The EXAMINE project collected data from 50 suction trap locations across Europe to explore environmental and landscape factors affecting aphid flight timing and abundance. This dataset specifically focuses on the Julian day of the first capture of the aphid species Myzus persicae at each site in 1995. Explanatory variables include geographical information (latitude, longitude, altitude), ten meteorologicalvariables (monthly rainfall from October 1994 to May 1995, mean temperature for the coldest 30-day period, and mean temperature for the subsequent 60-day period), and eight land-use variables representing the proportion of land within a 75 km radius used for different purposes (such as forest types, agricultural land, urban areas, and water bodies). These factors were selected for their potential influence on aphid migration patterns, with earlier flight dates expected in warmer and drier regions. The dataset enables analysis of how geography, climate, and landscape usage affect the seasonal timing of aphid arrival across Europe.
Format
A data frame with 23 variables: Trap, JDay, Latitude, Longitude, Altitude, OctRain, NovRain, DecRain, JanRain, FebRain, MarRain, AprRain, MayRain, C30Day, F60Day, ConForest, DecForest, MixForest, Grassland, Arable, InlandWater, Sea, Urban.
- Trap
Factor. Unique identifier for each suction trap location.
- JDay
Integer. Julian day of first catch of Myzus persicae at the site in 1995.
- Latitude
Numeric. Latitude (in decimal degrees) of the trap site.
- Longitude
Numeric. Longitude (in decimal degrees) of the trap site.
- Altitude
Integer. Altitude (in meters above sea level) of the trap site.
- OctRain
Numeric. Rainfall (mm) at the trap site in October 1994.
- NovRain
Numeric. Rainfall (mm) at the trap site in November 1994.
- DecRain
Numeric. Rainfall (mm) at the trap site in December 1994.
- JanRain
Numeric. Rainfall (mm) at the trap site in January 1995.
- FebRain
Numeric. Rainfall (mm) at the trap site in February 1995.
- MarRain
Numeric. Rainfall (mm) at the trap site in March 1995.
- AprRain
Numeric. Rainfall (mm) at the trap site in April 1995.
- MayRain
Numeric. Rainfall (mm) at the trap site in May 1995.
- C30Day
Numeric. Mean temperature (°C) for the coldest consecutive 30-day period at the site.
- F60Day
Numeric. Mean temperature (°C) for the following 60-day period after the coldest period at the site.
- ConForest
Numeric. Proportion of land (within 75 km radius) under coniferous forest.
- DecForest
Numeric. Proportion of land (within 75 km radius) under deciduous forest.
- MixForest
Numeric. Proportion of land (within 75 km radius) under mixed forest.
- Grassland
Numeric. Proportion of land (within 75 km radius) as grassland.
- Arable
Numeric. Proportion of land (within 75 km radius) as arable land.
- InlandWater
Numeric. Proportion of land (within 75 km radius) as inland water.
- Sea
Numeric. Proportion of area (within 75 km radius) that is sea.
- Urban
Numeric. Proportion of land (within 75 km radius) classified as urban area.
