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This article appeared in a journal published by Elsevier. The attachedcopy is furnished to the author for internal non-commercial researchand education use, including for instruction at the authors institution
and sharing with colleagues.
Other uses, including reproduction and distribution, or selling orlicensing copies, or posting to personal, institutional or third party
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In most cases authors are permitted to post their version of thearticle (e.g. in Word or Tex form) to their personal website orinstitutional repository. Authors requiring further information
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Ecological Modelling
jo u r n al hom ep age : www.elsev ier .com/ locate /eco lmodel
Can phenological shifts compensate for adverse effects of climate change onbutterfly metapopulation viability?
Anouk Cormonta,b,∗, René Jochema, Agnieszka Malinowskaa,b, Jana Verbooma,Michiel F. WallisDeVriesc,d, Paul Opdama,b
a Alterra, Wageningen University & Research Centre, P.O. BOX 47, 6700 AA Wageningen, The Netherlandsb Land Use Planning Group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlandsc De Vlinderstichting/Dutch Butterfly Conservation, P.O. Box 506, 6700 AM Wageningen, The Netherlandsd Laboratory of Entomology, Wageningen University, P.O. Box 8031, 6700 EH Wageningen, The Netherlands
a r t i c l e i n f o
Article history:Received 25 March 2011Received in revised form 1 December 2011Accepted 5 December 2011
The interaction between climate change and habitat fragmentation has been presented as a deadlyanthropogenic cocktail. We cannot stop climate change, but it is within our circle of influence as ecolo-gists to suggest landscape adaptation. Detailed population models that take into account climate changeare considerably needed. We explore a detailed individual-based spatially explicit metapopulation modelof a univoltine butterfly species where all processes are affected by daily weather, using historical dailyweather data and future daily projections as input, in order to examine responses of a butterfly popu-lation in landscapes under various states of fragmentation and two climate change scenarios. This toolis used to investigate how landscapes could be adapted to compensate for possible negative impacts ofclimate change on population performance. We find that our model butterfly metapopulation was notonly able to escape adverse conditions in summer by phenological shifts, but even to benefit from climaticwarming. Varying either the amount of suitable habitat or patch size revealed a sharp threshold in pop-ulation viability. In this particular case, however, the threshold was not affected by climate change andclimate-dependent landscape adaptation was not required. The model presented here can be adapted forother species and applied to investigate scenarios for landscape adaptation.
Climatic models indicate that increasing atmospheric concen-trations of greenhouse gases will result in continued warming aswell as in increased changes in daily, seasonal, inter-annual, anddecadal weather variability in the next century (IPCC, 2001, 2007b).It is suggested that these changes will result in mostly adverseimpacts on biophysical systems (IPCC, 2007a). At a smaller spa-tial and temporal scale, however, climate change is expressed bydaily weather that affects populations of individual species. Often,these species are now restricted to isolated patches of habitatthat are located within inhospitable area (Hanski and Simberloff,1997). Populations can only survive in these habitat patches if the
∗ Corresponding author at: Alterra, Wageningen University & Research Centre,P.O. BOX 47, 6700 AA Wageningen, The Netherlands. Tel.: +31 317 485957;fax: +31 317 419000.
juxtaposition and sizes of the areas allow a metapopulation net-work structure (Opdam et al., 2003).
Climate change and habitat fragmentation have been presentedas amplifying forces (Opdam and Wascher, 2004; Travis, 2003;Warren et al., 2001). Warren et al. (2001) found that most of thebutterfly species considered in their research had not expandedtheir range, despite the warming climate, because habitat patcheswere too isolated to colonize. Travis (2003) concluded on the basisof a simple lattice model with a climate-driven shift in suitablehabitat that the interaction between climate change and habitatloss might be disastrous: during climate change, decreasing habitatavailability becomes critical sooner than habitat loss alone wouldsuggest. Similarly, species suffer more from climate change in afragmented habitat, because they are unable to keep pace withclimate change and patch occupancy quickly declines. If these inter-actions between climate change and habitat loss are indeed alwaysdisastrous, it is important to know which adaptation measures areeffective. Except for mitigating greenhouse gas emissions, chang-ing land use to improve habitat configuration would be a mainadaptation measure. Increasing sizes and number of habitat areas,connecting habitats, and improving habitat quality (e.g. by makinghabitats more heterogeneous) have been proposed to be effective
A. Cormont et al. / Ecological Modelling 227 (2012) 72– 81 73
adaptation measures (Vos et al., 2008). Increasing the size of habitatpatches can be assumed to be more cost-effective than increasingthe number of habitat patches due to the smaller edge effects (lowerperimeter area ratio).
