#### Sampling

Consider a sampling situation where a GPS collar collects an animal's locations at a fixed sampling intensity (e.g., one location every 3 h) to provide *T* locations over a specific period of time. Also suppose that the two-dimensional space available to the collared animal is correctly identified, and a random sample of *i* (*i *=* *1, 2, …, *n*) sampling units is drawn from the available space. The sampling units should have complete spatial coverage across the study area, but can be of any shape (e.g., rectangular, circular, hexagonal), provided they are all the same size (e.g., circular units with 200-m radii). A simple random sample with replacement of sampling units (Fig. 1) is a common method for spatial sampling, but can result in clumps of overlapping units or gaps between sampled areas. Sampling units that are systematically spaced with a random start (Fig. 1) will generally provide a better representation of the study area and in some circumstances will improve precision (Manly 2009).

Regardless of the shape of the sampling units and whether they are selected by a random or systematic sample, it is important to note that the total count of animal locations across the sampled units should not be guaranteed to sum to *T*. If the total count of animal locations within the selected sampling units is known, this represents multinomial sampling rather than Poisson sampling (Ramsey and Schafer 2012). Multinomial sampling requires the more cumbersome multinomial model that uses multiple equations to estimate the probability of each outcome – that is, (Cameron and Trivedi 1998). As the multinomial model uses multiple equations it requires larger sample sizes, and interpretation can be difficult. Thus, for the purposes of RSF estimation we recommend a Poisson sampling approach where the total sum of counts within the sampling units is not known in advance, and a Poisson or NB distribution can be used for modeling. A simple random sample of potentially overlapping sampling units, or a systematic sample with a random start of nonoverlapping sampling units with unsampled areas in between will ensure that the counts of animal locations within all the sampling units is not guaranteed to sum to *T* (Fig. 1). Furthermore, locations included in more than one sampling unit or locations not captured in any of the sampling units will not bias model coefficients, as they are randomly determined under the proposed method.

Evaluation of many factors can help determine an appropriate size for the sampling units, but in general, the sampling units should be small enough to detect changes in animal movements while providing counts of animal locations that approximate a NB distribution (see 'Discussion'). Most importantly, the error in the animal locations and the derived covariates should be considered when determining an appropriate sampling unit size. Sampling units should be larger than the expected spatial error in the animal locations and covariate values. We recommend investigating multiple sizes of sampling units prior to modeling, although we caution against data mining for statistically significant results.

#### Statistical model

The number of animal locations (not the number of animals) in each sampling unit provides an unbiased measure of the frequency of animal use during the study period, provided environmental conditions such as canopy cover or terrain ruggedness do not prevent *T* locations from being recorded (Nielson et al. 2009). Count data from Poisson sampling are typically modeled using Poisson or NB regression (Cameron and Trivedi 1998). Poisson regression presumes a vector *Y* of non-negative counts with a variance that is equal to the mean (i.e., E[*Y*] = *u*, and var(*Y*) = *u*). However, because observed count data almost always have a variance larger than the mean (White and Bennetts 1996; Cameron and Trivedi 1998), NB regression usually provides a better representation of observed count data because it allows the variance to exceed the mean. Various NB model parameterizations exist, and distinctions are made based on the link function used and the assumed distribution of var(*Y*). For example, a NB1 formulation (log link) assumes var[*Y*] = *u *+ *u*/*θ*. Theta (*θ*) is often referred to as the dispersion parameter, but in the NB1 formulation *u*/*θ* is the amount of overdispersion, or additional variation in the data relative to a Poisson distribution. One might typically assume *θ* is constant, but it could also be a function of the habitat characteristics and modeled as such, although supplying a covariate model for *θ* is not a standard option in many NB regression model fitting routines and we do not discuss it further here.

The NB2 (log link) is the most common parameterization (Cameron and Trivedi 1998), which specifies that var[*Y*] = *u *+ *u*^{2}/*θ* (Hilbe 2008). The NB2 regression model is represented by

- (1)

where *t*_{i} is the total number of GPS locations within sampling unit *i* during the study period, β_{0} is an intercept term, β_{1}, β_{2}, …, β_{p} are coefficients to be estimated, *x*_{1i}, …, *x*_{pi} are the values of *p* covariates measured on sampling unit *i*, and *E*[.] denotes the expected value.

Modeling counts of use is acceptable, but it is often preferable to make inference to the relative frequency distribution of animal locations within the study area during the study period, also known as the utilization distribution (UD; Kernohan et al. 2001). With a slight adjustment to the NB model (eq. (1)) we can change the response from counts to relative frequencies. Inclusion of an offset term, ln(*T*), in equation (1) results in

- (2)

which is equivalent to

- (3)

Thus, the offset term simply scales the response to ensure modeling the relative frequency of use (e.g., 0, 0.003, 0.006, …) instead of integer counts (e.g., 0, 1, 2, …). Because the standard definition of probability is “the long run relative frequency of occurrence,” we can also refer to predictions of relative frequencies as estimates of probabilities of use by the monitored animal(s) during the study period.

