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Keywords:

  • absorbance;
  • lethal temperature;
  • Phyllonorycter blancardella;
  • stomatal conductance;
  • thermal environment

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
  • 1
     One major gap in our ability to predict the impacts of climate change is a quantitative analysis of temperatures experienced by organisms under natural conditions. We developed a framework to describe and quantify the impacts of local climate on the mosaic of microclimates and physiological states of insects within tree canopies. This approach was applied to a leaf mining moth feeding on apple leaf tissues.
  • 2
     Canopy geometry was explicitly considered by mapping the 3D position and orientation of more than 26 000 leaves in an apple tree. Four published models for canopy radiation interception, energy budget of leaves and mines, body temperature and developmental rate of the leaf miner were integrated. Model predictions were compared with actual microclimate temperatures. The biophysical model accurately predicted temperature within mines at different positions within the tree crown.
  • 3
     Field temperature measurements indicated that leaf and mine temperature patterns differ according to the regional climatic conditions (cloudy or sunny) and depending on their location within the canopy. Mines in the sun can be warmer than those in the shade by several degrees and the heterogeneity of mine temperature was incremented by 120%, compared with that of leaf temperature.
  • 4
    . The integrated model was used to explore the impact of both warm and exceptionally hot climatic conditions recorded during a heat wave on the microclimate heterogeneity at canopy scale. During warm conditions, larvae in sunlight-exposed mines experienced nearly optimal growth conditions compared with those within shaded mines. The developmental rate was increased by almost 50% in the sunny microhabitat compared with the shaded location. Larvae, however, experienced optimal temperatures for their development inside shaded mines during extreme climatic conditions, whereas larvae in exposed mines were overheating, leading to major risks of mortality.
  • 5
     Tree canopies act as both magnifiers and reducers of the climatic regime experienced in open air outside canopies. Favourable and risky spots within the canopy do change as a function of the climatic conditions at the regional scale. The shifting nature of the mosaic of suitable and risky habitats may explain the observed uniform distribution of leaf miners within tree canopies.

Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

A huge number of studies have demonstrated the effects of body temperature on physiology and behaviour of ectothermic organisms (e.g. reviewed by Chown & Nicolson 2004). One remaining major gap in our knowledge, however, is a quantitative analysis of body temperatures experienced by organisms under natural conditions (Helmuth 2002). This lack of knowledge severely curtails the possibility to extrapolate from controlled physiological measurements to the organismal physiology in the field. The temperature of organisms often differs greatly from that of their surrounding, and usually results from the interaction between multiple climatic factors and physiological and physical properties of the organism (Gates 1980; Casey 1992; Helmuth 2002).

Quantification of body temperature in the field permits us to forecast the impacts of predicted climate change on organismal physiology. Quantitative studies based on continuous records of body temperatures and climatic variables are constrained by two experimental aspects. First, body temperature measurements of small and moving animals in the wild are challenging. Secondly, continuous measurements are often carried out during relatively small time windows. Fortunately, the biophysical approach mitigates these constraints because it does not only generate body temperature estimates using climatic data, but also it identifies the physical and physiological mechanisms that determine the body temperature patterns (e.g. Porter & Gates 1969; Helmuth 1998; Helmuth, Kingsolver & Carrington 2005; Pincebourde & Casas 2006a). Once the thermal mechanisms are elucidated, biophysical models can be used to extrapolate the shifts in body temperature in response to the climate change (e.g. Gilman, Wethey & Helmuth 2006).

A crucial step in our understanding of the impact of environmental conditions on organismal physiology is to quantify the spatial heterogeneity in microclimatic conditions at local scales (i.e. the spatial scale at which the organism is moving). Local heterogeneity in microclimatic conditions can be due to the interaction between the sun course and the ground topology, the environmental geometry, or the heterogeneity in surface material properties (e.g. Huey et al. 1989; Grace 1991; Weiss et al. 1993; Coxwell & Bock 1995; Helmuth 2002). It is therefore necessary to elucidate the mechanisms leading to the observed local heterogeneity in microclimate to obtain a comprehensive and realistic representation of the environment experienced by an organism.

A tree canopy is a highly heterogeneous environment (Orians & Jones 2001). The energy budget of a leaf differs from that of another one depending on its position within the canopy due to variations in radiation interception and wind attenuation. The amount of radiation a leaf receives strongly depends on the radiation level above the tree and on the quantity of radiation intercepted, reflected and transmitted by the neighbouring leaves. The light environment of a canopy results from the complex interaction between sun position, the amount of radiation at the top of the canopy, and the geometry of the canopy (e.g. Ackerly & Bazzaz 1995; Sinoquet et al. 1998, 2001; Montgomery & Chazdon 2001). Wind speed is attenuated when air flow penetrates the canopy. Wind speed attenuation depends on the total amount of foliage along the wind vector (Daudet et al. 1999).

The mechanistic relationship between canopy geometry, microclimate heterogeneity and the physiology of herbivore insects has never been explored in depth. Our aim is to elucidate these links, and to integrate them into biophysical models to predict body temperature patterns from climatic variables taken at the regional scale (i.e. above the tree) for any location within the habitat. This framework is then used to determine the spatial and temporal thermal heterogeneities in a phytophagous insect microclimate at the tree canopy scale, and the consequences for the insect's physiology.

We investigated the impact of local climate on microclimate, body temperature and larval developmental rate heterogeneities at tree canopy scale in the leaf mining moth Phyllonorycter blancardella (Fabricius 1781) (Lepidoptera: Gracillariidae). This moth infests all parts of apple tree canopies (Pottinger & LeRoux 1971; Casas 1990). We first elucidated the impact of climatic conditions on mine temperature patterns based on field measurements made during 4 days. From these field observations, we analysed the dynamics of the climate-dependent mosaic of optimal and suboptimal microclimates within tree canopies during moderate climatic conditions. Secondly, we explored the impacts of climate variability on microclimate and developmental rate heterogeneities at canopy scale under severe thermal conditions. For this purpose we built a model by integrating (1) a model for radiation and leaf energy balance distribution within a tree canopy (Sinoquet et al. 2001); (2) a biophysical model of mine temperature (Pincebourde & Casas 2006a); (3) an empirical model of larval body temperature (Pincebourde & Casas 2006b); and (4) a physiological model of larval developmental rate (Logan et al. 1976). This integration allows us to determine the developmental rate of a larva at a given location within the tree crown from climatic variables measured above the tree (i.e. regional climate) and tree canopy geometry. We tested the integrated model using the field temperature measurements. While the integrated model was found adequate for modelling microclimate temperatures during moderate climatic conditions, we do not know its behaviour and consequences under warm and hot regional climatic weather. Therefore, we used climatic data measured near our study site during the 2003 European heat wave to explore these aspects. Heat waves are extreme climatic events characterized by exceptionally hot air temperatures. They are likely to occur with an increasing frequency and duration during the next century in North America and Europe as a result of anthropogenic greenhouse gas accumulation in the atmosphere (Easterling et al. 2000; Seneviratne et al. 2006).

Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

insect–plant relationship

The spotted tentiform leaf miner Phyllonorycter blancardella feeds on apple leaves. Females lay eggs individually on the lower side of leaves. Newly hatched larvae penetrate the leaf and five larval stages develop within leaf tissues. During the first three stages larvae are sap feeders, whereas the two last stages are tissue feeders (Pottinger & LeRoux 1971). Third stage larvae delimit the perimeter of the mine inside which fourth and fifth stage larvae feed on mesophyll tissues. The white spots at the mine surface (c.1 cm2) correspond to eaten areas, and the green patches correspond to the photosynthetically active tissues remaining within a mine (Pincebourde et al. 2006). A detailed description of the mine structure is given in Pincebourde & Casas (2006a). Our study focused on the fifth larval stage for which leaf tissue modifications are more pronounced (Pincebourde 2005; Pincebourde & Casas 2006a). The parameters related to stomatal behaviour and optical properties of the mine during the other larval stages are still unknown.

Temperature and climatic measurements, as well as determination of some model parameters, were made in the field. The canopy geometry of a 20-year-old apple tree Malus communis (L.) was measured in Vouvray (France), near Tours (47°22′N, 0°40′E). The tree was located in an open area. Its trunk had a circumference of 77 cm and a diameter of 25 cm. Its canopy was approximately 4·8 m in height, and 4·1 m and 4·9 m in east–west and north–south directions, respectively. The leaf miner was present at a low population density in the area. The apple tree was artificially infested with the leaf mining moth in June 2004. Fifteen bags made of fine tissue were placed at different locations within the tree crown, each bag enclosing a part of a branch. Some pupae and adults of the leaf miner, coming from our laboratory rearing, were dispersed within each bag.

measurement of climatic variables and leaf and mine temperatures

Leaf and mine temperature patterns were measured in the field on the 20-year-old apple tree in August 2004. Leaf and mine temperatures and climatic variables were measured simultaneously during four consecutive days (thereafter day 1, day 2, day 3 and day 4) showing different climatic conditions. These 4 days were categorized based on sky overcast. Two days (days 1 and 2) were cloudy (i.e. low radiation level and high ratio diffuse/direct radiation), whereas the two others (days 3 and 4) were sunny, cloudless days (i.e. high radiation level and low ratio diffuse/direct radiation).

Climatic measurements

Several instruments were deployed in the field. Air temperature and relative humidity within the canopy were measured using a MP100A probe (Campbell Scientific Ltd, Leicestershire, UK) equipped with a 41004-5 radiation shield (Campbell Scientific Ltd) and connected to a CR10X data logger (Campbell Scientific Ltd). The probe was placed 1·50 m above the ground within the canopy. Wind velocity and radiation level were measured above the canopy by placing the instruments 5 m above the ground near the apple tree. Wind velocity was recorded using a air velocity transducer (model 8465, TSI Incorporated, St Paul, MN, USA) connected to a CR10X data logger. Global radiation, diffuse radiation and PAR levels were measured with two pyranometer sensors (CM3, Campbell Scientific Ltd), the second being equipped with a shadow ring (CM 11/121, Kipp and Zonen, Delft, the Netherlands), and a quantum sensor (LI-190SA, Li-Cor Inc., Lincoln, NE, USA), respectively. The two pyranometers were connected to a CR10X data logger and the quantum sensor was linked to a LI-1400 data logger (Li-Cor Inc.). All measurements were made simultaneously every 5 min.

Leaf and mine temperature measurements

Leaf temperature was measured using a fine copper-constantan thermocouple (type T, 0·2 mm in diameter; TCSA, Dardilly, France) placed along the midrib and applied to the lower leaf surface. Thermocouples were attached such that the natural orientation of the leaves was not altered. Measurements were taken every 5 min. A total of 24 shaded leaves and 28 sunny leaves were measured during the 4 days, and different leaves were taken each day. The sample size differed due to exclusion from the analysis of some leaves showing an unclipped thermocouple during the day. Mine temperature was measured by inserting a fine copper-constantan thermocouple (type T, 0·2 mm in diameter; TCSA) through a feeding window located on the upper mined leaf surface. The insertion point was covered with vegetable oil in order to avoid any leakage. This method does not alter the stomatal behaviour in the lower mined leaf tissues (Pincebourde & Casas 2006a). Measurements were taken at 5-min intervals. Different mines were recording each day. Temperature measurements of 39 shaded mined leaf tissues and 28 sunny mined leaf tissues were obtained over the whole period and different mines were taken each day. Sample size differed for the same reasons as above. All thermocouples (leaves and mines) were connected to a Campbell CR10X data logger. All measured leaves and mines were labelled to identify them within the data set describing the canopy geometry, which allowed us to test for the accuracy of the integrated model (see below).

modelling radiation interception by a canopy

The RATP model (Radiation Absorption, Transpiration and Photosynthesis) was designed to describe the spatial distribution of radiation, transpiration and photosynthesis within plant canopies. The full model is described in Sinoquet et al. (2001) (see also Appendix S1, Supplementary material). Here, the submodel simulating the interception of solar radiation by leaves at the intracanopy scale was used. The model is based on the Beer's law, the canopy being treated as a turbid medium. This approach has been widely used to describe the light distribution within canopies (e.g. Impens & Lemeur 1969; Sinoquet & Bonhomme 1992; Kitajima, Mulkey & Wright 2005). Inputs for this model are the canopy geometry, optical properties of leaves and soil surface and the climatic driving variables. The canopy geometry is described by discretizing the space into a grid of 3D cubic cells (voxels) the size of which is user-defined. Each cell might be empty or characterized by the area density of a given plant component (i.e. leaf), according to 3D digitizing data. This model for interception of solar radiation was computed for both photosynthetically active radiation (PAR, 400–700 nm waveband) and near infrared radiation (NIR, 700–2500 nm waveband). Outputs are fluxes of PAR and NIR intercepted by both sunlit (inline image and inline image, respectively) and shaded areas (inline image and