The interaction between climate change and habitat frag-mentation has several dimensions. Especially increased weatherfluctuations have been shown to affect population dynamics(Grotan et al., 2009; Morris et al., 2008; Piessens et al., 2009).This leads to increased variability in vital demographic rates, espe-cially for metapopulations depending on small patches (Verboomet al., 2010). Moreover, climate change affects habitat quality,either positively or negatively. Thomas et al. (2001) found a sig-nificant broadening of the range of habitats used by Silver-spottedskipper, Hesperia comma L., spreading into north-facing hill slopehabitats that were previously climatically not suitable. However,WallisDeVries and Van Swaay (2006) showed that climatic warm-ing can lead to a reduction in available habitat due to microclimaticcooling in spring by advancing plant growth, which is particularlyunfavourable to thermophilous organisms, such as caterpillars;vegetation has a lower temperature threshold for growth thancaterpillars have for activity. At a much larger spatial scale, climaticwarming causes shifts in geographical distributions of species(Parmesan and Yohe, 2003; Root et al., 2003) and habitat frag-mentation may affect these range shifts (Anderson et al., 2009;Schippers et al., 2011; Vos et al., 2008). These large-scale expres-sions of climate change are the result of changes in populationprocesses at the local and regional scale. These climate-induceddemographic changes interact with each other and with landscapecharacteristics.
Considering the need for measures in the landscape, bet-ter understanding of the mechanistics behind the interactionbetween population dynamics, landscape characteristics and cli-mate change at the local scale is therefore required. However, theseinteractions have rarely been studied (but see, e.g. Zurell et al.,2009).
Empirical studies alone afford insufficient insight into complexinteractions and mechanisms. To control underlying processes,models provide tools to study relative impacts of components withmutual dependencies. However, most current models in this fieldonly predict large-scale shifts in species distributions (Akc akayaet al., 2006; Brook et al., 2009; Settele et al., 2008). These enve-lope models solely consider climate-driven changes in the quantityand location of suitable habitat and lack projections of complexdependencies between climate change, population dynamics, andlandscape characteristics (Zurell et al., 2009). To afford insight intothe combined impacts of various weather components and land-scape pattern indicators, we developed a novel spatially explicitpopulation model.
In this paper we present a detailed individual-based spatiallyexplicit metapopulation model of a butterfly species developed tostudy how this butterfly population performs under different sce-narios of climate change and habitat fragmentation. It uses dailyweather data as input. We have chosen to model a butterfly species,since butterflies are ectothermic and show a direct response toweather, have a fast turnover, and complete their life cycle on aspatial scale that is comparable to the scale of human land-useinterventions (e.g. construction of new industrial or residentialareas or abandonment of agricultural land). With model experi-ments, one can investigate how the landscape pattern could beadapted to compensate for possible negative impacts of climatechange on population performance by addressing the followingquestions:
- What is the effect of current and future weather circumstances,and various climate change scenarios on population dynamics inspace and time?
- What is the effect of landscape configuration, especially patcharea and habitat density on population viability under currentand future weather circumstances?
2. Methods
We applied an extended version of the model METAPHOR(Verboom, 1996), which is a spatially explicit, individual-basedmodel (programmed in C++) that simulates the dynamics of apopulation or metapopulation. METAPHOR was used in several the-oretical and applied studies (Reijnen et al., 1995; Schippers et al.,2009, 2011; Verboom et al., 2001; Vos et al., 2001). The model wasaltered by allowing time steps of 1 day, a stage structured popula-tion, and daily weather (past records or future projections) as input,as will be described in detail below following the ODD protocol(Grimm et al., 2006).
