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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

Standardized temperatures for each month and each dataset. The WorldClim dataset is represented as black horizontal bars. The Weather stations and Microclimate datasets are represented as dark gray and light gray boxplots, respectively. Panels (A–D) for the four study sites.
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Figure 2: Standardized temperatures for each month and each dataset. The WorldClim dataset is represented as black horizontal bars. The Weather stations and Microclimate datasets are represented as dark gray and light gray boxplots, respectively. Panels (A–D) for the four study sites.

Mentions: The Weather stations dataset random component followed a Gaussian distribution (Shapiro–Wilk tests, W = 0.993, 0.986, 0.992, 0.977 and p-value = 0.999, 0.970, 0.998, 0.846 for the four sites, respectively). We therefore simulated the Weather stations dataset using the WorldClim seasonality component and a Gaussian distribution for the random component with parameters fitted for each of the study sites. The resulting model allowed simulating multiple years with the associated variance in temperatures for the four sites (Figure 2).


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

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

Standardized temperatures for each month and each dataset. The WorldClim dataset is represented as black horizontal bars. The Weather stations and Microclimate datasets are represented as dark gray and light gray boxplots, respectively. Panels (A–D) for the four study sites.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 2: Standardized temperatures for each month and each dataset. The WorldClim dataset is represented as black horizontal bars. The Weather stations and Microclimate datasets are represented as dark gray and light gray boxplots, respectively. Panels (A–D) for the four study sites.
Mentions: The Weather stations dataset random component followed a Gaussian distribution (Shapiro–Wilk tests, W = 0.993, 0.986, 0.992, 0.977 and p-value = 0.999, 0.970, 0.998, 0.846 for the four sites, respectively). We therefore simulated the Weather stations dataset using the WorldClim seasonality component and a Gaussian distribution for the random component with parameters fitted for each of the study sites. The resulting model allowed simulating multiple years with the associated variance in temperatures for the four sites (Figure 2).

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