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Seafloor characteristics can help in the prediction of fish distribution, which is required for fisheries and conservation management. Despite this, only 5%–10% of the world's seafloor has been mapped at high resolution, as it is a time‐consuming and expensive process. Multibeam echo‐sounders (MBES) can produce high‐resolution bathymetry and a broad swath coverage of the seafloor, but require greater financial and technical resources for operation and data analysis than singlebeam echo‐sounders (SBES). In contrast, SBES provide comparatively limited spatial coverage, as only a single measurement is made from directly under the vessel. Thus, producing a continuous map requires interpolation to fill gaps between transects. This study assesses the performance of demersal fish species distribution models by comparing those derived from interpolated SBES data with full‐coverage MBES distribution models. A Random Forest classifier was used to model the distribution of Abalistes stellatus, Gymnocranius grandoculis, Lagocephalus sceleratus, Loxodon macrorhinus, Pristipomoides multidens, and Pristipomoides typus, with depth and depth derivatives (slope, aspect, standard deviation of depth, terrain ruggedness index, mean curvature, and topographic position index) as explanatory variables. The results indicated that distribution models for A. stellatus, G. grandoculis, L. sceleratus, and L. macrorhinus performed poorly for MBES and SBES data with area under the receiver operator curves (AUC) below 0.7. Consequently, the distribution of these species could not be predicted by seafloor characteristics produced from either echo‐sounder type. Distribution models for P. multidens and P. typus performed well for MBES and the SBES data with an AUC above 0.8. Depth was the most important variable explaining the distribution of P. multidens and P. typus in both MBES and SBES models. While further research is needed, this study shows that in resource‐limited scenarios, SBES can produce comparable results to MBES for use in demersal fish management and conservation. [ABSTRACT FROM AUTHOR]

Documenting the spatial distribution of high-quality habitat patches, the distributions of threats and protected areas, and the vulnerability of habitat patches to changes in environmental conditions is vital for conservation of rare species. Range-wide species distribution models were developed for Black Rails (Laterallus jamaicensis) to predict the distribution of high-quality habitat patches for breeding Eastern Black Rails (L. j. jamaicensis). Overlay analyses were conducted to quantify the distribution of habitat relative to human development and existing protected areas, as well as the vulnerability of the best habitat to future sea level rise. The amount of high-quality habitat varied among states (0.4-7.6% of area) and was relatively rare throughout the subspecies' range (3.3% of area). Human development was common but the amount varied spatially among states (2.2-15.3% of area). Higher-quality breeding habitat was more common on federal lands (9.4% of area) and protected areas (6.4% of area), yet 33-42% of the highest-quality habitat patches were vulnerable to sea level rise of 0.61-1.83 m. Our results imply that even though many of the highest-quality habitat patches may be less likely sites for development they are often vulnerable to rising seas, and thus maintenance of existing high-quality habitat patches may be difficult without management that takes into account the likelihood of future inundation. [ABSTRACT FROM AUTHOR]