2.1. Purpose
We aimed to investigate the effect of current and future weathercircumstances, and various climate change scenarios on butterflypopulation dynamics in space and time. Moreover, we aimed tostudy the effect of landscape configuration (patch area and habitatdensity) on population viability under current and future weathercircumstances.
2.2. State variables and scales
Our population consists of an imaginary butterfly species rep-resenting a widespread species in the centre of its range that ismoderately mobile and, therefore, potentially affected by habitatfragmentation. Parameter settings were derived from real speciesdata (mostly Meadow brown Maniola jurtina) as much as possi-ble. Each individual has 4 or 5 phases: egg, caterpillar, pupa, forfemale an unfertilized and a fertilized adult phase, and for maleonly one adult phase. Individuals are characterized by the statevariables: identity number, age, sex, identity of the patch wherethe individual resides, phase, and weight (for caterpillar only).The butterfly species is univoltine and overwinters as half-growncaterpillar.
The experiments were carried out in computer-generated land-scapes of 5 km × 5 km, with suitable habitat patches that aresurrounded by inhospitable area. These dimensions are in pro-portion to the assumed network size. The left and right sidesand the top and bottom sides of the landscapes are mergedin a toroidal way (periodic boundary). In these landscapes, allpatches have equal quality, and the weather is equal for allpatches.
2.3. Process overview and scheduling
METAPHOR describes the spatial dynamics of a(meta)population in discrete time, and the time step used inthe model is 1 day. Each day, individuals have a chance to changephase (which is evoked by daily growth for caterpillars), to die(mortality), to reproduce (only fertilized female adults) and tomove (only adults). Processes development/phase transition,mortality, reproduction, and movement determine the magnitudeand structure of subpopulations and, thus, direct populationdynamics. The first event in a new day is development, or changeto the next phase. Next, reproduction occurs. Then, individuals canmove, and finally, mortality takes place. The order of the processesdevelopment/phase transition, reproduction, movement andmortality is rather trivial but it seems realistic that development
74 A. Cormont et al. / Ecological Modelling 227 (2012) 72– 81
happens first. We assume that the order does not affect theconclusions.
2.4. Design concepts
The individual’s performance and behaviour are entirely rep-resented by stochastic processes governed by empirical rules, andall affecting factors (mainly weather) are imposed. Thus, individu-als make no adaptive decisions. Individuals are assumed to knowtheir own age, sex, position, and phase so that they apply their spe-cific behaviour and performance. Considering interaction amongindividuals, the number of eggs produced per female depends onthe number of female adults in a patch (density dependence). Also,mating depends on the presence of both a male and an unfertil-ized female adult within a specified neighbourhood (radius). Themodel is stochastic for all processes, including both demographicand environmental (variable weather) stochasticity. Indicators forpopulation performance used are total adult numbers and patchoccupancy of the landscape, summarized per scenario and overall patches, and date of emergence. The occupancy, or the aver-age fraction of occupied patches during the period the populationwas extant, was calculated as an ecological measure of fragmenta-tion effects (Hanski, 1994). Average population viability in years1981–2009 (indicated as 1995), 2026–2054 (indicated as 2040),and 2072–2100 (indicated as 2085) were used to present results,as well as population viability continuous through time.