Source
Welham, S. J., Gezan, S. A., Clark, S. J., and Mead, A. (2015) Statistical Methods in Biology: Design and analysis of experiments and regression
Examples
step(lm(JDay ~ . - Trap, data = examine), direction = "backward")
#> Start: AIC=284.22
#> JDay ~ (Trap + Latitude + Longitude + Altitude + OctRain + NovRain +
#> DecRain + JanRain + FebRain + MarRain + AprRain + MayRain +
#> C30Day + F60Day + ConForest + DecForest + MixForest + Grassland +
#> Arable + InlandWater + Sea + Urban) - Trap
#>
#> Df Sum of Sq RSS AIC
#> - JanRain 1 0.2 6102.8 282.22
#> - AprRain 1 7.1 6109.7 282.28
#> - F60Day 1 54.3 6156.9 282.67
#> - DecRain 1 95.3 6197.9 283.00
#> - C30Day 1 133.5 6236.1 283.30
#> - InlandWater 1 142.6 6245.2 283.38
#> - Altitude 1 210.0 6312.6 283.91
#> <none> 6102.6 284.22
#> - Latitude 1 287.1 6389.7 284.52
#> - Grassland 1 294.2 6396.8 284.58
#> - MarRain 1 339.1 6441.7 284.93
#> - FebRain 1 350.9 6453.5 285.02
#> - Longitude 1 365.7 6468.3 285.13
#> - Urban 1 439.7 6542.3 285.70
#> - MixForest 1 443.1 6545.7 285.73
#> - ConForest 1 464.3 6566.9 285.89
#> - NovRain 1 744.3 6846.9 287.98
#> - Sea 1 825.9 6928.5 288.57
#> - Arable 1 874.1 6976.7 288.92
#> - OctRain 1 1631.7 7734.3 294.07
#> - DecForest 1 2353.1 8455.7 298.53
#> - MayRain 1 4501.5 10604.1 309.85
#>
#> Step: AIC=282.22
#> JDay ~ Latitude + Longitude + Altitude + OctRain + NovRain +
#> DecRain + FebRain + MarRain + AprRain + MayRain + C30Day +
#> F60Day + ConForest + DecForest + MixForest + Grassland +
#> Arable + InlandWater + Sea + Urban
#>
#> Df Sum of Sq RSS AIC
#> - AprRain 1 7.9 6110.7 280.29
#> - F60Day 1 66.0 6168.7 280.76
#> - InlandWater 1 144.3 6247.0 281.39
#> - C30Day 1 198.3 6301.1 281.82
#> - Altitude 1 226.7 6329.5 282.05
#> - DecRain 1 227.5 6330.3 282.05
#> <none> 6102.8 282.22
#> - Latitude 1 310.5 6413.3 282.70
#> - Grassland 1 326.4 6429.2 282.83
#> - Longitude 1 382.7 6485.5 283.26
#> - MarRain 1 416.8 6519.6 283.53
#> - FebRain 1 427.3 6530.1 283.61
#> - Urban 1 441.2 6543.9 283.71
#> - MixForest 1 481.6 6584.4 284.02
#> - ConForest 1 523.1 6625.9 284.34
#> - Sea 1 857.8 6960.6 286.80
#> - Arable 1 937.9 7040.7 287.37
#> - NovRain 1 1067.8 7170.6 288.29
#> - OctRain 1 1636.1 7738.9 292.10
#> - DecForest 1 2355.8 8458.6 296.55
#> - MayRain 1 4505.3 10608.1 307.87
#>
#> Step: AIC=280.29
#> JDay ~ Latitude + Longitude + Altitude + OctRain + NovRain +
#> DecRain + FebRain + MarRain + MayRain + C30Day + F60Day +
#> ConForest + DecForest + MixForest + Grassland + Arable +
#> InlandWater + Sea + Urban
#>
#> Df Sum of Sq RSS AIC
#> - F60Day 1 58.1 6168.8 278.76
#> - InlandWater 1 137.2 6247.9 279.40
#> - C30Day 1 204.8 6315.5 279.94
#> - DecRain 1 227.8 6338.5 280.12
#> <none> 6110.7 280.29
#> - Altitude 1 257.9 6368.6 280.36
#> - Grassland 1 320.2 6430.9 280.84
#> - Latitude 1 437.0 6547.7 281.74
