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Occupancy models are a vital tool for ecologists studying the patterns and drivers of species occurrence, but their use often involves selecting among models with different sets of occupancy and detection covariates. The information-theoretic approach, which employs information criteria such as Akaike’s information criterion (AIC) is arguably the most popular approach for model selection in ecology and is often used for selecting occupancy models. However, the information-theoretic approach risks selecting models that produce inaccurate parameter estimates due to a phenomenon called collider bias, a type of confounding that can arise when adding explanatory variables to a model. Using simulations, we investigated the consequences of collider bias (using an illustrative example called M-bias) in the occupancy and detection processes of an occupancy model, and explored the implications for model selection using AIC and a common alternative, the Schwarz criterion (or Bayesian information criterion, BIC). We found that when M-bias was present in the occupancy process, AIC and BIC selected models that inaccurately estimated the effect of the focal occupancy covariate, while simultaneously producing more accurate predictions of the site-level occupancy probability than other models in the candidate set. In contrast, M-bias in the detection process did not impact the focal estimate; all models made accurate inferences, while the site-level predictions of the AIC/BIC-best model were slightly more accurate. Our results show that information criteria can be used to select occupancy covariates if the sole purpose of the model is prediction, but must be treated with more caution if the purpose is to understand how environmental variables affect occupancy. By contrast, detection covariates can usually be selected using information criteria regardless of the model’s purpose. These findings illustrate the importance of distinguishing between the tasks of parameter inference and prediction in ecological modeling. Furthermore, our results underline concerns about the use of information criteria to compare different biological hypotheses in observational studies.

1. Great efforts have been made to control soil erosion by restoring plant communities in degraded ecosystems world-wide. However, soil erosion has not been substantially reduced mainly because current restoration strategies lead to large areas of mono-specific vegetation, which are inefficient in reducing soil erosion because of their simple canopy and root structure. Therefore, an advanced understanding of how community functional composition affects soil erosion processes, as well as an improved restoration scheme to reduce soil erosion, is urgently needed. 2. We investigated the effect of community functional composition on soil erosion in restored semi-arid grasslands on the Loess Plateau of China. Community functional composition of 16 restored grasslands was quantified by community-weighted mean (CWM) and functional diversity (FD) trait values, which were calculated from nine plant functional traits of thirteen locally dominant plant species. Species richness and evenness were also measured. Soil erosion rates were measured using standard erosion plots. The multimodel inference approach was used to estimate the direction and the relative importance of these biodiversity indices in reducing soil erosion. 3. A robust and strong negative effect of functional divergence (FDiv) on soil erosion was found. The prevalence of particular trait combinations can also decrease soil erosion. The greatest control over soil erosion was exerted when the community mean root diameter was small and the root tensile strength was great. 4. Synthesis and applications: These findings imply that community functional diversity plays an important role in reducing soil erosion in semi-arid restored grasslands. This means that current restoration strategies can be greatly improved by incorporating community functional diversity into restoration design. We propose a trait-based restoration framework for reducing soil erosion, termed 'SSM' (Screening-Simulating-Maintaining). SSM aims to translate the target of community functional diversity into community assemblages that can be manipulated by practitioners. Based on this framework, a comprehensive procedure, highlighting functional diversity as the primary concern in determining optimal community assemblages, was developed to meet the pressing need for more effective restoration strategies to reduce soil erosion.

1. The management of both desirable and undesirable species requires an understanding of the factors determining their distribution. Quantitative distribution models offer simple methods for formulating the species-habitat link and the means not only for predicting where species should occur, but also for understanding the factors involved. Generalized linear modelling, in particular, links the incidence of species to habitat variables, and has increasingly formed the backbone of the modelling approaches used. New 'data technologies', such as remote sensing and geographical information systems, have further broadened these modelling applications to almost any ecological system and any species for which there are distribution data. 2. Many previous approaches have aimed to identify the most parsimonious model with the best suite of predictors, selected on the basis of null hypothesis testing. However, information-theoretic approaches based on Akaike's information criterion allow the selection of a best approximating model or a subset of models from a set of candidates. Information-theoretic approaches require a deeper understanding of the biology of the system modelled and may well become an improved paradigm for species distribution modelling. 3. Synthesis and applications. This special profile of six papers demonstrates the development in methodology used in species distribution modelling. The papers show how information-theoretic approaches can be coupled with emerging data technologies to address issues of conservation significance. With conservation biology and applied ecology at the forefront of many of the basic science developments so far, we expect these methods to pervade other areas of ecological research more fully in future.