2.5. Initialization and input
A run starts on January 1 with all patches occupied with 1000caterpillars and continues for 161 years and, thus, 58,765 timesteps (no intercalary years). For the first 40 years of each run, theweather of the years 1960–1979, derived from the De Bilt mete-orological station (http://www.knmi.nl), located in the centre ofthe Netherlands, was used as input, followed by once more the1960–1979 weather. This resulted in 40 years of pre-climate changeweather that were used to burn-in the model, as the pattern ofoccupancy is presumed to be the result of a quasi-equilibriumbetween species, weather, and landscape characteristics. Subse-quently, the weather of the years 1980–2009, derived from theDe Bilt meteorological station, was used as input. From KNMI dataon future daily average temperatures and precipitation amounts(http://climexp.knmi.nl/Scenarios monthly/), we calculated futurevalues for weather variables affecting processes in the popula-tion for the years 2010–2100 (Table 1). We used KNMI scenariosW and W+, which implies for both scenarios an average globaltemperature rise of 2 ◦C from 1990 till 2050, and an increasedoccurrence of mild wet winters and warm dry summers for theW+ scenario (see http://www.knmi.nl/research/climate services/).The KNMI data on future daily average temperatures and precip-itation amounts are transformations of historical weather series(1976–2005). Those are available for various baseline years (everytenth year between 2020 and 2100). Hence, weather data for asingle day appears in three different, partly overlapping transfor-mation series. The three projections for weather of a single daycan be regarded as independent, because of the 10-year interval.We randomly picked years and their daily values from these series,before we supplied them to the model. In this way, we avoid thepersistent reoccurrence of extreme weather in consecutive years,as might arise from using the historical weather in their nativestate. For a detailed description of the transformation series, seehttp://www.knmi.nl/research/climate services/. In Fig. 1, we showstatistics for daily mean temperature and precipitation surplus forperiods 1981–2009, 2026–2054, and 2072–2100 for both the W andW+ scenario.
2.6. Processes and parameterization
In the processes, we implemented as many weather effects aswe found to be relevant from literature and our own field exper-iments (Cormont et al., 2011) (Table 2). Egg hatching depends ondaily average temperature (◦C), which is summed for the days theindividual is in its egg phase until a specified temperature thresh-old (see Table 3 for formulas and parameter values). When thesummed daily average temperature exceeds this threshold, theegg has a chance to hatch (cf. Salpiggidis et al., 2004). Egg mor-tality depends on air humidity (%); with a decreased air humidity,egg mortality increases (Warren, 1992). Caterpillar developmentdepends on individual growth in weight (10−4 g), which increaseswith daily average temperature when this temperature exceeds7 ◦C (temperature threshold for caterpillar activity, cf. Kingsolveret al., 2004). This increase in growth is limited when daily averagetemperatures are more often between 0 ◦C (temperature thresholdfor grass growth) and 7 ◦C than in former, cooler times (1960–1990).In this case, relatively fast grass growth leads to a shady and coolmicroclimate, limiting caterpillar growth (WallisDeVries and VanSwaay, 2006). When individual caterpillar weight exceeds a spec-ified threshold, the caterpillar has a chance to pupate (Jansen,unpublished work). Individual caterpillars lose weight when thedaily average temperature drops below 7 ◦C. Reduction in (limitedor unlimited) growth occurs in periods of drought, when the pre-cipitation surplus becomes negative. In this case, growth is reducedwith a specified factor. Weight loss of more than a third of themaximum individual caterpillar’s weight ever reached increasescaterpillar mortality. Pupa hatching depends on daily average tem-perature (◦C), which is summed for the days the individual is inits pupa phase until a specified temperature threshold. When thesummed daily average temperature exceeds this threshold, thereis a chance for adult emergence from the pupa (cf. Stevens, 2004).For adults, reproduction is density dependent; the number of eggsproduced per female depends on the number of female adults inthe patch. The number of eggs produced per female on a spe-cific day further depends on the age of the individual as fertilizedfemale (Brakefield, 1982). There is a combination of weather thatis unfavourable to adult individuals: the daily maximum tem-perature does not exceed 18 ◦C (butterflies will not fly/fly less),or the daily precipitation amount exceeds 3 mm and falls on aday with almost continuous rainfall (thus not in showers; butter-flies will not fly/fly less), or the precipitation surplus is negative(drought can lead to nectar shortage). One such day will limit but-terfly movement, will decrease the mating chance (equals “phase”change from unfertilized to fertilized female, also depending onpresence of male adult in neighbourhood), and will decrease repro-duction. Three consecutive days of these kinds of weather willincrease mortality. When weather is favourable, adult butterfliesare able to move in a random walk manner, taking a specified num-ber of steps per day (depending on temperature threshold) of aspecified length and tortuosity between the steps. Inside-patch but-terfly movement distance increases with radiation and decreaseswith temperature. Outside-patch movement distance and tortu-osity between steps of both inside- and outside-patch movementare not affected by weather (based on Cormont et al., 2011). Anoverview of all parameter values is given in the (Table 3). We per-formed sensitivity analyses for various parameters to study theeffect on (meta)population performance.