#> - Urban 1 440.4 6551.1 281.77
#> - MarRain 1 477.0 6587.7 282.05
#> - MixForest 1 527.6 6638.3 282.43
#> - ConForest 1 534.8 6645.5 282.48
#> - FebRain 1 570.1 6680.8 282.75
#> - Longitude 1 622.5 6733.2 283.14
#> - Sea 1 853.1 6963.8 284.82
#> - Arable 1 931.2 7041.9 285.38
#> - NovRain 1 1373.1 7483.8 288.42
#> - OctRain 1 1746.7 7857.4 290.86
#> - DecForest 1 2533.2 8643.9 295.63
#> - MayRain 1 4562.5 10673.2 306.17
#>
#> Step: AIC=278.76
#> JDay ~ Latitude + Longitude + Altitude + OctRain + NovRain +
#> DecRain + FebRain + MarRain + MayRain + C30Day + ConForest +
#> DecForest + MixForest + Grassland + Arable + InlandWater +
#> Sea + Urban
#>
#> Df Sum of Sq RSS AIC
#> - InlandWater 1 102.9 6271.7 277.59
#> - DecRain 1 217.6 6386.4 278.50
#> <none> 6168.8 278.76
#> - Grassland 1 310.6 6479.3 279.22
#> - Urban 1 434.7 6603.5 280.17
#> - MarRain 1 463.0 6631.8 280.38
#> - FebRain 1 586.9 6755.7 281.31
#> - ConForest 1 590.2 6759.0 281.33
#> - MixForest 1 601.8 6770.6 281.42
#> - Altitude 1 802.6 6971.4 282.88
#> - Longitude 1 832.8 7001.6 283.09
#> - Sea 1 861.0 7029.8 283.29
#> - Arable 1 915.3 7084.1 283.68
#> - NovRain 1 1508.4 7677.2 287.70
#> - OctRain 1 1688.7 7857.4 288.86
#> - C30Day 1 1735.9 7904.7 289.16
#> - DecForest 1 3116.7 9285.5 297.21
#> - Latitude 1 4021.3 10190.1 301.86
#> - MayRain 1 4509.2 10678.0 304.20
#>
#> Step: AIC=277.59
#> JDay ~ Latitude + Longitude + Altitude + OctRain + NovRain +
#> DecRain + FebRain + MarRain + MayRain + C30Day + ConForest +
#> DecForest + MixForest + Grassland + Arable + Sea + Urban
#>
#> Df Sum of Sq RSS AIC
#> - Grassland 1 209.5 6481.2 277.23
#> - DecRain 1 217.4 6489.0 277.29
#> <none> 6271.7 277.59
#> - ConForest 1 488.0 6759.6 279.33
#> - MixForest 1 501.5 6773.2 279.44
#> - MarRain 1 546.2 6817.8 279.76
#> - Urban 1 667.3 6939.0 280.64
#> - FebRain 1 745.6 7017.3 281.20
#> - Altitude 1 781.1 7052.7 281.46
#> - Sea 1 839.7 7111.4 281.87
#> - Arable 1 861.2 7132.8 282.02
#> - Longitude 1 1061.7 7333.3 283.41
#> - NovRain 1 1413.9 7685.6 285.75
#> - OctRain 1 1627.6 7899.3 287.12
#> - C30Day 1 1683.1 7954.8 287.48
#> - DecForest 1 3048.3 9320.0 295.39
#> - MayRain 1 4454.9 10726.6 302.42
#> - Latitude 1 4554.4 10826.1 302.88
#>
#> Step: AIC=277.23
#> JDay ~ Latitude + Longitude + Altitude + OctRain + NovRain +
#> DecRain + FebRain + MarRain + MayRain + C30Day + ConForest +
#> DecForest + MixForest + Arable + Sea + Urban
#>
#> Df Sum of Sq RSS AIC
#> - DecRain 1 230.9 6712.0 276.98
#> <none> 6481.2 277.23
#> - ConForest 1 287.8 6769.0 277.40
#> - MixForest 1 388.2 6869.4 278.14
#> - MarRain 1 649.7 7130.8 280.01
#> - Sea 1 712.8 7193.9 280.45
#> - Arable 1 795.7 7276.9 281.02
#> - Urban 1 831.8 7313.0 281.27
#> - Altitude 1 874.1 7355.2 281.56
#> - FebRain 1 1112.2 7593.3 283.15
#> - Longitude 1 1155.5 7636.6 283.44
#> - NovRain 1 1432.3 7913.5 285.21