  • image

, respectively) in each 3D cell k occupied by leaves.

modelling the energy budget of a leaf and of a mine

We used published biophysical models to compute the energy balance of leaves and mines (Campbell & Norman 1998; Nobel 1999; Pincebourde & Casas 2006a). Computing the energy budget of leaves is needed due to the leaf temperature dependence of the mine thermal environment. Briefly, the biophysical model computes heat fluxes exchanged through radiation absorbance, radiation emission, conduction–convection and evaporation mechanisms (Appendix S1). The energy balance was solved for temperature using the iterative Newton–Raphson method (Nougier 1985). These energy budgets were coupled with the model for interception of solar radiation described above. Outputs of the energy budget model are temperatures of both leaves and mines in sunlit (inline image and inline image, respectively) and shaded areas (inline image and inline image, respectively) within each 3D cell k of the canopy.

modelling larval body temperature

Body temperature of a larva in cell k (inline image) depends on mine temperature and larval position i within a mine. A larva experiences different temperature when below feeding windows that transmit radiation within a mine easily or when it is located below green patches that transmit radiation only weakly (Pincebourde & Casas 2006b). We used an empirical model to calculate body temperature in cell k and position i from the mine temperature and the amount of incoming radiation (Pincebourde & Casas 2006b). In the sunlit area (the same calculation hold for shaded area by using the radiation term for the shade), we have

  • image(eqn 1)

where inline image is the empirical function describing the body-to-mine temperature deviation as a function of irradiance at larval position i within the mine. This function is, for feeding windows (FW) and green patches (GP), respectively,

  • image(eqn 2)
  • image(eqn 3)

These empirical relationships were obtained using metal halide lamps to irradiate the mines and the insects (see Pincebourde & Casas 2006b). The light spectrum of these lamps is very similar to that of the sun, including the ratio visible to near infrared radiation. The entire solar spectrum is therefore implicitly considered into the empirical eqns 2 and 3.

Data reported by Djemai, Meyhöfer & Casas (2000) show that, on average, 40% of the body is positioned below feeding windows, whereas 60% is not. Body temperature was therefore calculated as a weighed average of body temperatures found at the two locations. Outputs of the model at this stage are therefore body temperature of fifth stage larvae, within sunlit and shaded mines, in cell k.

modelling larval developmental rate

Developmental rate is defined as the inverse of the developmental time. The model developed by Logan et al. (1976) was used to compute the instantaneous developmental rate (dT: h−1) of a larva as a function of body temperature in cell k averaged over an hour

  • image(eqn 4)

where inline image and inline image are lower temperature threshold for development and upper lethal temperature (°C), respectively. C1 (day−1) is the developmental rate at the lower threshold; C2 is the rate of increase from the lower temperature threshold to optimum temperature for development; and C3 (°C) is the temperature range over which an increase in temperature has a negative influence on the developmental rate (e.g. see Fig. 4 for a graphical representation of the model). Instantaneous developmental rate was obtained dividing by 24 the equation that was initially developed to compute the daily developmental rate. We also computed the daily developmental rate (day−1), which is defined as the development rate per day. It was computed by summing the instantaneous developmental rates over the entire day. This physiological model assumes instantaneous adaptation of body temperature and also assumes that the developmental rate is independent of the temperatures previously sustained by the larvae.

image

Figure 4. The physiological model of developmental rate for fifth stage P. blancardella larvae. The developmental rate is shown as a function of body temperature on a daily (left axis) and on an hourly basis (right axis). The grey part of the curve indicates the body temperature range that induces thermal stress.

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crossing spatial scales

The radiation interception model, the energy budget models, the model of body temperature and the physiological model of developmental rate were combined in a final step (Fig. 1). This combination allowed us to calculate instantaneous and daily developmental rates of a larva located inside a given mine on a given leaf at a given location within the tree canopy, from the regional climatic data and the canopy geometry parameters. The complete model is called afterward ‘integrated model’. The integrated model comprises therefore a biophysical (mechanistic) component (radiation interception model and energy budgets), a physiologically semimechanistic part (developmental rate), and an empirical (nonmechanistic) component (body temperature model). The radiation interception model and the energy budgets of a leaf and a mine have been previously tested individually (Sinoquet et al. 2001; Pincebourde & Casas 2006a). Hence, we tested here the biophysical model combining the two using the data of leaf and mine temperatures and climatic variables measured during moderate climatic conditions. We did not test the body temperature, instantaneous developmental rate and integrated models in the field because it is impossible to measure body temperature without opening and destroying the mine.

image

Figure 1. The conceptual approach. This scheme shows how the radiation interception model, the energy budget models, the model of body temperature and the physiological model of developmental rate were integrated such that the larval developmental rate was predicted from regional climatic data (i.e. above the tree) and canopy geometry parameters. Boxes indicate the models, white arrows the model entries, and black arrows the model outputs.

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model parameters

Parameters of the models are described in Table 1.

Table 1.  Parameters of the integrated model. Equations of the stomatal response functions are given in Pincebourde & Casas (2006a)
ParametersSymbolsValues (units)Sources*
  1. PAR, photosynthetically active radiation; NIR, near infrared radiation; max., maximal; cond., conductance. *Data are from the current study except as noted. †Parameters of the developmental rate model of Logan et al. (1976) applied to last larval stage of P. blancardella.

