There is an old mantra in statistics: "your model is only as good as your data". In seeing your comment following the original question, it sounds like you are more interested in the power and effancy of your data than the actual model. It would be nice if you indicated what algorithms/models you are exploring and what outcome you desire. With this information, relevant recommendations could be provided regarding pros and cons of a given modeling approach. Some methodologies produce better estimates of 1st order variation whereas others produce 2nd order estimation that represents finer scale patterns. Statistically, both of these estimates would be valid.
I am not going to bother with singling out papers because, with a brief literature search you will find many papers comparing SDM methods, including a paper asking the question "do we need another SDM methods comparison". I agree with the paper questioning the need for additional comparisons. This is because I believe that we are asking the incorrect questions around performance and completely ignoring issues associated with expectations, data and extrapolation. Given your issue, this is exactly what you should be asking.
Following this opinion, I would recommend selecting a methodology that would produce sensible results and is easy to automate. This is because, ideally you could implement a Monte Carlo approach to produce many "quasi-random" estimates to quantify the spatial uncertainty in the estimate, say using the variance across replicates. Personally, in informal testing of MaxEnt (which I am not fond of and find somewhat invalid), given many runs of the model, the probability surfaces produced regions of uncertainty equating to +/- 1 standard deviation, which is worse than random and illustrates issues with spatial and statistical extrapolation of the model and potential instability in how the null is generated.
This brings up another issue in how the null is produced. For example, the MaxEnt software produces a fixed set of random or systematic samples. This is very unsatisfactory because there are no spatial constraints applied to the randomization. The model could perform very differently given a different null. This is something that could be explicitly explored in a Monte Carlo, as one of the simulation conditions.
The above point regarding MaxEnt stresses the need to narrow candidate models down to ones that meet your specific objectives, which can be variable in SDM's. Given a preference towards inference (or MaxEnt), I would direct one to Poisson Point Process models which provide a Bayesian framework to solve models using an MCMC. This would give one considerable inference around the results. For nonparametric methods the Random Forests and Kernel SVM models provide considerable power for binominal probability estimations. However, if one wants to explicitly incorporate spatial process into the model alternative approaches must be implemented adapting existing approach (eg., kernel weights in random forests) or selecting a model specifically designed for this purpose (eg., spatial autoregressive, conditional autoregressive, mixed effects models).
I would add that, since this data is produced via a return survey, it would be prudent for you to explore occupancy modeling which addresses observational bias and, in turn, adjusts probability of occurrence.