Identification of species' Biologically Important Areas (BIAs) is fundamental to conservation planning and species distribution models (SDMs) are a powerful tool commonly used to do this. Presence‐only data are increasingly being used to develop SDMs to aid the conservation decision‐making process. The application of presence‐only SDMs for marine species' is particularly attractive due to often logistical and economic costs of obtaining systematic species' distribution data. However, robust model validation is important for conservation management applications that require accurate and reliable species' occurrence data (e.g., spatially explicit risk assessments). This is commonly done using a random subset of the data and less commonly with fully independent test data. Here, we apply a spatial block cross‐validation (CV) approach to validate a MaxEnt presence‐only model using independent presence/absence survey data for a highly mobile, marine species (humpback whale, Megaptera novaengliae) in the Great Barrier Reef (GBR). A MaxEnt model was developed using opportunistic whale sightings (2003–2007) and then used to identify areas differing in habitat suitability (low, medium, high) to conduct a systematic, line‐transect, aerial survey (2012) and derive a density surface model. A spatial block CV buffering strategy was used to validate the MaxEnt model, using the opportunistic sightings as training data and independent aerial survey sightings data as test data. Moderate performance measures indicate MaxEnt was reliable in identifying the distribution patterns of a mobile whale species on their breeding ground, indicated by areas of high density aligned to areas of high habitat suitability. Furthermore, we demonstrate that MaxEnt models can be useful and cost‐effective for designing a sampling scheme to undertake systematic surveys that significantly reduces sampling effort. In this study, higher quality information on whale reproductive class (calf vs. non‐calf groups) was obtained that the presence‐only data lacked, while sampling only 18% of the GBR World Heritage Area. The validation approach using fully independent data provides greater confidence in the MaxEnt model, which indicates significant overlap with the main breeding ground of humpback whales and the inner shipping route. This is important when evaluating presence‐only models within certain conservation management applications, such as spatial risk assessments. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are important management tools for highly mobile marine species because they provide spatially and temporally explicit information on animal distribution. Two prevalent modeling frameworks used to develop SDMs for marine species are generalized additive models (GAMs) and boosted regression trees (BRTs), but comparative studies have rarely been conducted; most rely on presence‐only data; and few have explored how features such as species distribution characteristics affect model performance. Since the majority of marine species BRTs have been used to predict habitat suitability, we first compared BRTs to GAMs that used presence/absence as the response variable. We then compared results from these habitat suitability models to GAMs that predict species density (animals per km2) because density models built with a subset of the data used here have previously received extensive validation. We compared both the explanatory power (i.e., model goodness of fit) and predictive power (i.e., performance on a novel dataset) of the GAMs and BRTs for a taxonomically diverse suite of cetacean species using a robust set of systematic survey data (1991–2014) within the California Current Ecosystem. Both BRTs and GAMs were successful at describing overall distribution patterns throughout the study area for the majority of species considered, but when predicting on novel data, the density GAMs exhibited substantially greater predictive power than both the presence/absence GAMs and BRTs, likely due to both the different response variables and fitting algorithms. Our results provide an improved understanding of some of the strengths and limitations of models developed using these two methods. These results can be used by modelers developing SDMs and resource managers tasked with the spatial management of marine species to determine the best modeling technique for their question of interest. [ABSTRACT FROM AUTHOR]

Aim: Predicting distributions is fundamental to ecology, yet hindered by spatially restricted sampling, scale‐dependent relationships and detection error associated with field surveys. Predictive species distribution models (SDMs) are nonetheless vital for conservation of many species. We developed a framework for building predictive SDMs with multi‐scale data and used it to develop range‐wide breeding‐season SDMs for 14 marsh bird species of concern. Location: USA. Methods: We built SDMs using data from range‐wide surveys conducted over 14 years, and habitat and disturbance covariates measured at multiple spatial scales. We built hierarchical occupancy models that included heterogeneity in detectability during sampling, and used Bayesian model selection to regulate model complexity (covariates and scales) based explicitly on spatial predictive abilities. We thus integrated model selection for optimizing out‐of‐sample prediction, range‐wide sampling over broad conditions, multi‐scale analyses and scale optimization, and species‐specific detectability for a suite of wide‐ranging species. Results: Distributions of marsh birds were affected by local wetland conditions, but also by agricultural, urban and hydrologic disturbances operating from local scales (100–500 m) to the watershed level. Variables measuring human disturbances improved prediction for most species, and every species was affected by attributes at >1 scale. Five species showed evidence for continental‐scale range contraction during the study. Main conclusions: We demonstrate how hierarchical occupancy models can be optimized for prediction across a species' range at the extent of a continent while also accounting for imperfect detection, and thus describe a generalizable approach that can be used for any species. We provide the first data‐driven, empirical SDMs built at the range‐wide extent for most of our 14 study species and demonstrate that previous studies focused on local distributions and the effects of fine‐scale wetland vegetation missed important broadscale drivers of occupancy for marsh birds. [ABSTRACT FROM AUTHOR]

Aim : Changes in abundance and shifts in distribution as a result of a warming climate have been documented for many marine species, but opportunities to test our ability to forecast such changes have been limited. This study evaluates the ability of habitat‐based density models to accurately forecast cetacean abundance and distribution during a novel year with unprecedented warm ocean temperatures caused by a sustained marine heatwave. Location : California Current Ecosystem, USA. Methods : We constructed generalized additive models based on cetacean sighting and environmental data from 1991 to 2009 for eight species with a diverse range of habitat associations. Models were built with three different sets of predictor variables to compare performance. Models were then used to forecast species abundance and distribution patterns during 2014, a year with anomalously warm ocean temperatures. Cetacean sighting data collected during 2014 were used to assess model forecasts. Results: Ratios of model‐predicted abundance to observed abundance were close to 1:1 for all but one species and accurately captured changes in the number of animals in the study area during the anomalous year. Predicted distribution patterns also showed good concordance with the 2014 survey observations. Our results indicate that habitat relationships were captured sufficiently to predict both changes in abundance and shifts in distribution when conditions warmed, for both cool‐ and warm‐temperate species. Main conclusions: Models built with multidecadal datasets were able to forecast abundance and distribution in a novel warm year for a diverse set of cetacean species. Models with the best explanatory power did not necessarily have the best predictive power. Also, they revealed species‐specific responses to warming ocean waters. Results have implications for modelling effects of climate change on cetaceans and other marine predators. [ABSTRACT FROM AUTHOR]