Climatic variables
 Incident global radiationIbo0–1161·1 W m−2Driving variable
 Incident diffuse radiationIdo0–193·3 W m−2Driving variable
 Air temperatureTair15·6–36·8 °CDriving variable
 Ground temperatureTg21 °CDriving variable
 Air relative humidityhr40–70%Driving variable
 Air vapour pressure deficitea957·9–2967·7 PaDriving variable
 Wind velocityu0·4 m s−1Driving variable
Tree canopy geometry
 Leaf twist angleθ−180–180° 
 Leaf inclination angleα−88·52–89·08° 
 Leaf azimuth angleφ−180–180° 
 X coordinate rangeX534·01 cm 
 Y coordinate rangeY491·48 cm 
 Z coordinate rangeZ299·39 cm 
Absorbance
 Leaf PARinline image0·84Pincebourde & Casas (2006a)
 Leaf NIRinline image0·02Pincebourde & Casas (2006a)
 Mine PARinline image0·48Pincebourde & Casas (2006a)
 Mine NIRinline image0·44Pincebourde & Casas (2006a)
Soil scattering coefficient
 Soil PARinline image0·07Combes et al. (2000)
 Soil NIRinline image0·20Combes et al. (2000)
Plant conductance
 Leaf upper surfaceinline image0·003 mol m−2 s−1Pincebourde et al. (2006)
 Mine upper surfaceinline image0·009 mol m−2 s−1Pincebourde et al. (2006)
 Leaf max. stomatal cond.inline image0·276 mol m−2 s−1 
 Mine max. stomatal cond.inline image0·227 mol m−2 s−1 
Metric parameters
 Mine–leaf interface areaPM1·40 × 10−5 m2Pincebourde & Casas (2006a)
 Mine surfaceSM1·02 × 10−4 m2Pincebourde & Casas (2006a)
Developmental rate model
 Lower threshold 5·2 °CBaumgartner & Severini (1987)
 Upper threshold 42 °C 
 Lower developmentC10·047 day−1Baumgartner & Severini (1987)
 Rate of increaseC20·066Baumgartner & Severini (1987)
 Temperature rangeC32·390 °CBaumgartner & Severini (1987)
Parameters for the radiation interception model

Canopy geometry was measured with a 3D digitizer and the software Pol95 (Adam 1999), which capture localization in the space (3D coordinates) and orientation (three angles of Euler) of each leaf within a tree crown (Sinoquet et al. 1998). Dimensions of the leaves were also partly measured and the distribution of leaf dimensions was used to model those of the unmeasured leaves. Our data set reports the spatial description of a canopy containing 26 281 leaves and a total foliage area of 37·01 m2 (Fig. 2). We described canopy geometry as a matrix of 3D cubic cells, the volume of which was 8000 cm3 (i.e. 20 cm × 20 cm × 20 cm).

image

Figure 2. 3D representation of the 20-year-old apple tree, when viewed from the south-west side (azimuth 120°). This representation was obtained using VegeSTAR software (Adam, Sinoquet & Dones 2001). Leaves were assimilated as polygon-shaped entities. Variations in grey levels correspond to variations in the amount of radiation received by leaves when the tree is exposed to direct radiations. The darker the leaves, the higher the amount of radiation intercepted. The trunk has been drawn for visual convenience.

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Parameters for the energy budgets

All the parameters used to compute net radiation and sensible heat (i.e. heat exchanged through conduction–convection) balances of both leaves and mines are given in Pincebourde & Casas (2006a). We measured the maximal stomatal conductance values to compute the latent heat (i.e. heat lost through evaporation) balance of a leaf and a mine. Maximal stomatal conductance of both intact and mined leaf tissues were measured using an infrared gas analyser–leaf chamber system (LI-6400, Li-Cor Inc.). Measurements were made in situ on the 20 years apple tree following the method explained in Pincebourde & Casas (2006a). Maximal stomatal conductance was recorded on 13 intact sunny leaves and on 19 sunny mined integuments. The stomatal responses to environmental changes are given in Pincebourde & Casas (2006a).

Parameters for the developmental rate model

The developmental rate model was parameterized using both published data (Baumgartner, Delucchi & Genini 1981; Baumgartner & Severini 1987) and our own measurement of upper lethal temperature (inline image). The temperature threshold inducing 50% mortality (LD50) was measured by placing 10 groups of 12 freshly excised fifth stage larvae (n = 120 larvae) at a given temperature within a climatic chamber. The chamber regulated temperature with a 0·1 °C precision. For each group, the 12 larvae were placed within a small Petri dish equipped with a fine copper-constantan thermocouple (type T, 0·2 mm in diameter; TCSA) measuring the temperature inside the Petri dish. Air within the Petri dish was maintained saturated for water vapour by placing a humid cotton piece inside. Each group of 12 larvae was treated separately. The Petri dish was first placed at 30 °C for 15 min. The temperature was then increased by 0·32 °C per minute (i.e. rate of increase inherent to the climatic chamber) until it reached the pre-defined temperature. This temperature was maintained for 1 h after which the Petri dish was removed from the chamber. Each group was tested at a different experimental temperature from 36 °C to 45 °C (with 1 °C increment). Larval survival was assessed twice, immediately after the thermal treatment and 24 h later. The test consisted of touching larvae with a fine needle. Living larvae moved violently under this treatment. This behaviour is the characteristic response to oviposition attempts by parasitoids (Djemai et al. 2000).

model explorations

The temperatures of leaves and mines during two non consecutive clear days having similar radiation levels but differing in maximal air temperatures during daytime were simulated using the integrated model. We used meteorological data from the AGROCLIM database (INRA, France; [http://www.avignon.inra.fr/internet/unites/unite_experimentales/agroclim/version_index_html]) taken at Lusignan (France; 51°60′N, 0°15′E) on 20 and 22 June during the 2003 European heat wave. The first day (‘warm day’) was a typical summer day in the centre of France, whereas the second day (‘hot day’) was an extremely warm day. The loaded data consisted in values of air temperature and global incident radiation taken at a 1 h time step. The amount of diffuse radiation was theoretically determined using the ratio diffuse-to-global radiation, which depends on solar zenith angles (Gates 1980). PAR and NIR were assumed to contribute solar radiation by 48 and 52%, respectively (Varlet-Grancher 1975). The other meteorological variables were fixed at realistic values. The parameters in Table 1 were used for all simulations. A third of the leaves was assumed to be occupied by a mine for the simulation purposes and mined leaves were chosen by random within the 3D data sheet. Then, the number of cells occupied by mines, and therefore the sample size in the analysis, was 1611. Instantaneous developmental rates were obtained by using the body temperature predicted for each hour. We report leaf, mine and body temperature and developmental rates for 1611 grid cells of the canopy, which consisted in a total of 1943 cells.

statistical analyses

Box plot charts were built to represent the temperature patterns measured in the field during the four moderate days in August 2004. The distributions were compared using a two-sided Kolmogorov–Smirnov test (KS). The combination of the radiation interception and the energy budget models (biophysical part of the integrated model) was tested by comparing predictions of leaf and mine temperature to measurements made in the field during the 4 days. The thermal heterogeneity of mine microclimate at canopy scale was compared with that of leaves by plotting the temperature deviation between sunny and shaded mines as a function of that of leaves. A Student's t-test was used to test for a difference between the slopes of the regression lines (Zar 1998). Regressions were set to go through the origin.