2.7. Simulation experiments
The computer-generated landscapes of 5 km × 5 km containedsuitable habitat amounts of 0.5%, 1%, and 2%, distributed overhabitat patches of 0.1, 0.2, 0.4, and 0.8 ha (Table 4) that aresurrounded by inhospitable area. The number of patches per
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Table 1Weather variables used in the model.
Abbreviation Explanation (units) Calculation for 2010–2100
TAVG Average daily temperature (◦C) –a
TSM7 Temperature sum (◦C) above 7 ◦C since 1 January Sum of temperatures (◦C) for days with TAVG > 7 ◦C since 1 JanuaryTSM0 Temperature sum (◦C) above 0 ◦C since 1 January Sum of temperatures (◦C) for days with TAVG > 0 ◦C since 1 JanuaryTMAX Maximum daily temperature (◦C) Per date: TAVG, summed with average difference TMAX − TAVG for that
date over period 1960–2008a
PDAY Daily precipitation amount (mm) –a
SHWR Daily precipitation falls in showers (1) or continuously (0) (–) Per day 50% chance for precipitation in showers, and 50% chance for daywith continuous rainfall, based on random number drawn. Chances forrain in showers increase to 100% when TMAX > 25 ◦C
HUMI Average daily air humidity (%) Linear function of TAVG, PDAY, EVAPb, and PSPL, based on measuredweather 1960–2009 (best GLM)a
RADI Average daily radiation (J/m2) Cosine function, with values for average and standard deviation per date,based on measured weather 1960–2008a
PSPL Precipitation surplus since 1 January (mm) Summed difference PDAY − EVAPb since 1 Januarya
a For the years 1960–2009, values were derived from the De Bilt meteorological station (http://www.knmi.nl).b EVAP = daily evapotranspiration (mm); calculation based on Makkink formula (Makkink, 1957).
simulation landscape varied from 11 to 1000 (Table 4). The patcheswere distributed randomly by a landscape generator, keeping aminimal distance between patch edges of 150 m. For each of the11 combinations of habitat amount and patch size, ten land-scapes were generated. We generated ten weather series from theKNMI data on future daily average temperatures and precipitationamounts. Hence, ten runs per climate scenario were conducted foreach landscape; thus in total 2200 runs were conducted.
3. Results
3.1. Effect of future weather on population viability
Generally, our results do not show much negative impacts ofclimate change on population viability. If populations are sustain-able and survive 160 years, average total adult numbers remainconstant over time for both W and W+ scenarios (Fig. 2; graphs atright and bottom). There are no significant differences between Wand W+ scenarios within time slots. In general, average occupancyof the landscape remains 1 (all patches occupied) constantly overtime (Fig. 3).
A phenological shift in the moment of phase change could resultin earlier pupation and butterfly emergence and, hence, a lesservulnerability to drought. For a landscape with 0.5% habitat andpatches of 0.8 ha, we investigated the extent to which this phe-nomenon occurred. Over a period of 130 years, the moment ofpupation shifted from mid-June to the beginning of May (W) oreven somewhat earlier (W+; Fig. 4). Populations under the W+scenario showed a slightly more accelerated phenological shiftcompared to populations under the W scenario.
