Hi, you can perform BIOENV routine (PRIMER Package); see .pdf file enclosed.

Thank you so much for your response.

You can also do GLM with post-hoc tests, step-wise regression, or Pearson correlation for density dependent (or independent) population-environmental studies.

See:

J Insect Sci. 2015; 15(1): 58.

Published online 2015 May 14. doi: 10.1093/jisesa/iev041

PMCID: PMC4535478

Density, Demography, and Influential Environmental Factors on Overwintering Populations of Sogatella furcifera (Hemiptera: Delphacidae) in Southern Yunnan, China.

Thank you so much for your response. I'll check it out.

I will strongly recommend Linear models (LM) or Generalized linear model (GLM) over any other correlational analysis for one simple reason; the first is that if two or more variables from the environment  (predictor variables) are highly correlated, that normally could happen when you calculate environment as percentage from a total, then you could not control for that in the other methods, but with LM and GLM you could check for that with the variance inflation factor (VIF) and stepwise model selection, for selecting the best model explaining the relationship.  The second is that this models take in account different distribution models that your data could have. Normal(Gaussian) for LM or Poisson, binomial or gamma for GLM. So as you could see they are very robust for analize your data. I hope this help you.

I am totally agree with Andrew and Patricia, you can also perform GAMs (Generalized Additive Models) (Leps and Smilauer,

2003) GAMs are an extension of the GLMs that, unlike more conventional regression methods, do not require the assumption of a particular shape for the response variable distribution along the environmental gradient, being a

flexible and powerful analytical tool when assessing nonlinear relationships; for this analysis you can run CANOCO or R software.

This is a paper including GAMs: Nebra, A., Alcaraz, C., Caiola, N., Muñoz-Camarillo, G., & Ibáñez, C. (2016). Benthic macrofaunal dynamics and environmental stress across a salt wedge Mediterranean estuary. Marine environmental research, 117, 21-31.

Leps, J., Smilauer, P., 2003. Multivariate Analysis of Ecological Data Using CANOCO.

Hi

First, what kind of data do you have? both of your variables are numerical?, do they present normal distribution?, Homoscedasticity? 

You can try a linear regression or regressions with GLMs, only if both of your variables are numerical. If not you can try one way anova or GLM with factors.

You can perform one model for each pair of variables you have.

Hope can help :)

ou can also do GLM with post-hoc tests, step-wise regression, or Pearson correlation for density dependent (or independent) population-environmental studies.

See:

J Insect Sci. 2015; 15(1): 58.

Published online 2015 May 14. doi: 10.1093/jisesa/iev041

PMCID: PMC4535478

Density, Demography, and Influential Environmental Factors on Overwintering Populations of Sogatella furcifera (Hemiptera: Delphacidae) in Southern Yunnan, China.

What statistical tools can be used in determining the relationship between certain environmental factors and O. quadrasi snail population density?. Available from: https://www.researchgate.net/post/What_statistical_tools_can_be_used_in_determining_the_relationship_between_certain_environmental_factors_and_O_quadrasi_snail_population_density?tpr_view=y4mvZetM8BdjvAWqJwS8gSM0AljxxR3ktP84_2 [accessed Apr 26, 2017].

Thank you so much for all your responses! :)

Here are some software Statistica, Minitab and R

Thank you so much for the response!