The model was run using the climatic data recorded continuously during the four moderate days. The biophysical model was tested for its accuracy by performing Pearson correlations between predictions and measurements and by calculating the root mean square error (RMSE) of the predictions from the 1 : 1 relationship (i.e. measured temperature equals predicted temperature).

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

parameter estimations

Canopy geometry

The total leaf area per class of leaf inclination angle was computed. Inclination angle ranged from −88·52° to +89·08°. An inclination angle of 0 °C means that the leaf is horizontally orientated and perpendicular to the solar beam when the sun is at the zenith position. The distribution of leaf area per class of leaf inclination angle was normal (Lillifors two-tailed probability test: P = 0·74, nonsignificant from normal distribution). The distribution peaked at the inclination angle 0° and 22% of the canopy foliage had an inclination ranging from −10° to 10°.

Maximal stomatal conductance

The mean maximal stomatal conductance of mined leaf tissues (0·227 ± 0·108 mol m−2 s−1, n = 19) was similar to that of intact leaf tissues (0·276 ± 0·123 mol m−2 s−1, n = 13; Student's t-test: P = 0·71).

Upper lethal temperature

All larvae survived at a body temperature of 38 °C but the survival rate continuously decreased above that temperature. All excised larvae were killed during an exposure of 1 h at a body temperature of 43 °C (Fig. 3). The LD50 was at body temperature 42 °C for an exposure of 1 h (Fig. 3). Thus, the body temperature range between about 38 °C and 42 °C was considered as the thermal stress temperature range in P. blancardella.

image

Figure 3. Determination of the upper lethal temperature threshold for fifth stage P. blancardella larvae. Rate of survival is shown as a function of body temperature for an exposure of 1 h. Each dot represents a sample of 10 larvae.

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Developmental rate model

The development rate of larvae increases exponentially until body temperature reaches 37 °C, and decreases sharply from 37 °C to 42 °C (Fig. 4).

field measurements: leaf and mine temperature patterns

Temperature patterns during cloudy conditions

The climatic conditions during the two cloudy days ranged from 15·6 °C to 22·4 °C for air temperature, 57% to 91% for relative humidity, and 0 to 2·30 m s−1 for wind speed. Global and diffuse radiation levels were up to 883 W m−2 and 369 W m−2, respectively, at midday. The maximal radiation levels were relatively high for a cloudy day, due to sun appearing for a very short time window (i.e. about 15 min). The temperature pattern of leaves and mines were similar during the two cloudy days at shaded locations (Fig. 5; KS test: day 1, D = 0·075; day 2, D = 0·183; P > 0·05 for both) as well as at sunny locations (Fig. 5; KS test: day 1, D = 0·069; day 2, D = 0·143; P > 0·05 for both). Temperature patterns, however, differed significantly between shaded and sunny locations for both leaves (Fig. 5; KS test: day 1, D = 0·167; day 2, D = 0·371; P < 0·001 for both) and mines (Fig. 5; KS test: day 1, D = 0·179; day 2, D = 0·497; P < 0·001 for both). This suggests that leaves and mines reached similar temperatures during cloudy conditions within the respective locations.

image

Figure 5. Box plot charts of leaf and mine temperatures during two cloudy days (leaf panels) and two clear days (right panels) at both shaded and sunny locations. In each box plot, the centre horizontal line marks the median of the sample. The length of each box indicates the range within which the central 50% of the values fall with the box edges at the first and third quartiles. The whiskers show the range of values that fall within the inner fences. The asterisks indicate the values within inner and outer fences and far outside values are plotted with empty circles. Statistically different distributions are indicated by different letters above each box plot (two-sided Kolmogorov–Smirnov test).

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Temperature patterns during sunny conditions

Air temperature ranged from 12·1 °C to 24·7 °C, relative humidity from 66% to 88%, and wind speed from 0 to 3·2 m s−1 during the 2 days explored. Global and diffuse radiation levels were up to 1023 W m−2 and 362 W m−2, respectively. The temperature pattern of leaves and mines were similar during the two clear days at shaded locations only (Fig. 5; KS test: day 3, D = 0·131; day 4, D = 0·044; P > 0·05 for both). The temperature pattern of leaves and mines were different at sunny locations suggesting that sunny mines reached higher temperatures than sunny leaves under clear sky conditions (Fig. 5; KS test: day 3, D = 0·352; day 4, D = 0·599; P < 0·001 for both). Temperature patterns differed significantly between shaded and sunny locations for both leaves (Fig. 5; KS test: day 3, D = 0·434; day 4, D = 0·516; P < 0·001 for both) and mines (Fig. 5; KS test: day 3, D = 0·631; day 4, D = 0·846; P < 0·001 for both). The temperature patterns measured during day 4 are given for illustrative purposes in Fig. 6. The temperature of sunny mines and leaves was highly variable due to variations in wind speed as well as in radiation level.

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Figure 6. Mean temperature of mines, leaves and ambient air measured during the day 4 (clear, sunny day). Sunny mines were always warmer than shaded mines and all leaves.

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Comparing mine and leaf thermal heterogeneities at canopy scale

Thermal heterogeneity of leaf and mine microclimates at canopy scale were quantified by calculating the temperature difference between the sunny and the shaded locations for each category (leaf and mine) and for each set of climatic condition (clear vs. cloudy, cumulating the 2 days for each). The thermal heterogeneity of mines was higher than that of leaves during the two climatic conditions (Fig. 7). The thermal heterogeneity of mine microclimate was linearly related to that of leaf microclimate during the clear days (linear regression, y = 2·29x, R2 = 0·86, P < 0·001) as well as during the cloudy conditions (linear regression, y = 1·43x, R2 = 0·76, P < 0·001) (Fig. 7). However, the slope of the two regression lines significantly differed (Student's t-test: P < 0·001) and their values indicated that the thermal heterogeneity of the mine microclimate was increased by 129% and 43% during clear and cloudy climatic conditions, respectively, when compared with that of the leaf microclimate at canopy scale.