The phenological shift in pupation can be caused by severalweather components. We varied model dependencies of thesecomponents to find out to what component(s) the model butterflyis most sensitive. We investigated the effect of drought on caterpil-lar development by increasing both the growth reduction factor andthe mortality rate during weight loss with 10% as well as with 50%.Avoiding limited caterpillar growth in the parameter settings leadsto an acceleration of caterpillar development. The acceleration isless pronounced after 2040 (year 100). This is caused by the fact thatafter 2040, caterpillar growth is less limited by grass growth; dailyaverage temperatures usually exceed 7 ◦C and caterpillar develop-ment is not hampered by a shady and cool microclimate.
We indicated the period when butterflies can be encounteredon average in Fig. 1. This figure illustrates that the species canescape from adverse summer conditions (drought, extreme rainfall,extreme hot weather) by advancing its phenology.
3.2. Effect of habitat amount and patch size under climate change
By increasing either amount of habitat or patch size, we observea sudden transition from unviable to viable populations, suggestinga sharp threshold in the physical conditions for population via-bility. In this particular case, average occupancies of landscapesincrease sharply from 0 (all patches empty; graphs at left and topof Fig. 2) to 1 (all patches occupied; graphs at right and bottom)while increasing patch size from 0.2 to 0.4 ha with an amount ofsuitable habitat of 0.5%, and while increasing patch size from 0.1 to0.2 ha with an amount of suitable habitat of 1 or 2%. There are nosignificant differences between W and W+ scenarios within timeslots. Under the same spatial conditions, average adult numbersshift sharply from 0 (or a decrease to 0 within at most the first70 simulation years) to a gradual increase over time for both Wand W+ scenarios (Fig. 3). Doubling habitat amounts or patch sizeon both sides of the threshold sorts similar effects on populationviability. In this particular case, the landscape with suitable habitatamounts of 0.5%, distributed over habitat patches of 0.4 ha would bethe financially most profitable landscape of the tested landscapes,with the smallest amount of habitat, which is still sustainable to thespecies.
4. Discussion
This paper describes a novel approach to understanding thecombined effects of habitat fragmentation and climate changeusing a detailed individual-based metapopulation model in combi-nation with detailed daily weather data. This model could be usedto assess landscape patterns and suggest adaptation options. Wecannot stop climate change, but it is within our circle of influenceas ecologists to suggest landscape adaptation. Climate change hastwo aspects important to nature: the global warming and grad-ual changes in precipitation patterns on the one hand, and theincrease in variability of the weather on the other hand. While theglobal warming is expected to have positive effects on many but-terfly species at the northern edge of their range (e.g. Settele et al.,2008), the increasing frequency of weather extremes is expectedto have adverse effects (Parmesan et al., 2000; Piessens et al.,2009; Verboom et al., 2010). Explorations with the model revealedthat under the current settings the combined effect of augmentedweather variability and climate warming resulted in improved con-ditions for the model butterfly species, illustrated by increasedpopulation sizes and habitat occupancy in a fragmented habitatpattern. Our findings indicate that potentially detrimental effectsof weather variability did not occur because of a phenological shiftin the moment of phase change of caterpillars and pupae. This effect
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76 A. Cormont et al. / Ecological Modelling 227 (2012) 72– 81
Fig. 1. Moving averages (30 days) of mean daily temperature (◦C in black) and precipitation surplus (mm in grey) and standard deviations (dashed lines). Weather variablesare calculated for time slots 1981–2009 (indicated as 1995), 2026–2054 (indicated as 2040), and 2072–2100 (indicated as 2085). Timing of average flight periods resultingfrom model runs is indicated in the graphs. aWeather variables for time slot 1981–2009 are calculated with historical weather data, which does not concern any climatescenario; hence, the graphs for 1995 are identical.
was found in the two climate change scenarios, which both implyan average global temperature rise of 2 ◦C from 1990 till 2050, andassume an increased occurrence of mild wet winters and warmdry summers for one of the scenarios (W+). The experiments alsosuggested a sharp viability threshold with a changing landscapepattern: landscape patterns appeared to be either sustainable withall patches occupied, or unsustainable. This all-or-nothing result is
due to the large local population sizes (hence, minor contributionof demographic stochasticity) and the fact that all patches wereof equal habitat size and quality, and faced equal weather condi-tions. In real landscape, not only does the weather vary in space(precipitation more than temperature) but also microclimatic het-erogeneity will occur, due to slope, vegetation, soil type and otherlocal characteristics.
