a long-term commitment forsupporting the involved countries and international organiza-tions. There will be a premium on filed collected biodiversitydata, which will always be the backbone of any biodiversitymonitoring system. However, we suggest that phenologicalindices derived from remote sensing can support datacollection networks of farmland and possibly also otherspecies. Although remote sensing time–series tracking landsurface phenology is restricted in spatial resolution [30], thecomparability of different data sources and the replacementof frequent temporal gaps remains a challenge [31], andfurther research is needed to adequately assess start and endof season metrics [12]. We argue that phenology as assessedby remote sensing has a strong potential in ecological andmonitoring studies. Remotely sensed phenological indicesare time and cost-effective to derive when compared to insitu samples, offer high temporal resolution thus deliverrepeatable measures, are readily repeatable and available ona global coverage, and therefore offer standardized results.Through their indicator role, phenological indices might finda broader usage to indicate other taxa and more complexpatterns of ecosystem states.

Acknowledgement The authors express their great thanks to Wolf-gang Mehl from EC JRC, Ispra for his comments and programmingskills. Furthermore, we are most grateful to the EBCC, and forRichard Gregory for providing the data and allowing us to use it.

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Remotely Sensed Phenology and Farmland Birds Distribution