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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

Comparison between pest performances computed with the different temperature datasets as a function of the WorldClim temperature. For each performance, circles, triangles, and plus signs represent the difference between the WorldClim and the Weather stations, the WorldClim and the Microclimate, and the Weather stations and the Microclimate datasets, respectively.
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Figure 4: Comparison between pest performances computed with the different temperature datasets as a function of the WorldClim temperature. For each performance, circles, triangles, and plus signs represent the difference between the WorldClim and the Weather stations, the WorldClim and the Microclimate, and the Weather stations and the Microclimate datasets, respectively.

Mentions: At low temperatures (9–11°C), performance simulations revealed that the use of the WorldClim dataset tended to underestimate survival rate in comparison to the use of the Microclimate dataset (up to +100%), while the use of the WorldClim dataset led to both under- and overestimate the survival rate in comparison to the use of the Weather stations dataset (between −300 and +100%, Figure 4). At warmer temperatures (12–15°C), performance simulations based on the three datasets revealed almost identical pest survival rates. Simulations of developmental rate and fecundity showed less variation when estimated with the three temperature datasets.


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

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

Comparison between pest performances computed with the different temperature datasets as a function of the WorldClim temperature. For each performance, circles, triangles, and plus signs represent the difference between the WorldClim and the Weather stations, the WorldClim and the Microclimate, and the Weather stations and the Microclimate datasets, respectively.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 4: Comparison between pest performances computed with the different temperature datasets as a function of the WorldClim temperature. For each performance, circles, triangles, and plus signs represent the difference between the WorldClim and the Weather stations, the WorldClim and the Microclimate, and the Weather stations and the Microclimate datasets, respectively.
Mentions: At low temperatures (9–11°C), performance simulations revealed that the use of the WorldClim dataset tended to underestimate survival rate in comparison to the use of the Microclimate dataset (up to +100%), while the use of the WorldClim dataset led to both under- and overestimate the survival rate in comparison to the use of the Weather stations dataset (between −300 and +100%, Figure 4). At warmer temperatures (12–15°C), performance simulations based on the three datasets revealed almost identical pest survival rates. Simulations of developmental rate and fecundity showed less variation when estimated with the three temperature datasets.

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