Integrated models, scenarios and dynamics of climate, land use and common birds

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<ul><li><p>Climatic ChangeDOI 10.1007/s10584-014-1202-4</p><p>Integrated models, scenarios and dynamics of climate,land use and common birds</p><p>Jean-Sauveur Ay Raja Chakir Luc Doyen Frederic Jiguet Paul Leadley</p><p>Received: 14 May 2013 / Accepted: 26 June 2014 Springer Science+Business Media Dordrecht 2014</p><p>Abstract Reconciling food, fiber and energy productionwith biodiversity conservation is among the greatest chal-lenges of the century, especially in the face of climatechange. Model-based scenarios linking climate, land useand biodiversity can be exceptionally useful tools for deci-sion support in this context. We present a modeling frame-work that links climate projections, private land use deci-sions including farming, forest and urban uses and theabundances of common birds as an indicator of biodiver-sity. Our major innovation is to simultaneously integratethe direct impacts of climate change and land use on bio-diversity as well as indirect impacts mediated by climatechange effects on land use, all at very fine spatial resolu-tion. In addition, our framework can be used to evaluateincentive-based conservation policies in terms of land useand biodiversity over several decades. The results for ourcase study in France indicate that the projected effects of</p><p>Electronic supplementary material The online version of thisarticle (doi:10.1007/s10584-014-1202-4) contains supplementarymaterial, which is available to authorized users.</p><p>J. -S. Ay () L. Doyen F. JiguetCNRS, MNHN, UMR 7204 CESCO 55 rue Buffon,75005 Paris Francee-mail:</p><p>J. -S. Ay R. ChakirINRA, AgroParisTech, UMR 210 Economie Publique16 rue Claude Bernard, 75005 Paris France</p><p>L. DoyenGREThA, University Bordeaux IV, Avenue Leon Duguit,33600 Pessac France</p><p>P. LeadleyUniversity. Paris-Sud Laboratoire ESE UMR 8079CNRS UPS AgroParisTech, 91405 Orsay France</p><p>climate change dominate the effects of land use on birdabundances. As a conservation policy, implementing a spa-tially uniform payment for pastures has a positive effect inrelatively few locations and only on the least vulnerable birdspecies.</p><p>Keywords Integrated models Land use Incentivepolicy Common birds</p><p>1 Introduction</p><p>Climate and land use changes are considered to be two of themain drivers of past and future variations in terrestrial bio-diversity (Millennium Ecosystem Assessment 2005; Pereiraet al. 2010; Willis and MacDonald G 2011). For medium-term projections (ca. 40 yrs into the future) these two driverscan be treated very differently in terms of scenarios andpossibilities of intervention for biological conservation pol-icy (de Chazal and Rounsevell 2009; Wintle et al. 2011).Global warming depends on international commitments toreduce greenhouse gas emissions, and much of the climatechange projected over the next four decades is already com-mitted due to long lag times socio-economic drivers andin the Earth system (IPCC 2013). By contrast, Land UseChanges (LUC) are potentially under much greater controlof national and local decision makers concerning impacts onbiodiversity over the next few decades (Schroter et al. 2005;Verburg et al. 2008 but see Radeloff et al. 2012).</p><p>However, present and future land uses are influencedby climate change, and this is rarely accounted for whenexploring the interactive effects of climate change andland use on biodiversity (de Chazal and Rounsevell 2009).Local opportunities and constraints appear when climatechanges, leading to adaptation in the use of land resources</p><p></p></li><li><p>Climatic Change</p><p>(Jeltsch et al. 2011; Bradley et al. 2012). Moreover, mod-els foresee that future climate change will result northwardshifts of maize area in the United States, or rice area inChina (Tubiello et al. 2002; Xiong et al. 2009). Conse-quently, effective and efficient conservation policy has to bebased on the direct climate effect on species and the indirecteffects induced by human adaptations, strategies and pub-lic policies (Hannah et al. 2002; Berrang-Ford et al. 2011;Johnston et al. 2013). This paper presents an integratedbio-economic framework to explore the interactions amongclimate change, land use and biodiversity. This frameworkis structured in three modeling blocks: Species DistributionModels (SDM) of bird abundances and distributions, econo-metric models of LUC and Ricardian models of returns fromland in response to climate change. This integrated struc-ture is then used to simulate climate change effects on futureland uses and bird distributions from the present to 2053based on climate and economic projections, and an exampleof spatially uniform conservation policy.