To quantify the effect of the surrounding landscape context on a biological response at a site, most studies measure landscape variables within discs centred on this biological response (threshold-based method, TBM). This implicitly assumes that the effect of a unit area of the landscape is consistent up to a threshold distance beyond which it drops to zero. However, it seems more likely that the landscape effect declines with increasing distance from the biological response point. Here, we develop a method to quantify landscape context effects by weighting the landscape variables by functions that decrease with distance. We illustrate the method using abundance data on birds and insects, and compare the results to the threshold approach., We defined distance weighting functions by the function family (e.g. negative exponential, Gaussian...) and by the parameters for this function. We developed a method to simultaneously estimate the parameters characterizing the effect of the landscape variables and the parameters of the best weighting functions. For each test dataset, we determined which weighting function (family and parameters) had the most support, by optimizing the model AIC., The distance-weighted method (DWM) improved model support over the TBM in three of four datasets, with the exponential power function selected as the best weighing function in all three cases., The observed differences between estimations of landscape context effects by the distance-weighted and the threshold methods have significant implications for landscape management. For example, the DWM suggests that managing a landscape for 90% of its effect on a focal population requires an area over five times larger than the area estimated by the threshold method, a situation that might apply for priority conservation of few remnant populations of a severely endangered species. In contrast, management for 30% of the landscape effect requires only about half the area estimated using the threshold method, a situation that might apply to a management situation with limited resources or low political/societal support., The DWM is applicable to any species-habitat relationship. More comparisons are needed to determine the situations in which distance-weighted estimation of landscape context effects is warranted over the simpler threshold method. [ABSTRACT FROM AUTHOR]

Seasonal forecasting of environmental conditions and marine species distribution has been used as a decision support tool in commercial and aquaculture fisheries. These tools may also be applicable to species targeted by the recreational fisheries sector, a sector that is increasing its use of marine resources, and making important economic and social contributions to coastal communities around the world. Here, a seasonal forecast of the habitat and density of dolphinfish ( Coryphaena hippurus ), based on sea surface temperatures, was developed for the east coast of New South Wales (NSW), Australia. Two prototype forecast products were created; geographic spatial forecasts of dolphinfish habitat and a latitudinal summary identifying the location of fish density peaks. The less detailed latitudinal summary was created to limit the resolution of habitat information to prevent potential resource over-exploitation by fishers in the absence of total catch controls. The forecast dolphinfish habitat model was accurate at the start of the annual dolphinfish migration in NSW (December) but other months (January - May) showed poor performance due to spatial and temporal variability in the catch data used in model validation. Habitat forecasts for December were useful up to five months ahead, with performance decreasing as forecast were made further into the future. The continued development and sound application of seasonal forecasts will help fishery industries cope with future uncertainty and promote dynamic and sustainable marine resource management. [ABSTRACT FROM AUTHOR]

Species distribution models ( SDMs) are used to inform a range of ecological, biogeographical and conservation applications. However, users often underestimate the strong links between data type, model output and suitability for end-use. We synthesize current knowledge and provide a simple framework that summarizes how interactions between data type and the sampling process (i.e. imperfect detection and sampling bias) determine the quantity that is estimated by a SDM. We then draw upon the published literature and simulations to illustrate and evaluate the information needs of the most common ecological, biogeographical and conservation applications of SDM outputs. We find that, while predictions of models fitted to the most commonly available observational data (presence records) suffice for some applications, others require estimates of occurrence probabilities, which are unattainable without reliable absence records. Our literature review and simulations reveal that, while converting continuous SDM outputs into categories of assumed presence or absence is common practice, it is seldom clearly justified by the application's objective and it usually degrades inference. Matching SDMs to the needs of particular applications is critical to avoid poor scientific inference and management outcomes. This paper aims to help modellers and users assess whether their intended SDM outputs are indeed fit for purpose. [ABSTRACT FROM AUTHOR]