Many of California's native populations of freshwater fish are in serious decline, as are freshwater faunas worldwide. Habitat loss and alteration, hydrologic modification, water pollution, and invasions have been identified as major drivers of these losses. Because these potential causes of decline are frequently correlated, it is difficult to separate direct from indirect effects of each factor and to appropriately rank their importance for conservation action. Recently a few authors have questioned the conservation significance of invasions, suggesting that they are \"passengers\" rather than \"drivers\" of ecological change. We compiled an extensive, watershed-level data set of fish presence and conservation status, land uses, and hydrologic modifications in California and used an information theoretic approach (Akaike's information criterion, AIC) and path analysis to evaluate competing models of native fish declines. Hydrologic modification (impoundments and diversions), invasions, and proportion of developed land were all predictive of the number of extinct and at-risk native fishes in California watersheds in the AIC analysis. Although nonindigenous fish richness was the best single predictor (after native richness) of fishes of conservation concern, the combined ranking of models containing hydrologic modification variables was slightly higher than that of models containing nonindigenous richness. Nevertheless, the path analysis indicated that the effects of both hydrologic modification and development on fishes of conservation concern were largely indirect, through their positive effects on nonindigenous fish richness. The best-fitting path model was the driver model, which included no direct effects of abiotic disturbance on native fish declines. Our results suggest that, for California freshwater fishes, invasions are the primary direct driver of extinctions and population declines, whereas the most damaging effect of habitat alteration is the tendency of altered habitats to support nonindigenous fishes.

1. To develop a conservation management plan for a species, knowledge of its distribution and spatial arrangement of preferred habitat is essential. This is a difficult task, especially when the species of concern is in low abundance. In south-western Victoria, Australia, populations of the rare rufous bristlebird Dasyornis broadbenti are threatened by fragmentation of suitable habitat. In order to improve the conservation status of this species, critical habitat requirements must be identified and a system of corridors must be established to link known populations. A predictive spatial model of rufous bristlebird habitat was developed in order to identify critical areas requiring preservation, such as corridors for dispersal. 2. Habitat models generated using generalized linear modelling techniques can assist in delineating the specific habitat requirements of a species. Coupled with geographic information system (GIS) technology, these models can be extrapolated to produce maps displaying the spatial configuration of suitable habitat. 3. Models were generated using logistic regression, with bristlebird presence or absence as the dependent variable and landscape variables, extracted from both GIS data layers and multispectral digital imagery, as the predictors. A multimodel inference approach based on Akaike's information criterion was used and the resulting model was applied in a GIS to extrapolate predicted likelihood of occurrence across the entire area of concern. The predictive performance of the selected model was evaluated using the receiver operating characteristic (ROC) technique. A hierarchical partitioning protocol was used to identify the predictor variables most likely to influence variation in the dependent variable. Probability of species presence was used as an index of habitat suitability. 4. Negative associations between rufous bristlebird presence and increasing elevation, 'distance to creek', 'distance to coast' and sun index were evident, suggesting a preference for areas relatively low in altitude, in close proximity to the coastal fringe and drainage lines, and receiving less direct sunlight. A positive association with increasing habitat complexity also suggested that this species prefers areas containing high vertical density of vegetation. 5. The predictive performance of the selected model was shown to be high (area under the curve 0.97), indicating a good fit of the model to the data. Hierarchical partitioning analysis showed that all the variables considered had significant independent contributions towards explaining the variation in the dependent variable. The proportion of the total study area that was predicted as suitable habitat for the rufous bristlebird (using probability of occurrence at a ≥ 0·5 level) was 16%. 6. Synthesis and applications. The spatial model clearly delineated areas predicted as highly suitable rufous bristlebird habitat, with evidence of potential corridors linking coastal and inland populations via gullies. Conservation of this species will depend on management actions that protect the critical habitats identified in the model. A multiscale approach to the modelling process is recommended whereby a spatially explicit model is first generated using landscape variables extracted from a GIS, and a second model at site level is developed using fine-scale habitat variables measured on the ground. Where there are constraints on the time and cost involved in measuring finer scale variables, the first step alone can be used for conservation planning.

An important goal of invasion biology is to identify physical and environmental characteristics that may make a region particularly receptive to invasions. The inland waters of California (USA) are highly invaded, particularly by fishes, although there is wide variation in numbers of nonnative fishes across the state's watersheds. Here we examine patterns of fish invasions in California watersheds and their associations with natural environmental characteristics, native fish diversity, and various measures of human habitat disturbance. Our analysis is based on an extensive data set on the distribution of freshwater fishes across California's watersheds and on GIS land-use coverages for the entire state. We used canonical correspondence analysis to examine associations between environmental characteristics and the distributions of both native and nonnative fish species. We then employed an information-theoretic model-selection approach to rank hypothesized models derived from the fish invasion literature with regard to how well they predicted numbers of nonnative fishes in California watersheds. Our results indicate that pervasive, anthropogenic, landscape-level changes (particularly the extent of urban development, small-scale water diversions, aqueducts, and agriculture) influenced spatial patterns of invasion. In addition, we find that deliberately stocked fishes have different habitat associations, including a strong association with the presence of dams, than other introduced fishes. In our analysis, watersheds with the most native species also contain the most nonnative species. We find no evidence that existing watershed protection helps to prevent fish invasions in California, but we suggest that restoration of natural hydrologic processes may reduce invasion impacts.

