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Microclimate Data Improve Predictions of Insect Abundance Models Based on Calibrated Spatiotemporal Temperatures.

Rebaudo F, Faye E, Dangles O - Front Physiol (2016)

Bottom Line: We developed a statistical procedure to calibrate all datasets to monthly and yearly variation in temperatures, while keeping both spatial and temporal variances (air monthly temperature at 1 km² for the WorldClim dataset, air hourly temperature for the weather station, and air minute temperature over 250 m radius disks for the microclimate dataset).We used an additional set of study sites to test the ability of the results of our model to be extrapolated over larger scales.Our simulations therefore stress the importance to consider different temperature datasets depending on the issue to be solved in order to accurately predict species abundances.

View Article: PubMed Central - PubMed

Affiliation: Centro de Análisis Espacial, Instituto de Ecología, Universidad Mayor de San AndrésLa Paz, Bolivia; UMR Evolution Génome Comportement et Ecologie, Université Paris-Sud-Centre National de la Recherche Scientifique-IRD-Paris-Saclay, Institut de Recherche pour le DéveloppementGif-sur-Yvette, France.

ABSTRACT
A large body of literature has recently recognized the role of microclimates in controlling the physiology and ecology of species, yet the relevance of fine-scale climatic data for modeling species performance and distribution remains a matter of debate. Using a 6-year monitoring of three potato moth species, major crop pests in the tropical Andes, we asked whether the spatiotemporal resolution of temperature data affect the predictions of models of moth performance and distribution. For this, we used three different climatic data sets: (i) the WorldClim dataset (global dataset), (ii) air temperature recorded using data loggers (weather station dataset), and (iii) air crop canopy temperature (microclimate dataset). We developed a statistical procedure to calibrate all datasets to monthly and yearly variation in temperatures, while keeping both spatial and temporal variances (air monthly temperature at 1 km² for the WorldClim dataset, air hourly temperature for the weather station, and air minute temperature over 250 m radius disks for the microclimate dataset). Then, we computed pest performances based on these three datasets. Results for temperature ranging from 9 to 11°C revealed discrepancies in the simulation outputs in both survival and development rates depending on the spatiotemporal resolution of the temperature dataset. Temperature and simulated pest performances were then combined into multiple linear regression models to compare predicted vs. field data. We used an additional set of study sites to test the ability of the results of our model to be extrapolated over larger scales. Results showed that the model implemented with microclimatic data best predicted observed pest abundances for our study sites, but was less accurate than the global dataset model when performed at larger scales. Our simulations therefore stress the importance to consider different temperature datasets depending on the issue to be solved in order to accurately predict species abundances. In conclusion, keeping in mind that the mismatch between the size of organisms and the scale at which climate data are collected and modeled remains a key issue, temperature dataset selection should be balanced by the desired output spatiotemporal scale for better predicting pest dynamics and developing efficient pest management strategies.

No MeSH data available.


Related in: MedlinePlus

Observed and predicted abundances computed with the different temperature datasets for the four studied sites. Pest abundances are represented as boxplots and correspond to all pest abundances per month.
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Figure 5: Observed and predicted abundances computed with the different temperature datasets for the four studied sites. Pest abundances are represented as boxplots and correspond to all pest abundances per month.

Mentions: Pest abundances and their standard deviations as a function of months are represented as bar plots in the top of Figure 3. For the models presented in Table 3 based on the three temperature datasets, we found no significant differences between observed and predicted abundances of potato moth (Student tests, p > 0.43, Figure 5). The lowest AIC and highest r-squared values were found for the model based on the Microclimate dataset (Figure 5), indicating a better accuracy of this model over the two other temperature datasets (Table 3). The shape of the boxplots in Figure 5 also indicates that predictions based on the WorldClim and Weather stations datasets tended to smooth abundances between months over the year (low variance compared to the observed abundances) while the Microclimate dataset better represents intra-annual variation in potato moth abundances.