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A. Cormont et al. / Ecological Modelling 227 (2012) 72– 81 77
Table 2Dependencies used in the model.
aPhase transition in this cell is from egg to caterpillar, etc., for other cells.1 Salpiggidis et al. (2004); 2 expert knowledge Dutch Butterfly Conservation; 3 Warren (1992); 4 WallisDeVries and Van Swaay (2006); 5 Jansen (unpublished work); 6Stevens (2004); 7 Brakefield (1982); 8 Cormont et al. (2011); weather variables are abbreviated—for abbreviation of weather variables see Table 1; only unshaded cells arerelevant in butterfly life-cycle.
Varying habitat amount and patch size had a similar impacton population performance. Such a sharp threshold in response tolandscape pattern change was found before (e.g. Bascompte andSole, 1996; Lande, 1987; Levins, 1970) in studies using patch occu-pancy or spatially explicit metapopulation models. It representsthe metapopulation threshold where colonization rate and localextinction rate are balanced.
4.1. No negative climate impact due to phenological shift
We show that for the parameter settings used here, averagetotal adult numbers, and hence population viability remains con-stant over time for both W and W+ scenarios. The increase of drysummer periods expected for the W+ scenario could impede adultactivity and caterpillar development, causing negative impacts on
Chance for processes when PSPL < 0 mm 0.25Mortality Mortality rate inside patch, favourable weather 0.14
Mortality rate during dispersal 0.8Mortality rate unfavourable weather 0.8
Movement Threshold for TMAX for number of steps per day 31 (◦C)Number of steps per day when TMAX is above threshold 647Number of steps per day when TMAX is below threshold 510Chance to stay in patch when encountering patch border(U turn), coming from inside patch
0.88
step length inside patch (m):= f · TMAX + g · RADI + h f −0.07 (m/◦C)
a For abbreviation of weather variables, see Table 1.b Number of eggs laid per female on specific day (part of formula for daily fraction is indicated below brace).c Density of female (unfertilized and fertilized) in patch.d Age of individual as fertilized female.
population performance. However, we did not find any differencein population viability between scenarios W and W+.
The slight interruption in the acceleration of caterpillar devel-opment between 2010 and 2040 (years 70–100, especially clearfor the W scenario; Fig. 4) is caused by the shift from historicalweather data as model input (up to 2009) to transformation dataon future weather (i.e. after 2009) that is derived from transforma-tions of historical weather series, taking the climate around 1990(1976–2005) as a basis. Changed weather conditions that actu-ally occurred between 1990 and 2010 are therefore not accountedfor in the transformation series: the transformed data underesti-mate the rate of climate change when compared to real change
in the period 1990–2010, and especially the record hot years in2005–2010.
Advanced timing of caterpillar development due to climaticwarming can also be inferred from empirical studies showingadvancing butterfly emergence (Roy and Sparks, 2000; Van Strienet al., 2008). Sparks and Menzel (2002) state that in the UK, mostbutterfly species have already been affected by climatic warming.Trends to earlier first and peak appearance have been noted, andmost of these correlates well with temperature (Roy and Sparks,2000). Data on pupation and emergence dates for Purple Emperorbutterfly Apatura iris shows an advanced emergence of on aver-age 9 (males) to 12 (females) days per decade (Dell et al., 2005).
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Table 4Number of patches per simulation landscape.
Habitat amount (%) Patch area (ha)
0.1 0.2 0.4 0.8
0.5 125 63 31 161 250 125 63 312 500 250 125 a
a No runs conducted with this landscape.