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Figure 7. Mine and leaf temperature heterogeneities at canopy scale during the cloudy and the clear days. The difference between mine temperature at shaded and sunny locations is given as a function of the difference between leaf temperature at shaded and sunny locations. The dashed line indicates the x = y relationship (i.e. microclimate heterogeneity is similar for both mines and intact leaves).

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validity of the integrated model

Testing the validity during cloudy conditions

Model predictions for shaded leaf temperature were well matched by measurements in the field (day 1: Pearson's r = 0·84; day 2: Pearson's r = 0·95; P < 0·001 for both) as for sunny leaf temperature (day 1: Pearson's r = 0·87; day 2: Pearson's r = 0·91; P < 0·001 for both) (Fig. 8). Model predictions for shaded and sunny mine temperature were also matched by measurements (shaded mines: Pearson's r = 0·89 for day 1, and Pearson's r = 0·96 for day 2; sunny mines: Pearson's r = 0·91 for day 1, and Pearson's r = 0·88 for day 2; P < 0·001 for all) (Fig. 8). The average RMSE of predictions for shaded leaves, sunny leaves, shaded mines and sunny mines were (day 1/day 2) 0·88 °C/0·52 °C, 0·75 °C/0·86 °C, 0·66 °C/0·36 °C and 0·97 °C/1·19 °C, respectively. The RMSE of predictions for the two whole data sets was 0·82 °C and 0·73 °C, respectively. Therefore, the biophysical model adequately predicts both leaf and mine temperatures as a function of their location within the canopy during cloudy conditions.

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Figure 8. Accuracy of the integrated model as shown by comparing its predictions and actual records of leaf and mine temperatures, made at several positions within the tree canopy and during moderate climatic conditions. This comparison was done for both cloudy conditions (left column) and sunny conditions (right column). Lines show the x = y relationship and the root mean square error of predictions (RMSE) is given for the whole data set of each day.

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Testing the validity during sunny conditions

Model predictions for leaf and mine temperatures were matched by measurements (day 3: shaded leaves: Pearson's r = 0·86; sunny leaves: Pearson's r = 0·83; shaded mines: Pearson's r = 0·91; sunny mines: Pearson's r = 0·93; day 4: shaded leaves: Pearson's r = 0·96; sunny leaves: Pearson's r = 0·82; shaded mines: Pearson's r = 0·93; sunny mines: Pearson's r = 0·80; P < 0·001 for all; Fig. 8). The average RMSE of predictions for shaded leaves, sunny leaves, shaded mines, sunny mines and the whole data set were (day 3/day 4) 0·42 °C/0·48 °C, 1·01 °C/0·56 °C, 0·52 °C/0·74 °C, 0·95 °C/0·87 °C and 0·73 °C/0·66 °C, respectively. The biophysical model at canopy scale predicts leaf and mine temperatures with acceptable precision under clear sky conditions.

Range of validity

The energy budgets of a leaf and a mine and the radiation interception model were previously tested over a wide range of radiation and air temperature values (Sinoquet et al. 2001; Pincebourde & Casas 2006a; Fig. 9). The integrated biophysical model was tested over a smaller range in the field (Fig. 9). All models were found to be valid within the range of climatic conditions tested. The model exploration range (corresponding to the moderate and the hot days) included the integrated model testing range and beyond that (Fig. 9). Our predictions are, however, likely to be valid as (1) the exploration range is within the range of validity of the individual models, and (2) the integrated model was valid over the entire range tested and there were no signs of divergence between data and predictions at the boundaries of the tested parameter range.

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Figure 9. Range of air temperature and radiation level over which the energy budgets models (both leaf and mine), the radiation interception model and the integrated biophysical model were tested. Each area on the graph comprises all variable values tested for each model. All models were found to be valid within the range of climatic conditions tested. The range of variables used for the model explorations is also indicated. Superscript letters indicate the models that were tested elsewhere: aPincebourde & Casas (2006a); bSinoquet et al. (2001).

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model exploration: patterns of leaf, mine and body temperature

The radiation level pattern during the warm and the hot days were very similar, with a maximal level at midday of 1139 W m−2 and 1111 W m−2, respectively (Fig. 10a,b). Air temperature was the only meteorological variable which differed between the two days. Maximal air temperatures were 29·1 °C and 36·8 °C during the warm and the hot days, respectively (Fig. 10c,d). We summed the sunny foliage area of all grid cells to compute the amount of canopy foliage directly exposed to solar radiation. We found that about 44% of the total foliage area was directly exposed to the sun between 7.00 and 17.00 h (not shown). Therefore, the sunny habitat was a significant proportion of the canopy.

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Figure 10. Radiation level is shown during the warm (a) and the hot (b) day. Integrated model predictions (means, SD not shown for clarity) of leaf and mine temperatures at shaded and sunny locations, and measured air temperature, are given for the warm (c) and the hot (d) day. Mean (± SD) body temperature of larvae within sunny mines is also shown. The mean body temperature of larvae within shaded mines was very close to the microclimate temperature (not shown for clarity). The grey parts indicate the body temperature range that induces thermal stress. Temperatures above the stressful range are lethal (at an exposure of 1 h). The horizontal lines indicate the optimal temperature for larval development (Topt). Legends (c and d): air temperature (—), shaded leaf (•), sunny leaf (○), shaded mine (▴), sunny mine (▵), body temperature (×).

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The same general pattern of leaf and mine temperature was found for the 2 days (Fig. 10c,d). Sunny categories were always warmer than shaded categories. Shaded mines were warmer than shaded leaves during daytime, and the temperature of sunny mines was always higher than that of sunny leaves. The standard deviations of mean temperatures in sun and shade at a given time were relatively low and ranged from 0·40 °C to 0·86 °C during the warm day and from 0·51 °C to 1·09 °C during the hot day (minimum to maximum, taken over the daytime and over all locations; not shown).