1. The problems of analysing used-available data and presence-only data are equivalent, and this paper uses this equivalence as a platform for exploring opportunities for advancing analysis methodology. 2. We suggest some potential methodological advances in used-available analysis, made possible via lessons learnt in the presence-only literature, for example, using modern methods to improve predictive performance. We also consider the converse - potential advances in presence-only analysis inspired by used-available methodology. 3. Notwithstanding these potential advances in methodology, perhaps a greater opportunity is in advancing our thinking about how to apply a given method to a particular data set. 4. It is shown by example that strikingly different results can be achieved for a single data set by applying a given method of analysis in different ways - hence having chosen a method of analysis, the next step of working out how to apply it is critical to performance. 5. We review some key issues to consider in deciding how to apply an analysis method: apply the method in a manner that reflects the study design; consider data properties; and use diagnostic tools to assess how reasonable a given analysis is for the data at hand. [ABSTRACT FROM AUTHOR]

Restoration and conservation may be enhanced by using ecologically-based methods for prioritizing actions. Efforts are currently underway to restore river flows to the San Joaquin River in California's Central Valley. Although fish are the primary restoration target for restored flows, comple-mentary efforts are being designed to protect, enhance, and restore riparian and floodplain habitats to benefit the larger ecological community. We describe our efforts to use bird habitat distribution models to inform and prioritize conservation activities along the San Joaquin River. We demonstrate the integra-tion of habitat distribution models into an established conservation planning process that illustrates the synergies and tradeoffs of protecting high quality habitat for multiple species-groups and other restoration opportunities on the San Joaquin River. We used quantitative models to develop habitat quality indices for marsh birds, early-successional riparian birds, and mid/late-successional riparian birds, and used these indices to rank 18 sites under consideration along the San Joaquin River. We found little evidence that the rankings of the 18 sites for the three habitats were correlated, suggesting that any prioritiza-tion decisions will need to consider the quality of all three habitats, rather than one habitat acting as a surrogate for the others. Considering the habitat model rankings together with expert opinion rankings based on existing habitat quality, restoration potential, and flood management opportunities allowed us to identify sites that ranked high across multiple criteria. These results illustrate a simple process by which quantitative information from habitat models can be combined with expert opinion to inform priorities for protection and restoration. [ABSTRACT FROM AUTHOR]

Seabird populations of the Southern Ocean have been responding to climate change for the last three decades and demographic models suggest that projected warming will cause dramatic population changes over the next century. Shift in species distribution is likely to be one of the major possible adaptations to changing environmental conditions. Habitat models based on a unique long-term tracking dataset of king penguin (Aptenodytes patagonicus) breeding on the Crozet Islands (southern Indian Ocean) revealed that despite a significant influence of primary productivity and mesoscale activity, sea surface temperature consistently drove penguins' foraging distribution. According to climate models of the Intergovernmental Panel on Climate Change (IPCC), the projected warming of surface waters would lead to a gradual southward shift of the more profitable foraging zones, ranging from 25 km per decade for the B1 IPCC scenario to 40 km per decade for the A1B and A2 scenarios. As a consequence, distances travelled by incubating and brooding birds to reach optimal foraging zones associated with the polar front would double by 2100. Such a shift is far beyond the usual foraging range of king penguins breeding and would negatively affect the Crozet population on the long term, unless penguins develop alternative foraging strategies. [ABSTRACT FROM AUTHOR]

1. When invasive species are first detected in a new environment there is often a demand for information about the potential for the organism to spread and create impacts. Uraba lugens (Lepidoptera: Nolidae) is an Australian native moth that has invaded New Zealand in what are presumed to be two separate episodes. After U. lugens was found in Auckland in 2001 aclimex™ model was prepared to gauge the potential for the moth to spread and inflict damage in New Zealand. Inconsistencies in fitting model parameters to the then known native distribution, indicated that the known native distribution was probably incomplete, and that the unknown part of its range was critical for defining its likely range limits in New Zealand. 2. A synthetic sex pheromone trapping survey was used to ascertain the altitudinal and upper rainfall range limits of U. lugens in part of its native range in Australia. The survey extended the known range of U. lugens into higher-altitude and higher-rainfall zones of Tasmania. We used the expanded distribution information to refine aclimex model, and to project the climate suitability for U. lugens with particular emphasis on New Zealand. 3. The projected climatic suitability of New Zealand indicates that the area likely to be at risk of invasion by U. lugens is considerably more extensive than was indicated from its historical distribution records in Australia. It now includes all the eucalypt forestry areas. Similarly, the global potential distribution covers the major eucalypt forestry regions of the world. 4. The minimum heat sum necessary to complete a generation for U. lugens that was associated with its range limits was considerably lower than that predicted by average development rates gained from constant temperature and daylength studies. Possible explanations for this anomaly are discussed. 5. Synthesis and applications. The lack of suitable data on the distribution of organisms is perhaps the single most common challenge for ecological climate modellers. Trapping along climatic gradients with a synthetic pheromone lure offers a cheap, rapid means of ascertaining the climatic distribution of suitable high profile insects. [ABSTRACT FROM AUTHOR]

