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The oceanographic conditions of the Southern California Bight (SCB) dictate the distribution and abundance of prey resources and therefore the presence of mobile predators, such as goose‐beaked whales (Ziphius cavirostris). Goose‐beaked whales are deep‐diving odontocetes that spend a majority of their time foraging at depth. Due to their cryptic behavior, little is known about how they respond to seasonal and interannual changes in their environment. This study utilizes passive acoustic data recorded from two sites within the SCB to explore the oceanographic conditions that goose‐beaked whales appear to favor. Utilizing optimum multiparameter analysis, modeled temperature and salinity data are used to identify and quantify these source waters: Pacific Subarctic Upper Water (PSUW), Pacific Equatorial Water (PEW), and Eastern North Pacific Central Water (ENPCW). The interannual and seasonal variability in goose‐beaked whale presence was related to the variability in El Niño Southern Oscillation events and the fraction and vertical distribution of the three source waters. Goose‐beaked whale acoustic presence was highest during the winter and spring and decreased during the late summer and early fall. These seasonal increases occurred at times of increased fractions of PEW in the California Undercurrent and decreased fractions of ENPCW in surface waters. Interannual increases in goose‐beaked whale presence occurred during El Niño events. These results establish a baseline understanding of the oceanographic characteristics that correlate with goose‐beaked whale presence in the SCB. Furthering our knowledge of this elusive species is key to understanding how anthropogenic activities impact goose‐beaked whales. [ABSTRACT FROM AUTHOR]

An ecohydraulic model system was developed to evaluate the effects of the Da‐Wei dam construction and possible fish restoration strategies. The ecohydraulic model system consists of a hydrodynamic model, a hydromorphology model, a habitat model, and the logistic and matrix population models. The effects of dam construction and fish stocking strategy on fish habitat and population status were studied in the Jiao‐Mu River, providing the modeled ecological status of Schizothorax prenanti and Schizothorax plagiostomus heckel populations within the river segment. The two fish species richness, fish age structures, and fish density distributions were predicted. Based on the fish number prediction, the optimal number of fish to stock was evaluated with the population models, and an optimal fish stocking proposition was worked out. The results indicated that the two fish species habitat quality and fish richness would decrease due to the hydropower dam construction, and the fish stocking strategy could make up the fish species ecological status. The findings could facilitate the quantity and quality assessment of a dam development plan and increase the success rate of fish restoration. [ABSTRACT FROM AUTHOR]

Global change has altered the nature of disturbance regimes, and megafire events are increasingly common. Megafires result in immediate changes to habitat available to terrestrial wildlife over broad landscapes, yet we know surprisingly little about how such changes shape space use of sensitive species in habitat that remains. Functional responses provide a framework for understanding and predicting changes in space use following habitat alteration, but no previous studies have assessed functional responses as a consequence of megafire. We studied space use and tested for functional responses in habitat use by breeding greater sage‐grouse (Centrocercus urophasianus) before and after landscape‐level changes induced by a >40,000 ha, high‐intensity megafire that burned sagebrush steppe in eastern Idaho, USA. We also incorporated functional responses into predictive resource selection functions (RSFs) to map breeding habitat before and after the fire. Megafire had strong effects on the distribution of available resources and resulted in context‐dependent habitat use that was heterogeneous across different components of habitat. We observed functional responses in the use and selection of a variety of resources (shrubs and herbaceous vegetation) for both nesting and brood rearing. Functional responses in the use of nesting habitat were influenced by the overarching effect of megafire on vegetation, whereas responses during brood rearing appeared to be driven by individual variation in available resources that were conditional on nest locations. Importantly, RSFs built using data collected prior to the burn also had poor transferability for predicting space use in a post‐megafire landscape. These results have strong implications for understanding and predicting how animals respond to a rapidly changing environment, given that increased severity, frequency, and extent of wildfire are consequences of global change with the capacity to reshape ecosystems. We therefore demonstrate a conceptual framework to better understand space use and aid habitat conservation for wildlife in a rapidly changing world. [ABSTRACT FROM AUTHOR]

Understanding species' associations with physical habitat conditions is a fundamental goal of ecology. For organisms that occupy lotic ecosystems, relationships to streamflow are of particular importance, but these associations are unstudied for most species. We tested the predictability of fish–microhabitat relationships in river shoals (shallow, rocky areas with relatively swift water flow) using a large data set from the Conasauga River in Georgia, USA. Our objective was to assess the consistency of species‐specific relationships with flow‐dependent variables (depth, velocity, Reynolds number and Froude number) while accounting for other microhabitat variables (e.g. vegetation). We used data from 8285 seine‐sets collected during late summer or autumn at 26 sites over 12 years to relate occurrence and counts of 22 fish species to habitat variables using generalised linear multiple regression models. Results showed that microhabitat models explained a substantial amount of the variation in counts for some species, although other species were poorly predicted. We classified 16 species as velocity specialists and nine species as depth specialists, with six species specialised for depth and velocity and three species classified as depth and velocity generalists. The variability in habitat associations that we observed suggests that species will be unevenly affected by anthropogenic activities that alter flows. [ABSTRACT FROM AUTHOR]