Predictions using equation (1) represent counts of use. Predictions from equation (3) represent relative frequencies, or approximations of probabilities of use and thus the model is considered a resource selection probability function (RSPF; Manly et al. 2002). If the sampling units are allowed to overlap or spatial gaps exist between the units (Fig. 1), predictions are not subject to a unit-sum constraint, but should be within the (0, 1) interval.

Standard statistical software packages such as R (R Development Core Team 2012) and SAS (SAS Institute, Cary, NC) can easily fit NB regression models. These software packages estimate NB regression models using maximum likelihood (ML), or a simplification allowed for exponential-based models using iteratively reweighted least-squares algorithms. Thus, information theoretic approaches such as Akaike's Information Criteria (AIC; Burnham and Anderson 2002) can be used for variable selection or to compare competing models.

#### Assumptions

A key assumption to any study of resource selection is that the sample of animal locations is representative of the group of animals for which inference is desired. For GPS studies where fix-rate success is poor and the realized number of recorded locations is <*T* (i.e., Pr[detection] <1), then alternative approaches may be needed to account for missing locations and habitat-induced fix-rate bias (Frair et al. 2004; Nielson et al. 2009; Augustine et al. 2011). Another obvious, but easily overlooked assumption of the proposed modeling approach is that the counts from the sampling units follow the NB distribution chosen (e.g., the NB2 distribution). We discuss three assumptions for application of most NB distributions, although some assumptions can be relaxed for certain distributions (e.g., truncated NB, zero-inflated NB). First, the data cannot be censored or truncated. Violation of this assumption is not anticipated in the RSF setting, as it would only occur when a portion of the locations are ignored (e.g., counts >7 are set to 7). However, truncated NB models are available if necessary. For example, if the sampling design does not permit the possibility of zero counts, a zero-truncated NB model (Hilbe 2008) should be used. Second, the sample units should not contain an excess of zeros. Unfortunately, there is no clear rule for how large the counts can be or what constitutes an excess of zeros. Most NB distributions can allow for very large counts and many zeros. For example, based on a NB2 distribution with a mean of *u *=* *5 and *θ *= 0.2, the probability of getting a zero count within a sampling unit is 0.52, and the probability of getting a count >50 is 0.013.

Alternative models have been developed to accommodate an excessive number of zero counts, including the hurdle and zero-inflated NB models (Hilbe 2008). Both of these models assume that the data can be separated into two distributions. The hurdle model assumes that a binary process determines whether a count should be >0, and then a count process generates the actual count (≥1) for those units. The zero-inflated NB model allows for modeling of zero counts using a mixture of binary and count processes – that is, zeros could have come from either the binary or count process. Use of either the hurdle model or the zero-inflated NB requires assuming that some of the responses have to be zero, regardless of sampling intensity (fix schedule and/or study length). This implies that either all (hurdle) or a portion (zero inflated) of the sampling units with zero counts were either not available to the study animal(s), or that it was not possible to detect the animal(s) in those sampling units. Obviously, use of these models requires thoughtful justification which should not be based solely on the proportion of zero counts in the data. Proper identification of what is available to the animal may preclude use of these more complicated models.

The third assumption deals with independence and requires that data are not structured as panels (clusters; Hilbe 2008). For example, longitudinal data (GPS fixes) often come in panels (Hilbe 2008), and each sampled animal represents a panel, provided the sample of animals was not clustered (e.g., several animals from same group). In this situation, we may expect spatial correlation in habitat use within each panel, and “within-panel correlation will result in overdispersed data,” (Hilbe 2008) which will result in underestimation of model SEs and inflated Type I error rates. An informal goodness of fit can reveal the potential for panel data. This test involves comparing the residual deviance, *D*, to the model degrees of freedom (df = *n* − [*p *+* *1]), with *D* significantly greater than df being evidence of groupings in the data or other potential violations of the other assumptions listed above. Accounting for panel data in this sampling scheme is the same as treating the animal (or family group) as the “experimental unit” (Thomas and Taylor 2006). This can be done by fitting a separate model to each experimental unit and then averaging coefficients and calculating the SE of the mean coefficient (e.g., Marzluff et al. 2004; Sawyer et al. 2006). If some animals, by chance, have too few locations within the selected sampling units to fit equation (3) (e.g., the counts do not follow a NB2 distribution), we recommend use of other methods such as random-effects models, generalized estimating equations, and bootstrapping (see 'Sampling').

Finally, we emphasize that temporal independence of the animal locations is not a requirement of the NB RS(P)F presented here because the sampling units provide the response variable, and those units have no associated time stamp other than the period in which the study was conducted. Computationally, this is an appealing attribute of the NB RS(P)F approach because temporal correlation is not considered a nuisance, nor does it have to be explicitly modeled (see Fieberg et al. 2010). We note, however, that animal locations should be sampled at the same temporal frequency throughout the period of interest (i.e., similar fix-rate schedule), unless the locations are weighted appropriately (Fieberg et al. 2010).