</p><p>Firstly, in the SDM, the abundances of commonbird species are related to local environmental condi-tions (Furness and Greenwood 1993; Gregory et al.2005; Renwick et al. 2012). SDMs assume that habitatand climate requirements can be deduced from currentdistributions, and that future abundance and distributionscan then be extrapolated using projections of future cli-mate and habitat changes (Peterson et al. 2011). SDMfor this study are developed using avian data from theFrench Breeding Bird Survey (FBBS), a standardized mon-itoring scheme in which skilled volunteer ornithologistsidentify breeding birds by song or visual contact everySpring (Jiguet et al. 2012). Observations indicate that birdpopulations are decreasing for pasture habitat specialists(Devictor et al. 2008) and are shifting up in altitude andtowards the north as a result of recent climate warm-ing (Jiguet et al. 2010). Secondly, the econometric LUCmodel fits the private land use decisions as functions ofeconomic returns (Lubowski et al. 2008; Nelson et al. 2008;Radeloff et al. 2012). This model is based on a analysis ofobserved land use data from the TERUTI land use survey(France, 19932003). TERUTI data have already been usedfor econometric LUC models but not for the whole Franceat the fine level of spatial resolution used in this study(Chakir and Parent 2009; Chakir and Le Gallo 2013). Theeconometric model is then used in a step-by-step scenarioanalysis to isolate and illustrate the impacts of the indi-vidual drivers of bird species abundance and distribution.Finally, the Ricardian model uses observed co-variations ofland prices and climate to infer the potential future conse-quences of climate change on the economic returns fromland (Mendelsohn et al. 1994; Mendelsohn and Dinar 2009).This approach is developed at the scale of France on thebasis of land prices from the statistical services of French</p><p>Ministry of Agriculture and regionalized climate data(Deque 2007; Boe et al. 2009).</p><p>This paper addresses three main questions:</p><p>(i) What is the effect of climate change on common birdabundances, assuming either constant or economi-cally driven land use changes?</p><p>(ii) Does climate-induced land use change mitigate oramplify the direct effects of climate change on com-mon birds abundances?</p><p>(iii) What is the effect on LUC and common birdabundances of a uniform conservation payment tolandowners order to promote pastures?</p><p>First, our model projects a significant negative impactof climate change on bird abundances by mid-century. Thiseffect is strong relative to the effect of projected LUC.Locally, climate change is projected to result in a greaterelevation shift than northern shift in the distribution of birds.Second, climate-induced LUC is foreseen to amplify thenegative direct effects of climate change on birds. Thisis not the case everywhere, with some locations, particu-larly in southern France that are projected to benefit fromclimate-induced LUC. Third, we find that spatially uni-form payments of 200 euro.ha1 to promote pastures onlyslightly counteract the negative effects of climate change.We foresee that these relatively high payments will have apositive effect in relatively few locations and only on theleast vulnerable species.</p><p>2 Models</p><p>2.1 Species Distribution Models</p><p>Bird abundance and distributions are modeled with an SDMthat accounts for the potential impact of climate and habi-tat (Pearson and Dawson 2003). For a general descriptionof the method, we note tqs the abundance of species s inthe FBBS sampling square q at the time t and we assumethe following relationship between the outcome and itspredictors:</p><p>log(qst</p><p>) = s(cqt , hqt , xq, zq</p><p>) + s t, (1)</p><p>where the s(), s = 1, . . . , S are spline-basedsmoothing functions with an endogenous structure as iscommon for Generalized Additive Models (GAM, Hastieand Tibshirani 1990; Wood 2006). The smoothing func-tions have to be estimated, as the scalars s that capturethe linear growth 20032009 for each species s (see OnlineResources 1.1 for more details about avian data). cqt standsfor the two principal axes at location q and time t ofa Principal Component Analysis of the climatic variables</p></li><li><p>Climatic Change</p><p>matrix. The Online Resources Figure 1 shows the relation-ships between the climate variables and these 2 principalaxes, which account for 87 % of the total variance. hqtis the vector of habitat variables including a fragmentationindex, xq represents a vector of topographic variables (alsofrom a PCA of topographic variables reported in OnlineResources Figure 1) while zq is the spatial location of thecenter of gravity of each FBBS square. Including thesespatial coordinates in the smoothed functions allows usto separate the unobserved contextual effects (i.e., inter-species competition, spillovers from anthropogenic pertur-bations) from the direct topographic, climatic and habitateffects. Because bird abundances are over-dispersed posi-tive integers, they are modeled as a distribution from thenegative binomial family. The function gam() from theR package mgcv 1.7 was used to estimate such mod-els (Wood 2006). Because the impacts of climate changeon species distributions have been shown to vary depend-ing on choice of modeling technique (Buisson et al. 2010;Garcia et al. 2012) and of spatial structure (Dormann et al.2007), we have estimated other SDMs based on alternativeassumptions. We also