Long‐term browning has resulted in increases in dissolved organic carbon and reduced water clarity that have altered the vertical physical structure of many lake ecosystems. The primary responses include reduced ultraviolet (UV) penetration, warming surface waters, and decreased deepwater dissolved oxygen concentrations that interactively alter vertical habitat suitability for zooplankton.Over 3 decades, Daphnia populations have decreased in abundance and shallowed in their vertical distribution in temperate Lake Giles (Pennsylvania, USA). Using 3 decades of corresponding long‐term vertical profile data of UV radiation, water temperature, and dissolved oxygen, we modelled both the suitable vertical habitat and thermally optimal habitat for Daphnia to understand the potential role of habitat availability for their population dynamics.The vertical extent of suitable habitat increased over time primarily due to strong decreases in UV penetration. In contrast, thermally optimal habitat decreased due to strong increases in vertical thermal gradients that were especially strong in late summer.The vertical distribution of Daphnia became shallower over this time period and may be a response to lower UV exposure near the surface, but continued warming of surface waters and decreasing deepwater dissolved oxygen concentrations are likely to lead to a vertical habitat squeeze.The biological implications of long‐term browning require more attention due to the complex and important implications for population dynamics, species interactions, and food web structure in lakes. [ABSTRACT FROM AUTHOR]

Worldwide, many productive rivers are dam‐regulated and rely on flow management strategies that must balance support of ecological processes with human water use. One component of evaluating this balance is to understand ecological consequences of alternative flow management strategies, which has often been accomplished by coupling population dynamics models with models that relate streamflow to habitat availability and quality. Numerous methods assign habitat availability to locations within a river basin: These include fine‐scale field‐measured values that are extrapolated to other locations within the basin having similar physical characteristics or equation‐driven values created by functions of model‐predicted values of physical characteristics. The array of options for creating habitat models is evolving rapidly as high‐resolution remote‐sensing data becomes more accessible and computational capacity improves. Our objective was to identify trade‐offs among approaches that assign habitat relationships to large rivers and to create a decision support tool to supplement choices of extent and granularity. Using a selection of case studies that represent a breadth of scales and diverse trade‐offs, we demonstrate the need for a transparent process of data evaluation and assessment to determine the appropriate fit for model scope or context that best supports management needs and recognize sources of uncertainty. The structured approach proposed here aims at improving future model development and refine population dynamics models that inform the management of rivers. [ABSTRACT FROM AUTHOR]

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]

Using the gridded fishery data to estimate the habitat preferences of bigeye tuna (Thunnusobesus) in the Indian Ocean is challenging, as it is still not clear what type of model is appropriate to make reliable habitat predictions. In this study, we tested two classes of habitat models: Generalized Additive Models (including Gaussian distribution GAM, Poisson distribution GAM, Negative Binomial distribution GAM, Tweedie class distribution GAM, and Zero‐inflated distribution GAM) and Maximum Entropy Model (MaxEnt) for forecasting the habitat of bigeye tuna in the Indian Ocean, using the 5°×5° monthly gridded fishery data from 2008 to 2015 provided by Indian Ocean Tuna Commission (IOTC) and the environmental factors (i.e., the vertical temperature, salinity, dissolved oxygen concentration, thermocline depth, vertical shear of ocean current, concentration of sea surface chlorophyll‐a (chl‐a), and the eddy kinetic energy (EKE)). We compared the models' fitting ability, predictive performance, predicted results, and the ecological interpretation within the models. The results showed (1) GAMs provided better model fit and predictive performance than MaxEnt; (2) GA‐GAM showed the best model fit performance by using the log transformation of the standardized CPUE as response variable; (3) TW‐GAM was suitable for predicting the habitat of adult bigeye tuna with the over‐dispersed fishery datasets. (4) Results suggested that the vertical temperature, dissolved oxygen concentration, and thermocline depth could be used as reliable predictors in forecasting the spatial‐temporal distribution of the adult bigeye tuna in the Indian Ocean. [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]