Spontaneous recognition of human faces is a challenging problem that has install important concern from signal processing researchers in Last years. This is owing to its many uses in various fields, including security and forensic analysis. Notwithstanding this interest, face recognition is yet one of the most challenging troubles. Up to this time, no way gives a good solution to all attitudes. In this paper we present a neoteric mathematical technicality for face recognition. which we call, (ITSM), is Accredit on our lately disseminated efficient information-theoretic-statistical equation (ITSM), which Merge three mathematically balanced equations. The first one is entropic equation (EE), the second one is histogram equation (HE), and the third one is the standard statistic (SSIM). (ITSM) Tested against versus (SSIM) and (ITSSIM) beneath Gaussian noise, so we got good results even beneath a large scale of PSNR. The face recognition with (ITSM) certified on both above measures of a test image and a database images. We performed the performance evaluation with (MATLAB) using part of the Famous (AT&T) gray Image Database that made up of (49) face images, from which we chose seven person and for each one we chose seven Perspectives (poses) with different facial emotions. The Target of this paper is to present an efficient technicality for face recognition that may work in real-time milieu. Through the implementation of our information, facial recognition has been proven with a method (ITSM) Hybrid (information - theoretic-statistical) that surpasses the known statistical technicality of face recognition (SSIM) and a technicality based on information theory known as (ISSIM).

Abstract Background H3N8 canine influenza virus (CIV) infection might contribute to increased duration of shelter stay for dogs. Greater understanding of factors contributing to CIV within shelters could help veterinarians identify control measures for CIV. Objectives To assess community to shelter dog CIV transmission, estimate true prevalence of CIV, and determine risk factors associated with CIV in humane shelters. Animals 5,160 dogs upon intake or discharge from 6 US humane shelters, December 2009 through January 2012. Methods A cross-sectional study was performed with prospective convenience sampling of 40 dogs from each shelter monthly. Nasal swabs and serum samples were collected. Hemagglutination inhibition and real-time reverse transcriptase-polymerase chain reaction assays were performed for each nasal and serum sample. True prevalence was estimated by stochastic latent class analysis. Logistic regression was used to identify risk factors associated with CIV shedding and seropositivity. Results Nasal swabs were positive from 4.4% of New York (NY), 4.7% of Colorado (CO), 3.2% of South Carolina, 1.2% of Florida, and 0% of California and Texas shelter dogs sampled. Seropositivity was the highest in the CO shelter dogs at 10%, and NY at 8.5%. Other shelters had 0% seropositivity. Information-theoretic analyses suggested that CIV shedding was associated with region, month, and year (model weight = 0.95) and comingling/cohousing (model weight = 0.92). Conclusions and Clinical Importance Community dogs are a likely source of CIV introduction into humane shelters and once CIV has become established, dog-to-dog transmission maintains the virus within a shelter.

We question whether the growing popularity of model selection based on information theory (IT) and using the Akaike's Information Criterion (AIC) represent a useful paradigm shift in data analysis or a substitution of 1 statistical ritual for another, which leaves in place long-standing problems in wildlife science. We discuss the relevance of model selection in science, problems in the IT-AIC algorithm, errors of commission and omission in IT-AIC-based studies, and the role of IT-AIC in knowledge accrual. Model selection is just another minor tool in the grand panorama of science. The human mind, not statistical methods, produces scientific breakthroughs. Although IT-AIC might include elements of hypothetico-deductive science, it is arguably a form of sensitivity analysis, magnitude of effects estimation, or simple description as currently applied. Accordingly, it is largely an inductive approach to knowledge accrual and, therefore, subject to the pitfalls of induction. The algorithm tends to over fit data (i.e, use too many variables), resulting in models that contain useless variables and that generalize poorly. Errors of commission in IT-AIC-based papers include hopelessly uninformative lists of encrypted models and imposition of the model-selection approach on studies better executed in a simple, descriptive format. The major error of omission is an almost universal failure to test selected models on independent data. From our perspective, IT-AIC is a harmless human construct that is being ritualistically applied and therefore cannot be expected to correct long-standing problems in the conduct of wildlife science, such as failure to apply the hypothetico-deductive method. We view the growing application of IT-AIC as problematic because that growth might discourage use of the full panoply of available methods of inquiry. Accordingly, we urge colleagues to avail themselves of the rich pageant of available analytical techniques that can be applied in wildlife research under the hypothetico-deductive method and to keep ecology, rather than statistics, in the forefront of wildlife science.

Modeling and inference are central tomost areas of science and especially to evolving and complex systems. Critically, the information we have is often uncertain and insufficient, resulting in an underdetermined inference problem; multiple inferences, models, and theories are consistent with available information. Information theory (in particular, the maximum information entropy formalism) provides a way to deal with such complexity. It has been applied to numerous problems, within and across many disciplines, over the last few decades. In this perspective, we review the historical development of this procedure, provide an overview of the many applications of maximum entropy and its extensions to complex systems, and discuss in more detail some recent advances in constructing comprehensive theory based on this inference procedure. We also discuss efforts at the frontier of information-theoretic inference: application to complex dynamic systems with time-varying constraints, such as highly disturbed ecosystems or rapidly changing economies.