Test a significant difference between two slope values The data I have are a regression slope value of y~time, a standard error, an n value and a p value, for a particular species in two different areas.  I want to check whether the the regression slope for one area is significantly different from the regression slope for the other area - is this possible with such data?  Does anyone have any suggestions how I could go about this?  I can't get access to the raw data  unfortunately...
Sorry that this is such a simple question!
 A: If the slopes come from ordinary least squares regression, it would be good to verify that the year-to-year data which generated these values are indeed independent. Most capture-recapture studies need to account for previous years' volumes using some method of handling the dependence of volume over time. 
Using standard errors, you can construct confidence intervals around your slope parameters. A naive test for whether they are different at the correct $\alpha$ level is to inspect whether any of the confidence intervals overlap. (Note the confidence interval from one parameter has to overlap the other actual parameter value, not its confidence interval, in order to fail to reject the null hypothesis that they're different).
A: The classic (and more statistically powerful) way of testing this is to combine both datasets into a single regression model and then include the area as an interaction term. See, for example, here: 
http://www.theanalysisfactor.com/compare-regression-coefficients/
A: The following article might be helpfull to you, as it describes how to evaluate if the effect of a given explanatory factor is invariant over persons, time, or organizations:
Paternoster, R., Brame, R., Mazerolle, P., & Piquero, A. R. (1998). Using the Correct Statistical Test for the Equality of Regression Coefficients. Criminology, 36(4), 859–866.
What they basically say is, that to test the hypothesis that the difference between $b_1$ and $b_2$ (1 and 2 being two samples or times) is equal to zero you can apply the following formula:
$\begin{equation} Z=  \frac{b_1-b_2}{\sqrt{{SEb_1}^{2}+{SEb_2}^2}} \end{equation}$
SE being the standard error of the respective 'slopes' in your case. 