#### Example

We illustrate the NB RSPF using 4,911 locations from 10 GPS-collared Rocky Mountain elk (*Cervus elaphus*) in the Starkey Experimental Forest and Range (hereafter Starkey), the site of long-term ungulate research within a landscape-scale enclosure of 7768 ha (Wisdom et al. 2005) in northeastern Oregon, USA. This research was conducted following review and approval by the Starkey Institutional Animal Care and Use Committee, as required by the Animal Welfare Act of 1985 and its regulations. Researchers specifically followed protocols established by the Starkey Institutional Animal Care and Use Committee for conducting deer and elk research at Starkey (Wisdom et al. 1993).

The store-on-board GPS collars (GPS 4400M; Lotek Wireless Inc., Newmarket, Ontario, Canada) were programed to attempt location fixes every hour between 1 August and 17 August 2010, and then every 5 min from 18 August through 21 August 2010. For this example, we used all location data prior to 18 August along with the first location obtained every hour after 17 August, resulting in a sample of locations with consistent coverage during the study period. The GPS fix success was >99%, and 94% of the locations were three-dimensional.

To estimate frequency of use we took a systematic sample of 502 nonoverlapping circular sampling units with 200-m radii and calculated the number of elk locations within each unit. Because the sampling units were much larger than the expected error in the GPS locations (<20 m), we were not concerned about the location error affecting model results. For this example, we fit a regression model with four covariates: (1) distance to nearest road, (2) mean percent slope, (3) distance to cover-forage edge, (4) and mean soil depth (cm). All distances were measured in km from the center of each circular sampling unit and based on landscape features within 4 km of the study area. Thus, we considered that roads and cover-forage edges directly outside of the enclosure could have affected the distribution of elk.

We estimated a NB RSPF using the glm.nb function based on the NB2 formulation available in the MASS contributed package (Venables and Ripley 2002) for the R software. The offset term in the model was ln(4911), which represents the natural log of the total number of recorded locations summed over all animals. We chose to pool the data across animals to estimate model coefficients and account for among-animal variation by bootstrapping the individual animals 1000 times, making the RSPF a marginal model where the variability between and within animals is collapsed. The SD of the 1000 estimates for each parameter was our estimate of the SE for each coefficient, and the central 90% of the distribution for each coefficient was used as the 90% CI (Manly 2007).

Of the 4911 elk locations collected, 4240 occurred within the 502 sampling units. The mean count of locations in the sampling units was 8, and ranged from 0 to 105. Approximately 31% of the sample units had 0 animal locations. Based on the model fit to the count data, the ML estimate of *θ *= 0.424. A NB2 distribution with these parameters should have, on average, 27% zero values, which is close to the observed proportion. The ratio of the observed residual deviance to the residual df was 1.1, indicating minor overdispersion not explained by the model, which we expected due to the panel nature of the location data.

Estimates of coefficients (Table 1) for the regression model suggest that intensity of use was highest in areas with steeper slopes, deep soils, away from roads, and close to the cover-forage edge (Table 1). Standard errors based on bootstrapping were generally more conservative (larger) than those estimated using the standard model output ignoring the panel nature of the counts (Table 1).

Table 1. Elk resource use modeling results, with coefficients, SEs based on maximum likelihood (ML) and bootstrapping, and 90% percentile confidence intervals based on bootstrappingCovariate | Estimate | ML (SE) | Bootstrap (SE) | 90% Confidence interval |
---|

Lower limit | Upper limit |
---|

Intercept | −8.025 | NA | NA | NA | NA |

Distance (km) to road | 0.545 | 0.172 | 0.277 | 0.141 | 1.041 |

Mean % slope | 0.002 | 0.008 | 0.018 | −0.026 | 0.033 |

Distance (km) to cover-forage edge | −0.355 | 0.284 | 0.369 | −0.985 | 0.246 |

Mean soil depth (cm) | 0.022 | 0.003 | 0.003 | 0.018 | 0.028 |

The odds ratio for distance from road indicated that probability of elk use was expected to increase by for every 1 km increase in distance from road out to a maximum of 2 km. The odds ratio for mean slope indicated that for every 1-unit increase in mean % slope between 0 and 50% there was an expected 0.24% increase in elk use. Odds ratios also indicated that probability of elk use was expected to decline by 30% for every 1 km increase in distance to cover-forage edge, out to a maximum distance of 1.2 km, and elk use increased by 2.23% for every 1 cm increase in soil depth between 0 and 185 cm. Marginal plots (Fig. 2) illustrate how predicted elk use changed across the range of the observed data. Ninety percent prediction intervals were calculated using the 1000 bootstrap replicates to create a distribution of predictions for each level of each covariate.