Body temperature of larvae within sunny mines was predicted to be well below the temperature range inducing thermal stress during the warm day (Fig. 10c). The mean temperature within sunny mines was up to 35·0 °C at midday during the warm day, and mean body temperature was up to 36·4 °C, which is very close to the optimal temperature for development. The mean temperature within shaded mines was only 28·7 °C at midday during warm conditions (Fig. 10c). The mean body temperature within shaded mines was less than 0·5 °C above mine temperature (not shown). Indeed, mean body temperature within shaded mines was about 8 °C below the optimal temperature for larval development. Mean shaded mine temperature, however, was up to 35·3 °C during the hot day, bringing body temperature close to the optimal level for their development (Fig. 10d). By contrast, mean sunny mine temperature during the hot day was within the thermal stress range for about 7 h (Fig. 10d). Mean sunny mine temperature and mean body temperature of larvae living inside were up to 41·5 °C and 42·9 °C, respectively, at midday. Thermal conditions within sunny mines are therefore dangerous for the leaf miner during a hot day.

model exploration: heterogeneity in developmental rates

Not surprisingly, the instantaneous developmental rate of larvae within sunny mines was higher than that within shaded mines during the warm day (Fig. 11). For example, the mean instantaneous developmental rate within sunny mines at 14.00 h was increased by 42% when compared with that within shaded mines. The instantaneous developmental rate in sunny mines during the hot day showed a different pattern. Instantaneous developmental rate within sunny mines was 94% lower than that within shaded mines at about 14.00 h, when the sunny mine temperature attained its maximal value (Fig. 11).

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Figure 11. Instantaneous developmental rate of fifth stage P. blancardella larvae within sunny (○) and shaded (•) mines during the warm and hot days (mean ± SD). Thermally stressful period as deduced from body temperature patterns is indicated in grey for sunny mines.

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These differences in instantaneous developmental rates between sunny and shaded locations resulted in variations in daily developmental rates. The daily developmental rate within sunny mines was significantly increased by 28% when compared with that in shaded mines during the warm day (Student's t-test: t1610 = 1208·4, P < 0·001) (Fig. 12a). By contrast, the daily developmental rate within sunny mines was 22% lower during the hot day when compared with that in shaded mines (Student's t-test: t1610 = 84·8, P < 0·001) (Fig. 12a). Body temperature within sunny mines was above 42 °C for more than 1 h in 81·2% of the 1611 grid cells. The lethal exposure was never reached within the 18·8% grid cells remaining. This suggests that a temperature-induced mortality rate of about 81% could arise within sunny mines during the hot day conditions (Fig. 12b). By contrast, we predicted from temperature patterns that the survival rate would be 100% within all mines during the warm day and within shaded mines during the hot day (Fig. 12b).

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Figure 12. Daily developmental rate (a) (mean ± SD) and estimated survival rates (b) of fifth stage P. blancardella larvae within sunny (white bars) and shaded (black bars) mines during the warm and the hot days. Survival rate was deduced from the proportion of larvae having a body temperature warmer than 42 °C for at least 1 h during the day. Stars indicate the level of statistical significance (P < 0·001).

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Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

validity and generality of the model

We combined two biophysical models (radiation interception by canopies and energy budgets) to compute microclimate temperatures from regional climatic data. The possibility that errors of each model may interact is of concern. Estimating the potential level of this error is therefore critical when interpreting the model outputs. The radiation interception model was shown by Sinoquet et al. (2001) to accurately predict both the magnitude and the daily course of leaf irradiance with a root mean square error of predictions of 90 µmol m−2 s−1. Moreover, the energy budget of a mine has been shown to predict mine temperature with a root mean square of prediction of 0·9 °C (Pincebourde & Casas 2006a). The temperature change induced by a variation in irradiance of 90 µmol m−2 s−1 causes a temperature change of less than 1 °C. The predicted worse error of the integrated model is obtained by summing the error of each model (because the two models are independent from each other), i.e. less than 2 °C. The largest measured error of the integrated model was obtained for sunny mine temperature, which was overestimated up to about 1 °C. Therefore, combining several models does not disproportionately increase the error of predictions. An error of 1 °C could, however, still be problematic when body temperatures are close to the lethal level, as adding or subtracting 1 °C to the mean body temperature in sunny mines during the hot day leads to an estimated mortality rate of 95% and 49%, respectively. The mortality rate due to physical conditions is, however, expected to be significant in any case.

The integrated model works on the basis of the amount of radiation intercepted by a given leaf. It categorizes a leaf or a mine into sunny or shaded locations. Thus, we concentrate here on the two extreme microclimates to determine the boundaries of the thermal heterogeneity at the canopy scale, i.e. leaves that are always under the sun and those that are in the shade throughout the day. A single leaf within the canopy can, however, shift between a sunny and a shaded microclimate within the same day, depending on its position within the tree crown. For example, leaves located in the periphery of East and West portions of a large and spatially complex canopy can receive radiation only early in the morning and late in the afternoon, respectively (Sinoquet et al. 2001). Canopy gaps can also cause some shaded leaves at the bottom of the tree crown to receive radiation for short time windows (Sinoquet et al. 2001). Moreover, the radiation interception model computes the amount of radiative energy absorbed by each leaf by averaging the leaf inclination angle within each cell k. The true variability in leaf and mine temperatures is therefore higher than computed from the mean inclination angle. A leaf absorbs more solar energy when the inclination angle is 0° (i.e. the leaf surface is perpendicular to solar beam) and then declines as the inclination angle decreases or increases. Leaves and mines having a large inclination angle would likely experience a thermal environment that is intermediate between the sunny and shaded location. We observed a normal distribution of leaf area as a function of leaf angle for the whole canopy with the distribution peak at inclination angle of about 0°. The results we report are therefore representative of what is happening for most leaves and mines within a canopy.

The sheer amount of work needed to map more than 26 000 leaves, as well as the difficulties of the best stochastic and statistical models for plant canopy structure to predict the geometry dynamics in older trees (e.g. Costes et al. 2003; Durand et al. 2005) implies that we are not yet in a position to consider the impact of varying canopy geometry on the microclimate experienced by insects. However, the rather dense canopy structure of our tree, combined with the uniform distribution of leaf miners implies that we have broadly sampled all possible microclimatic regimes. Such models as the one we propose are actually necessary to describe the intracanopy distribution of microclimate and developmental rate. For example, it is impossible to compute the developmental rates solely from mine temperature measurements as both the amount of radiation received and body temperature are unknown for all locations.

