To study the impact of weather variables on the nesting population of a bird species, multiple regression, generalized linear models (GLMs), or mixed-effects models (GLMMs) should be used, depending on the data structure, distribution, and presence of temporal or spatial dependencies.

Hello Divya Kumari, I guess you must have climate variables so rainfall, temperature as continuous measures. Do you have categorical variables such as catastrophic fire rating % of the month. Then you must have other variables related to the birds ie; weight of the bird, bird species etc.

@Nadeem Akhtar, thank you, but Is there any reliable source to learn the process and result interpretation of GLM and GLMM in SPSS ?

@Deborah J Hilton, I have climate variables and population data for two years, I do not have any categorical variables. In this case, to observe the impact of these variables, how do I study and structure the data?

Good morning Divya Kumari, I'm just going to work. Do you have my other work website on google pages. There is a favourite links page. I think it is the last URL which is a good reference. If you can't find it please advise.

To study the impact of weather variables on the nesting population of a bird species, you should choose a statistical test based on: 1. Your research question (Are you testing for relationships or differences?)

2. Data types (Are your variables continuous, categorical, or a mix?)

3. Assumptions (Normality, independence, linearity, etc.) Possible Approaches & Tests 1. Correlation Analysis (If exploring relationships)

- Pearson’s r → If both weather variables (e.g., temperature, rainfall) and nesting population are continuous and normally distributed.

- Spearman’s rank → If data is non-parametric (not normally distributed or ordinal). 2. Regression Analysis (If predicting nesting population based on weather)

- Simple Linear Regression → One weather variable (e.g., temperature) predicting nesting numbers.

- Multiple Linear Regression → Multiple weather variables (e.g., temperature + rainfall + wind speed) predicting nesting population.

- Generalized Linear Models (GLMs) → If nesting data is count-based (Poisson regression) or binary (Logistic regression for presence/absence). 3. Time-Series Analysis (If data is collected over time)

- ARIMA models → If nesting populations and weather vary seasonally/annually.

- Cross-correlation function (CCF) → To check lagged effects (e.g., does rainfall 3 months prior affect nesting?). 4. Non-parametric Alternatives (If assumptions fail)

- Mann-Whitney U / Kruskal-Wallis → If comparing nesting numbers between categorical weather groups (e.g., dry vs. wet season). Recommended Starting Point

Since weather variables are likely continuous predictors and nesting population is a response variable, multiple regression (or GLM) is often the best choice. If weather effects are time-lagged, consider time-series models.