Climate and Weather

Another important component of our models will be measures of short-term and long-term weather conditions. For short-term variables (weather), we consider the minimum, mean, and maximum temperature, total precipitation, and mean relative humidity on the day an individual was captured as well as the cumulative daily precipitation for the year up to the capture date. Long-term variables (climate) include the average monthly (mean) temperature, average monthly precipitation, and the average monthly rain days (as a proportion) across 7 years (2017 - 2024) at a site. Additionally, coefficients of variation are for the climate variables were also considered.

To reduce complexity in our weather and climate information, we conducted PCA for both short-term (weather) and long-term (climate) data. Positive values of the first weather PC are associated with warmer and wetter observations (Fig 4) and the first two PCs account for 73% of the variation in the weather data (Table 8). Positive values of the first climate PC are associated with cooler, dryer, and more variable sites (Fig 5) and the first two PCs explain 94% of the climatic variation among sites (Table 9).

Figure 4: Principal component ordination of weather on the day of an individual capture.

Table 8: Importance of weather principal components.

	PC1	PC2	PC3	PC4	PC5	PC6
Eigenvalue	2.99	1.40	0.89	0.60	0.11	0.01
Proportion Explained	0.50	0.23	0.15	0.10	0.02	0.00
Cumulative Proportion	0.50	0.73	0.88	0.98	1.00	1.00

Figure 5: Principal component ordination of 7-year climate at a NEON site.

Table 9: Importance of climate principal components.

	PC1	PC2	PC3	PC4	PC5	PC6
Eigenvalue	3.19	2.47	0.15	0.13	0.05	0.01
Proportion Explained	0.53	0.41	0.03	0.02	0.01	0.00
Cumulative Proportion	0.53	0.94	0.97	0.99	1.00	1.00