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Estimating the prevalence or the absolute probability of the presence of a species from presence‐background data has become a controversial topic in species distribution modelling. In this paper, we propose a new method by combining both statistics and machine learning algorithms that helps overcome some of the known existing problems. We have also revisited the popular but highly controversial Lele and Keim (LK) method by evaluating its performance and assessing the RSPF condition it relies on. Simulations show that the LK method with the RSPF assumptions would render fragile estimation/prediction of the desired probabilities. Rather, we propose the local knowledge condition, which relaxes the predetermined population prevalence condition that has so often been used in much of the existing literature. Simulations demonstrate the performance of the new method utilizing the local knowledge assumption to successfully estimate the probability of presence. The local knowledge extends the local certainty or the prototypical presence location assumption, and has significant implications for demonstrating the necessary condition for identifying absolute (rather than relative) probability of presence from presence background without absence data in species distribution modelling. [ABSTRACT FROM AUTHOR]

Use-availability and presence-only analyses are synonyms. Both require two samples (one containing known locations, one containing potential locations), both estimate the same parameters, and both use the same fundamental likelihood., Use-availability and presence-only designs compare characteristics of points where an organism was located to those where the organism could have been located. These designs can be generalized to estimate the relative probability that any event occurred at a set of locations., This article generalizes the use-availability likelihood given in Johnson et al. (Resource selection functions based on use-availability data: theoretical motivation and evaluation methods, Journal of Wildlife Management, 2006) to point locations. This derivation arrives at the same likelihood as Fithian & Hastie (Statistical Models for Presence-Only Data: Finite-Sample Equivalence and Addressing Observer Bias, 2012) but uses a different technique and allows a more general link function. Fithian & Hastie (2012) use a case-control argument and Bayes theorem to derive the likelihood. This article uses Lagrangian multipliers to maximize the two-sample likelihood., Resource selection functions (RSF) defined here are ratios of density functions. RSFs must be positive and unbounded. Proper link functions must provide proportionality over their entire range. Given these conditions, the exponential link is the most logical and appropriate link function for RSFs. These conditions exclude the logistic link., This article affirms that estimation of a RSF does not involve 'running logistic regression'. By assigning 0 and 1 (pseudo-)responses to vectors of covariates associated with locations in the used and available sample, it is possible to 'trick' logistic regression software into maximizing the use-availability likelihood. Representing the analysis as 'logistic regression' is misleading because that implies use of the logistic link, which is inappropriate for RSF's. It is more accurate to state that the 'use-availability likelihood was maximized'., RSFs are more general, intuitive and useful than resource selection probability functions (RSPF). RSPFs depend heavily on sampling mechanisms and the number of used and available locations selected. Consequently, the objective of estimation in use-availability studies should be the RSF, not the RSPF., Two simple examples and R code in the Supporting Information illustrate computations. These examples maximize the general log likelihood without the aid of logistic regression software. [ABSTRACT FROM AUTHOR]

Following the introductions carried out in late 1960s, Eastern cottontail Sylvilagus floridanus Allen, 1890 rapidly colonized the Po Plain (northern Italy), following the Po River and its tributaries. We monitored a cottontail population using the line-transect method from autumn 2005 to spring 2009 in a 8.2-km2 study area located along the Po River, and we investigated species habitat requirements by assessing the presence/absence of faecal pellets in 200 randomly distributed plots from September 2006 to August 2007 and by Resource Selection Probability Function through logistic regression analyses and multi-model inference. The cottontail population varied dramatically over time in size, with a great drop at the end of the breeding period. Cottontails selected foraging habitats at the macro- and micro-scales, with some differences among seasons. Two macro-habitat variables differed significantly between used and unused plots through seasons: arboriculture stands were always greater in presence plots, whereas winter cereals were always greater in absence ones. On the macro-level, woody and herbaceous habitats, such as fallow fields, characterized presence plots. At the micro-habitat level, presence plots were associated with permanent dense cover except during summer. Several logistic regression models were built through seasons and ranked using the Akaike's Information Criterion. Arboriculture stands enhanced cottontail presence mostly during the growing season contrary to crop fields. Hedgerows were used according to availability during feeding activity. Cottontail habitat selection varied according to seasonal changes in resource availability and suitability of the different habitat types. [ABSTRACT FROM AUTHOR]

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Changes in mature forest cover amount, composition, and configuration can be of significant consequence to wildlife populations. The response of wildlife to forest patterns is of concern to forest managers because it lies at the heart of such competing approaches to forest planning as aggregated vs. dispersed harvest block layouts. In this study, we developed a species assessment framework to evaluate the outcomes of forest management scenarios on biodiversity conservation objectives. Scenarios were assessed in the context of a broad range of forest structures and patterns that would be expected to occur under natural disturbance and succession processes. Spatial habitat models were used to predict the effects of varying degrees of mature forest cover amount, composition, and configuration on habitat occupancy for a set of 13 focal songbird species. We used a spatially explicit harvest scheduling program to model forest management options and simulate future forest conditions resulting from alternative forest management scenarios, and used a process-based fire-simulation model to simulate future forest conditions resulting from natural wildfire disturbance. Spatial pattern signatures were derived for both habitat occupancy and forest conditions, and these were placed in the context of the simulated range of natural variation. Strategic policy analyses were set in the context of current Ontario forest management policies. This included use of sequential time-restricted harvest blocks (created for Woodland caribou (Rangifer tarandus) conservation) and delayed harvest areas (created for American marten (Martes americana atrata) conservation). This approach increased the realism of the analysis, but reduced the generality of interpretations. We found that forest management options that create linear strips of old forest deviate the most from simulated natural patterns, and had the greatest negative effects on habitat occupancy, whereas policy options that specify deferment and timing of harvest for large blocks helped ensure the stable presence of an intact mature forest matrix over time. The management scenario that focused on maintaining compositional targets best supported biodiversity objectives by providing the composition patterns required by the 13 focal species, but this scenario may be improved by adding some broad-scale spatial objectives to better maintain large blocks of interior forest habitat through time.