#> - C30Day 1 1581.2 8062.3 286.15
#> - OctRain 1 2165.2 8646.3 289.64
#> - DecForest 1 3110.6 9591.8 294.83
#> - MayRain 1 4427.8 10909.0 301.27
#> - Latitude 1 4519.5 11000.6 301.68
#>
#> Step: AIC=276.98
#> JDay ~ Latitude + Longitude + Altitude + OctRain + NovRain +
#> FebRain + MarRain + MayRain + C30Day + ConForest + DecForest +
#> MixForest + Arable + Sea + Urban
#>
#> Df Sum of Sq RSS AIC
#> - ConForest 1 134.0 6846.1 275.97
#> <none> 6712.0 276.98
#> - MixForest 1 288.4 7000.4 277.08
#> - MarRain 1 446.2 7158.2 278.20
#> - Sea 1 522.2 7234.3 278.73
#> - Arable 1 564.8 7276.9 279.02
#> - Altitude 1 808.8 7520.8 280.67
#> - Urban 1 809.9 7522.0 280.68
#> - Longitude 1 1609.4 8321.4 285.73
#> - C30Day 1 1637.1 8349.2 285.89
#> - NovRain 1 1886.8 8598.8 287.37
#> - FebRain 1 2590.9 9302.9 291.30
#> - DecForest 1 2881.5 9593.5 292.84
#> - OctRain 1 3067.2 9779.2 293.80
#> - Latitude 1 4337.5 11049.5 299.91
#> - MayRain 1 6597.5 13309.6 309.21
#>
#> Step: AIC=275.97
#> JDay ~ Latitude + Longitude + Altitude + OctRain + NovRain +
#> FebRain + MarRain + MayRain + C30Day + DecForest + MixForest +
#> Arable + Sea + Urban
#>
#> Df Sum of Sq RSS AIC
#> - MixForest 1 244.4 7090.4 275.72
#> <none> 6846.1 275.97
#> - Sea 1 415.1 7261.2 276.91
#> - Arable 1 437.3 7283.4 277.07
#> - MarRain 1 535.5 7381.6 277.74
#> - Altitude 1 738.0 7584.0 279.09
#> - Urban 1 1095.7 7941.8 281.39
#> - C30Day 1 1631.6 8477.7 284.66
#> - NovRain 1 1753.3 8599.3 285.37
#> - Longitude 1 2299.8 9145.9 288.45
#> - DecForest 1 2764.4 9610.4 290.93
#> - OctRain 1 2943.5 9789.5 291.85
#> - FebRain 1 2979.5 9825.6 292.04
#> - Latitude 1 4732.0 11578.0 300.24
#> - MayRain 1 6465.3 13311.3 307.22
#>
#> Step: AIC=275.72
#> JDay ~ Latitude + Longitude + Altitude + OctRain + NovRain +
#> FebRain + MarRain + MayRain + C30Day + DecForest + Arable +
#> Sea + Urban
#>
#> Df Sum of Sq RSS AIC
#> <none> 7090.4 275.72
#> - Arable 1 369.0 7459.5 276.26
#> - MarRain 1 372.6 7463.0 276.28
#> - Sea 1 456.8 7547.2 276.85
#> - Urban 1 1057.1 8147.5 280.67
#> - Altitude 1 1160.7 8251.1 281.30
#> - NovRain 1 1754.4 8844.8 284.78
#> - Longitude 1 2056.8 9147.3 286.46
#> - C30Day 1 2095.1 9185.5 286.67
#> - OctRain 1 2701.5 9792.0 289.87
#> - FebRain 1 2740.7 9831.1 290.06
#> - DecForest 1 3595.9 10686.3 294.24
#> - Latitude 1 4488.6 11579.0 298.25
#> - MayRain 1 7027.5 14117.9 308.16
#>
#> Call:
#> lm(formula = JDay ~ Latitude + Longitude + Altitude + OctRain +
#> NovRain + FebRain + MarRain + MayRain + C30Day + DecForest +
#> Arable + Sea + Urban, data = examine)
#>
#> Coefficients:
#> (Intercept) Latitude Longitude Altitude OctRain NovRain
#> -127.04850 5.04506 1.81366 0.05987 -0.33638 0.36199
#> FebRain MarRain MayRain C30Day DecForest Arable
#> 0.40695 -0.16988 -0.59253 -5.86472 164.39112 18.81644
#> Sea Urban
#> 24.80381 -162.52820
#>