fitted negative binomial mixed mod-els without including geographical coordinates (with the Rpackage glmmADMB, see Online Resources Table 5) andzero-inflated hurdle models with and without geographicalcoordinates (with the package pscl, see Online ResourcesTable 6). From the time dimension, Online ResourcesFigure 10 presents the predictions from 3 scenarios rely-ing on 4 different SDMs. From the space dimension, the 15Pearson correlation coefficients between the projections arecomprise between 0.50 and 0.98, with more than the halfgreater than 0.8 (Online Resources Figure 11). Includinggeographical coordinates increases the goodness-of-fit buthas a relative limited impact on abundance variations withinscenarios, we focus only on the results from the negativebinomial GAMs here for the sake of clarity.</p><p>2.2 Econometric model of Land Use Changes</p><p>We have reduced land use types to five (L = 5) mutuallyexclusive categories: annual crops, perennial crops, pas-tures, forests and urban areas (see Online Resources 1.2).Landowners are assumed to choose LUC in order to max-imize their utility1 and these choices are assumed to beindependent for each parcel. With this latter simplifyingassumption, each parcel is associated with a distinct deci-sion process. In particular, a stylized landowner i chooses</p><p>1 Rationality is not a necessary condition, as Train 2009 (Chap. 2, p.14)explains: The derivation assures that the model is consistent with util-ity maximization; it does not preclude the model from being consistentwith other forms of behavior. The models can also be seen as simplydescribing the relation of explanatory variables to the outcome of achoice, without reference to exactly how the choice is made.</p><p>the land use type it on a parcel if this provides the highestutility over all possible uses:</p><p>it = arg max</p><p>{uit</p><p>}. (2)</p><p>This formulation for utility is forward-looking andaccounts for the possibility of multi-year land use such asperennial crops, forest or urban. Utility is typically assumedto be the expected one-period net returns that are the out-come of a dynamic optimization problem (Plantinga 1996;Lubowski et al. 2008). We exploit this result here by assum-ing a parametric but nevertheless flexible structure betweenthe expected returns and utility. At t , for each land use( = 1, . . . , L) and for each sampled plot (i = 1, . . . , I ),we assume:</p><p>uit = + rit1 + cit2 + xi3 + rit (cit + xi)4 (3)+hit1 + it .</p><p>Where rit is the vector of net returns in t for each ofthe possible land uses on parcel i. These rent variablesare only available at the scale of the Small AgriculturalRegion (SAR, see Online Resources Table 4 for a synthe-sis of the spatial units used to match the data). As such,they are crossed with climate cit and constant biophysi-cal variables xi (elevation, slope and land quality) to allowparcel-level deviations from the aggregate effects. Conver-sion costs between uses are taken into account by includingL 1 dummy variables representing the previous land useof a parcel i: hit1. So, the vector estimates the costs tochange to land use . Each vector of coefficients to estimate[; ; ] is unique for each land use category . Thismeans that expected economic returns, climate, biophysi-cal variables and conversion costs could have heterogeneouseffects on the utility, depending on the land use.</p><p>Because all the sources of landowners utility cannot beobserved, an error term it is included in (4). McFadden(1974) identifies three criteria for using a multinomial logitmodel: independence, homoscedasticity and extreme valuedistribution (i.e., Gumbel). Assuming these criteria are met,one can show that the probabilities have simple closedforms, which correspond to the logit transformation of thedeterministic part of the utility function (uit uit it ).The probability that a parcel i is in use at the period t is:</p><p>pit = exp(uit )k exp(uikt )</p><p>= f(rit , cit , xi, hit1</p><p>). (4)</p><p>The estimation was performed using nnet 7.3 on R.The unobserved factors are assumed to be uncorrelated overalternatives and periods, as well as having a constant vari-ance. These assumptions, used to provide a convenient formfor the choice probability, were found to be not restrictive(homoscedasticity cannot be rejected by a score test, p-value= 0.283). Moreover, these hypotheses are associatedwith the classical restriction of Independence of Irrelevant</p></li><li><p>Climatic Change</p><p>Alternatives (IIA) for which Hausman-McFadden speci-fication tests are performed, with mixed evidence. Theindependence is not rejected for three uses: pasture, peren-nial crop and urban (p-values are respectively 0.001, 0.005and 0.036) but rejected for annual crop and forest at 5 %. Inthe land use econometric literature, use of nested multino-mial logit is found not to change the results (Lubowski et al.2008).</p><p>2.3 Models of economic returns</p><p>In the Ricardian model, the price of land is used to computethe expected net returns from land uses. Land is consi...</p></li></ul>


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