Microclimate Data Improve Predictions of Insect Abundance Models Based on Calibrated Spatiotemporal Temperatures.

Rebaudo F, Faye E, Dangles O - Front Physiol (2016)

Observed and predicted abundances computed with the different temperature datasets for the four studied sites. Pest abundances are represented as boxplots and correspond to all pest abundances per month.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4836147&req=5

Figure 5: Observed and predicted abundances computed with the different temperature datasets for the four studied sites. Pest abundances are represented as boxplots and correspond to all pest abundances per month.
Mentions: Pest abundances and their standard deviations as a function of months are represented as bar plots in the top of Figure 3. For the models presented in Table 3 based on the three temperature datasets, we found no significant differences between observed and predicted abundances of potato moth (Student tests, p > 0.43, Figure 5). The lowest AIC and highest r-squared values were found for the model based on the Microclimate dataset (Figure 5), indicating a better accuracy of this model over the two other temperature datasets (Table 3). The shape of the boxplots in Figure 5 also indicates that predictions based on the WorldClim and Weather stations datasets tended to smooth abundances between months over the year (low variance compared to the observed abundances) while the Microclimate dataset better represents intra-annual variation in potato moth abundances.

Bottom Line: We developed a statistical procedure to calibrate all datasets to monthly and yearly variation in temperatures, while keeping both spatial and temporal variances (air monthly temperature at 1 km² for the WorldClim dataset, air hourly temperature for the weather station, and air minute temperature over 250 m radius disks for the microclimate dataset).We used an additional set of study sites to test the ability of the results of our model to be extrapolated over larger scales.Our simulations therefore stress the importance to consider different temperature datasets depending on the issue to be solved in order to accurately predict species abundances.

View Article: PubMed Central - PubMed

Affiliation: Centro de Análisis Espacial, Instituto de Ecología, Universidad Mayor de San AndrésLa Paz, Bolivia; UMR Evolution Génome Comportement et Ecologie, Université Paris-Sud-Centre National de la Recherche Scientifique-IRD-Paris-Saclay, Institut de Recherche pour le DéveloppementGif-sur-Yvette, France.

ABSTRACT
A large body of literature has recently recognized the role of microclimates in controlling the physiology and ecology of species, yet the relevance of fine-scale climatic data for modeling species performance and distribution remains a matter of debate. Using a 6-year monitoring of three potato moth species, major crop pests in the tropical Andes, we asked whether the spatiotemporal resolution of temperature data affect the predictions of models of moth performance and distribution. For this, we used three different climatic data sets: (i) the WorldClim dataset (global dataset), (ii) air temperature recorded using data loggers (weather station dataset), and (iii) air crop canopy temperature (microclimate dataset). We developed a statistical procedure to calibrate all datasets to monthly and yearly variation in temperatures, while keeping both spatial and temporal variances (air monthly temperature at 1 km² for the WorldClim dataset, air hourly temperature for the weather station, and air minute temperature over 250 m radius disks for the microclimate dataset). Then, we computed pest performances based on these three datasets. Results for temperature ranging from 9 to 11°C revealed discrepancies in the simulation outputs in both survival and development rates depending on the spatiotemporal resolution of the temperature dataset. Temperature and simulated pest performances were then combined into multiple linear regression models to compare predicted vs. field data. We used an additional set of study sites to test the ability of the results of our model to be extrapolated over larger scales. Results showed that the model implemented with microclimatic data best predicted observed pest abundances for our study sites, but was less accurate than the global dataset model when performed at larger scales. Our simulations therefore stress the importance to consider different temperature datasets depending on the issue to be solved in order to accurately predict species abundances. In conclusion, keeping in mind that the mismatch between the size of organisms and the scale at which climate data are collected and modeled remains a key issue, temperature dataset selection should be balanced by the desired output spatiotemporal scale for better predicting pest dynamics and developing efficient pest management strategies.

No MeSH data available.


Related in: MedlinePlus