biophysical ecology of leaf miners

The biophysical functioning of a mine (i.e. the interplay between physical properties of a mine and abiotic factors) is based on the modifications provided by a larva to the absorbance properties of the mine surface and to the stomatal responses to changes in environmental conditions (Pincebourde & Casas 2006a,b; Pincebourde et al. 2006). Larvae induce an increase in the absorbance of near infrared radiation by creating feeding windows. Also, stomata below a mine are closing as radiation level is increasing, whereas they are opening in an intact leaf. The interaction between mine properties and radiation level explains why the temperature difference between shaded and sunny mines was higher during sunny conditions than during cloudy days. The biophysical properties of the mine allow the sunny mine temperature to rise much more than sunny leaf temperature do. These characteristics would, however, be risky during hot conditions without any feedback mechanism in the mine temperature regulation. Indeed, mines have the ability to control partly for overheating (Pincebourde & Casas 2006a). Without such a mechanism, body temperatures of larvae within sunny mines would be expected to be well above 42 °C during hot days. Modulation of heat stress through the manipulation of the biotic environment has been shown in other sedentary organisms such as mussels (Helmuth 1998). Interestingly, organisms living sometimes near their thermal limits such as leaf miners, mussels (Helmuth 1998) and Drosophila (Feder, Blair & Figueras 1997a,b) are showing original mechanisms to decrease their body temperature by only a few degrees. This might be sufficient to increase survival during extreme thermal events by a large margin.

the canopy mosaic of herbivore microhabitats is dynamic

Variations in regional climatic parameters induce the mosaic of microhabitats to be dynamic. We clearly observed that the thermal heterogeneity in mine microclimates at canopy scale substantially differed between cloudy and sunny conditions. The impact was only quantitative during moderate conditions and no shift in habitat suitability is expected as long as the temperatures are still between the minimum required for the development and the optimal temperature. Within this temperature range, sunny mines are likely to be always the more favourable microhabitat for the insect development. Based on these observations, we however expect that conditions within sunny mines would become risky for insect survival during extreme temperature event. The model exploration provided us with both qualitative and quantitative mechanistic details.

The developmental rate of fifth stage P. blancardella larvae is maximal at body temperatures between 35 °C and 38 °C. Body temperatures predicted within sunny mines at midday during the warm day match this optimal body temperature range while that predicted for shaded mines was never above 30 °C. The differences in microclimate between sunny and shaded locations within the canopy caused the instantaneous developmental rate inside sunny mines to be substantially faster than that within shaded mines at midday during a warm day. Conversely, body temperature of larvae within sunny mines falls within the range of thermal stress for several hours during the hot day. The instantaneous developmental rate was therefore considerably lowered. Indeed, the slope of the developmental rate curve is so steep between body temperature 38 °C and 42 °C that even a slight increase of about 1·5 °C leads to a large decrease of about 48% in the developmental rate. This explains why large standard deviations were obtained in the mean instantaneous developmental rate of larvae within sunny mines during the hot day (Fig. 11).

The canopy geometry both magnifies and reduces the heterogeneity of microclimatic conditions experienced by an insect, compared with the regional conditions. The high temperatures occurring during heat waves induce a reversal between the best and worst locations for development. The sunny location is optimal for larval development under warm climatic conditions, but becomes risky for survival under hot conditions. By contrast, shaded locations are suboptimal for larval development under moderate and warm climatic conditions and optimal during hot summers. It should be noted, however, that microclimate quality is not the only parameter of importance for herbivores living within tree canopies.

shifting mosaic and insect distributions

Orians & Jones (2001) qualified plants canopies as resource mosaics and sorted out the numerous abiotic and biotic factors causing this heterogeneity. Leaf nutritional quality varies, among others, as a function of light environment, temperature and soil nutrients (e.g. Lawler et al. 1997; Le Corff & Marquis 1999). Moreover, predators and parasitoids might not forage uniformly within tree canopies. For example, herbivores can be less exposed to predators when resting on leaves inside canopies compared with leaves at the periphery where they are more visible (e.g. Tschanz, Schmid & Bacher 2005). The foraging behaviour of parasitoids can also depend on the level of heterogeneity in herbivore density within the canopy (e.g. Casas 1990; Casas & Djemai 2002). It is not known whether the canopy-scale mosaics of leaf nutritional quality and predation pressure are dynamic, and if so over which temporal windows, and whether their heterogeneity can be modified by variations in climatic conditions.

Thus, the relative importance of microclimatic conditions in the mother's decision for oviposition sites cannot be fully assessed yet. Leaf miners are sessile organisms and the microhabitat characteristics of a larva clearly depends on the oviposition site previously chosen by the female. It is not known whether females of leaf mining insects are able to discriminate between leaves that will provide the optimal microclimate to larvae and those that will not, but that seems to be a difficult task. For example, adult and larva of Drosophila melanogaster, which is feeding on necrotic fruits, also have behaviours that can potentially exploit the environmental heterogeneity to avoid thermally lethal conditions (Feder et al. 1997a). But even so, larvae still undergo stressful and often lethal conditions in the field (Feder et al. 1997b). No stratification in P. blancardella larvae density was found within apple tree canopies, and the distribution seems rather uniform over height (Pottinger & LeRoux 1971; Casas 1990). In other leaf mining species, when a density gradient has been measured, it was shown to be caused by biotic factors rather than by climatic parameters (e.g. Brown et al. 1997). Indeed, the distribution of leaf mining larvae within tree canopies probably reflects a trade-off between several concurrent pressures. A uniform distribution might be the best one to choose when the number of influencing factors is so large and the mosaic of favourable and risky habitats so dynamic.

The canopy geometry induces a large heterogeneity of microclimatic conditions for the leaf miner. Favourable and risky spots within the canopy do change as function of the climatic conditions at the regional scale. Extreme climatic events lead to deadly risks of overheating at specific locations otherwise optimal for the leaf miner's growth. The reversal of suitability of microhabitats as a function of extreme climatic conditions is striking and may well apply to many other biological systems for which such detailed and comprehensive models are not available. This climate-induced dynamic of the microhabitat heterogeneity within canopies could lead to strong modifications in the population dynamics of herbivores through differential mortality. Ultimately, our approach can be used to determine under which conditions climate variations lead to mere local change in microhabitat use and under which conditions more severe shifts in geographical distribution are mandatory.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

The authors are grateful to Jean Guern and Dominique Lidoreau for giving us access to the apple tree. We acknowledge Brian Helmuth for his comments on the manuscript. This study was mainly funded by a PhD scholarship from the French Ministry for Education and Research to S.P. and by the ACI ‘Ecologie Quantitative’ project ‘Ecologie Physique’ led by J.C.

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  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Appendix S1. Equations of the Biophysical Models

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JAE1231_supmat.doc183KSupporting info item

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