Questions: Can a statistical model be designed to represent more directly the nature of organismal response to multiple interacting factors? Can multiplicative kernel smoothers be used for this purpose? What advantages does this approach have over more traditional habitat modelling methods? Methods: Non-parametric multiplicative regression (NPMR) was developed from the premises that: the response variable has a minimum of zero and a physiologically-determined maximum, species respond simultaneously to multiple ecological actors, he response to any one factor is conditioned by the values of other factors, and that if any of the factors is intolerable then the response is zero. Key features of NPMR are interactive effects of predictors, no need to specify an overall model form in advance, and built-in controls on overfitting. The effectiveness of the method is demonstrated with simulated and real data sets. Results: Empirical and theoretical relationships of species response to multiple interacting predictors can be represented effectively by multiplicative kernel smoothers. NPMR allows us to abandon simplistic assumptions about overall model form, while embracing the ecological truism that habitat factors interact. [ABSTRACT FROM AUTHOR]

Abstract: Increasing use is being made in conservation management of statistical models that couple extensive collections of species and environmental data to make predictions of the geographic distributions of species. While the relationships fitted between a species and its environment are relatively transparent for many of these modeling techniques, others are more ‘black box’ in character, only producing geographic predictions and providing minimal or untraditional summaries of the fitted relationships on which these predictions are based. This in turn prevents robust evaluation of the ecological sensibility of such models, a necessary process if model predictions are to be treated with confidence. Here we propose a new but simple method for visualizing modeled responses that can be implemented with any modeling method, and demonstrate its application using five common methods applied to the prediction of an Australian tree species. This is achieved by insetting an “evaluation strip” into the spatial data layers, which, after predictions have been made, can be clipped out and used for creating plots of the modelled responses. We present findings of the application strip for algorithms GLMs, GAMs, CLIM, DOMAIN and MARS. Evaluation strips can be constructed to investigate either uni-variate responses, or the simultaneous variation in predicted values in relation to two variables. The latter option is particularly useful for evaluating responses in models that allow the fitting of complex interaction terms. [Copyright &y& Elsevier]

The article reports on models of habitat distribution. It mentions the nonviability of research on distributions at fine resolutions over landscapes due to lack of time and resources. It also notes its utilization for evaluating the possible effects of poor water table depth and environmental changes on the distribution of the butterfly Plebejus argus.

Geographic distributions are a basic component of a species' ecology, and predicting distributions is a fundamental task of conservation and resource management. Reliable prediction depends on identification of appropriate scales of effect for environmental data, and scale‐optimization techniques are thus desirable to identify optimal scales for predictor variables. Recent statistical developments have also advanced methods of model selection based explicitly on predictive ability, which differ from commonly used methods that regulate model structures via anticipated predictive performance. Such methods are beginning to permeate into species distribution models (SDMs), yet there remains no consensus methodology for developing optimally predictive multi‐scale SDMs when covariate data are collected over a range of scales. Thus, we compared the performance of common approaches for scale optimization and model selection in terms of their ability to produce optimally predictive multi‐scale Bayesian occupancy models for predicting a species distribution, using models of the breeding distribution for King Rails (Rallus elegans) as a case study. Our results demonstrate sizable gains in predictive performance for hierarchical occupancy models selected via their ability to predict out‐of‐sample data using the logarithmic scoring rule, as compared to models selected using information criteria (deviance information criteria [DIC] and Watanabe information criteria [WAIC]). Information criteria commonly selected individual covariates, as well as scales of effect for those covariates, with suboptimal predictive performance. Performance of models selected using the logarithmic scoring rule was also robust across the method of scale optimization, which was not true for models selected using DIC and WAIC. Thus, we demonstrate empirical benefits of study designs and statistical tools that enable covariate and scale selection based explicitly on predictive ability. Our results also imply that more careful consideration of what constitutes an optimal scale is warranted in many ecological applications, as the meaning of optimal is not independent of the technique used for scale selection. [ABSTRACT FROM AUTHOR]