Emergence dates related strongly to spring temperatures, particu-larly with increasing daily maximum temperatures for the monthsMarch–May. The rise in spring temperatures especially influencedlate larval instar growth and development. A negative consequenceobserved with earlier emergence dates is the occurrence of lethalextra broods in typical univoltine species; early instars of individ-uals emerging in cold autumn conditions suffer from food shortagedue to leaf fall (Dell et al., 2005). However, from empirical stud-ies it is unclear how earlier emergence is related to populationabundance (Roy and Sparks, 2000). Our simulations suggest a linkbetween population size and emergence date that is of specialimportance in the light of climate change. Advancing phenologymay thus result in an avoidance or reduction of adverse droughtconditions during summer.
4.2. Perspectives for conservation
As advanced timing of development improves conditions forour model butterfly species, climate change does not necessar-ily sort negative impacts on population performance. It is clear,
however, that this applies specifically to a univoltine species withbutterflies emerging in late spring or early summer. It is thereforeunlikely that this result can be generalized to species with differ-ent life-histories. Thus, butterflies emerging later in summer maybe expected to suffer more heavily from summer droughts, despitepossible phenological shifts.
In any case, viable populations only persist in sufficiently suit-able landscapes, concerning juxtaposition and sizes of habitatpatches. We have shown that either varying the amount of suit-able habitat or patch size revealed a sharp threshold in populationviability, and this is consistent with metapopulation theory (e.g.Levins, 1970). Hence, further habitat fragmentation will eventuallyresult in regional loss of species. Species requirements on patchcarrying capacity and interpatch distance should unabatedly beconsidered.
4.3. Perspectives for further research
Developing detailed models is constrained by availableknowledge and data necessary for model construction and parame-terization (Gallien et al., 2010). Systematic fieldwork and laboratoryexperiments are necessary to unravel the exact relationshipbetween weather and activity, development and/or survival of thedifferent stages of butterflies.
We suggest that the model can be improved by adding hetero-geneity in the landscape, e.g. by varying the weather variables frompatch to patch, and possibly also within patches, mimicking dif-ferent microhabitats with different microclimates. A perspectiveof our approach would be to apply our model in series of realis-tic landscapes, including unequal patch sizes and heterogeneous,
Fig. 2. Box plots for number of adults in patches over total flight period in time slots. Box plots are shown for different simulation landscapes.
80 A. Cormont et al. / Ecological Modelling 227 (2012) 72– 81
Fig. 3. Box plots for occupancy chance in time slots. Box plots are shown for different simulation landscapes.
climate-dependent habitat quality. Our study can be extendedby experiments in which the performances of several taxonomicgroups or “ecoprofiles” (species requirements on patch carryingcapacity and interpatch distance, see Opdam et al., 2008) will
be compared. We propose to incorporate a greater variation inlife-history traits, such as timing of development, that reflects sus-ceptibility to climate change. In these ways, generalizations oflandscape adaptation rules will be allowed for.
Fig. 4. Sensitivity to parameter settings for landscape with suitable habitat amounts of 0.5, distributed over habitat patches of 0.8 ha, on moving averages (30 years) of daynumber of start of pupation, which decreased over time under default parameter values (bold solid lines). We increased standard caterpillar mortality rate with 10% (dashedlines), we enlarged the effect of drought on caterpillar development by increasing both growth reduction and caterpillar mortality rate during weight loss with 10% (dottedlines) as well as with both 50% (dot dashed lines), and we avoided limited caterpillar growth (long dashed lines)—the latter three lines are hidden by the position of the boldsolid lines; black lines: W scenario; grey lines: W+ scenario.
Author's personal copy
A. Cormont et al. / Ecological Modelling 227 (2012) 72– 81 81
Acknowledgments
This research was funded by the Dutch national research pro-gramme ‘Climate Changes Spatial Planning’ and is part of thestrategic research programme ‘Sustainable spatial developmentof ecosystems, landscapes, seas and regions’ (Project EcologicalResilience) which is funded by the Dutch Ministry of Agriculture,Nature Conservation and Food Quality, and carried out by Wagenin-gen University and Research centre. We thank Claire Vos for helpfulcomments on the manuscript and Frits Bink for his expert judgmenton butterfly reproduction.
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