First off, you will need to correct the bias in your GPS telemetry locations. It is a bit dangerous to throw data like this at a species distribution model. You very likely have transient locations that are not, in fact, part of the utilization distribution function or niche. You will need to asses the frequency of habitat use based on your telemetry locations before building any statistical relationships. Commonly this is done via a specialized Kernel density estimate. There are some robust models available in the R ade libraries. These libraries also have ordination methods available for understanding habitat characteristics. The R learning curve aside the ade libraries are your best bet for dealing with your telemetry data and quantifying habitat utilization.
If you wish to actually derive a species distribution (niche) model the you will need to generate pseudo absence data to act as a null in a statistical model. I would highly recommend not using MaxEnt as it is exactly mathematically equivalent to a GLM regression and decomposes into a conditional logistic regression. As such, the nonparametric qualities that have previously been assumed are quite incorrect and given complex, nonlinear or autocorrelated data, the results very biased.
I included some references to start you down the path of analyzing your telemetry data and species distribution modeling.
References:
Basille, M., Calenge, C., Marboutin, E., Andersen, R. and Gaillard, J.M. (2008) Assessing habitat selection using multivariate statistics: Some refinements of the ecological-niche factor analysis. Ecological Modelling, 211:233-240.
Benhamou, S. (2011) Dynamic approach to space and habitat use based on biased random bridges. PLOS ONE, 6:1-8.
Benhamou, S. and Cornelis, D. (2010) Incorporating movement behavior and barriers to improve kernel home range space use estimates. Journal of Wildlife Management. 74:1353-1360.
Bingham, R. and Brennan, L. (2004) Comparison of type I error rates for statistical analyses of resource selection Journal of Wildlife Management,
68:206-212.
Bullard, F. (1999) Estimating the home range of an animal: a Brownian bridge approach. Thesis, Johns Hopkins University.
Calenge, C., Dufour, A. and Maillard, D. (2005) K-select analysis: a new method to analyse habitat selection in radio-tracking studies Ecological Modelling, 186:143-153.
Calenge, C. and Dufour, A. (2006) Eigenanalysis of selection ratios from animal radio-tracking data. Ecology, 87:2349-2355.
Calenge, C., Darmon, G., Basille, M., Loison, A. and Jullien, J. (2008) The factorial decomposition of the Mahalanobis distances in habitat selection studies. Ecology, 89:555-566.
Darmon, G., Calenge, C., Loison, A., Jullien, J.M., Maillard, D. and Lopez, J.F. (2012) Spatial distribution and habitat selection in coexisting species of mountain ungulates. Ecography, 35:44-53.
Dray, S., Royer-Carenzi, M. and Calenge, C. (2010) The exploratory analysis of autocorrelation in animal-movement studies. Ecological Research, 4:34-41.
Evans J.S., M.A. Murphy, Z.A. Holden, S.A. Cushman (2011). Modeling species distribution and change using Random Forests in Predictive Species and Habitat Modeling in Landscape Ecology: Concepts and Applications. eds Drew CA, YF Wiersma, F Huettmann. Springer, NY
Fauchald, P. and Tveraa, T. (2003) Using _rst-passage time in the analysis of area-restricted search and habitat selection. Ecology, 84:282-288.
Gueguen, L. (2001) Segmentation by maximal predictive partitioning according to composition biases. pp 32-44 in: Gascuel, O. and Sagot, M.F. (Eds.), Computational Biology, LNCS, 2066.
Hegel T., S.A. Cushman, F. Huettmann, and J.S. Evans (2010) Current State of the Art for Statistical Modeling of Species Distributions. Chapter 16 in Spatial Complexity, Informatics and Wildlife Conservation eds. F Huettmann and S.A. Cushman. pp. 273-311. Springer, New York.
Hengl, T., H. Sierdsema, A. Radovic, and A. Dilo (2009) Spatial prediction of species distributions from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging. Ecological Modelling, 220(24):3499-3511
Horne, J., Garton, E., Krone, S. and Lewis, J. (2007) Analyzing animal movements using Brownian bridges. Ecology, 88:2354-2363.
Keating, K. and Cherry, S. (2009) Modeling utilization distributions in space and time. Ecology, 90:1971-1980.
Kenward, R., Clarke, R., Hodder, K. and Walls, S. (2001) Density and linkage estimators of home range: nearest neighbor clustering defines multinuclear
cores. Ecology, 82:1905-1920.
Lavielle, M. (2005) Using penalized contrasts for the change-point problem. Signal Processing, 85:1501-1510.
Lavielle, M. (1999) Detection of multiple changes in a sequence of dependent variables. Stochastic Processes and their Applications. 83:79-102.
Martin, J., Calenge, C., Quenette, P.Y. and Allaine, D. (2008) Importance of movement constraints in habitat selection studies. Ecological Modelling, 213:257-262.
Renner, I.W., and D.I. Warton (2013) Equivalence of MAXENT and Poisson Point Process Models for Species Distribution Modeling in Ecology. Biometrics Online early DOI: 10.1111/j.1541-0420.2012.01824.x