We investigated the effect of environmental conditions on Atlantic mackerel spawning habitat in the southern Gulf of St. Lawrence (sGSL). Based on generalized additive models, we (i) modelled the optimal spawning habitat of mackerel in the sGSL using daily egg production (DEP) in June, (ii) predicted known and new potential present spawning habitats in the GSL and the north‐west Atlantic, and (iii) assessed how they respond to future climate change. Our findings showed that both mackerel presence–absence and given‐presence DEP were associated with sea surface temperature (10–16.5°C), salinity above 31 and depth < 120 m. Adding zooplankton showed a marked effect on the DEP given‐presence compared to the presence–absence. Predictions of spawning habitats under present (1999–2012) and future scenario (2066–2085) conditions were estimated from the presence–absence model without zooplankton, using physical conditions of the BNAM. Under present conditions, our model predicted well the main spawning habitat in the sGSL and other known secondary spawning habitats in the northern GSL (nGSL), the western and southern Newfoundland, and the north‐west Atlantic coast. Under future conditions, our study suggests that spawning habitats in the sGSL and the nGSL would expand. Our results, therefore, suggest that mackerel could benefit from a warmer GSL, minimizing the potential for a northward migration of the stock due to decreasing suitability of the sGSL as its main spawning ground, and a new but spatially limited potential habitat in Newfoundland coasts. These results can be used to inform stock management and develop adaptive management plans in the context of climate change. [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]

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Abstract: An eco‐hydraulic model is described, namely, “WW‐Eco‐tools.” The model is composed of hydro‐morpho‐dynamic, habitat, and population models. Fish habitat suitability models assess habitat quality, based on abiotic parameters, namely, flow velocity, depth, and substratum data. These are all derived from a hydro‐morpho‐dynamic model. The relationships between parameters and habitat features are represented as suitability index curves (SI curves) or fuzzy rules. To dynamically simulate fish species, two different population models are developed. The first is converted from a logistic population concept. Its model parameters are related to time‐dependent fish habitat conditions, namely, weighted usable areas and an overall suitability index. The second model is based on a matrix population concept, with numbers as the only state vector. Age‐specific fecundities and survival rates depend on the habitat qualities defined in the matrix population model. The eco‐hydraulic model provides very promising results, highlighting the fundamental role of temporal variability of hydro‐morphological parameters in structuring habitat and populations of fish species. Using this software to anticipate water management changes, simulated population trends can help decision‐makers optimize management measures. [ABSTRACT FROM AUTHOR]

Abstract: Atlantic salmon, Salmo salar L., parr habitat characterisation is usually performed by in situ measures of key environmental variables taken at the exact fish location if the fishing gear allows precise pinpointing of this location, or in large sampling sections covering a river reach or mesohabitat, often ignoring variability in the immediate vicinity around individual fish. These data may be critically important in the development and validation of habitat preference models. The influences of seven increasing distances of measures, the variation of the number of considered measures and the depth of velocity measurement (bottom or 0.6 of the depth) in the calculations of HSI (Habitat Suitability Index) from a multiple‐experts fuzzy model of Atlantic salmon parr habitat were tested. When a parr was present, six measures collected in a 50‐cm radius around the fish to provide an average measure as input data and velocity measured at 60% of the depth gave the highest HSI values. These results show some potential for the use of an intermediate study scale, between micro‐ and mesohabitat, and questions how fish habitat conditions are currently measured. [ABSTRACT FROM AUTHOR]

Aim To develop a more ecologically realistic approach for estimating the population size of cetaceans and other highly mobile species with dynamic spatial distributions. Location California Current Ecosystem, USA. Methods Conventional spatial density models assume a constant relationship between densities and habitat covariates over some time period, typically a survey season. The estimated population size must change whenever total habitat availability changes. For highly mobile long-lived species, however, density-habitat relationships likely adjust more rapidly than population size. We developed an integrated population-redistribution model based on a more ecologically plausible alternative hypothesis: (1) population size is effectively constant over each survey season; (2) if habitat availability changes, then the population redistributes itself following an ideal free distribution process. Thus, the estimated relationship between densities and habitat covariates adjusts rather than population size. We constructed Bayesian hierarchical models corresponding to the conventional and alternative hypotheses and applied them to distance sampling data for Dall's porpoise ( Phocoenoides dalli), a highly mobile cetacean with distribution patterns closely tied to cool sea-surface temperatures. Results The Dall's porpoise data provided strong support for the hypothesis based on an ideal free redistribution process. Our results indicate that the population size of Dall's porpoise within the survey region was relatively stable over each summer/fall survey season, but the distribution expanded and contracted with the extent of suitable habitat. Over multiple survey seasons, the model partitioned variation in observed densities among three sources: variation in population size, the density-habitat relationship and measurement error, leading to lower and more ecologically plausible estimates of interannual variation in population size. Main conclusions We conclude that the integrated population-redistribution model ( IPRM) presented here represents an ecologically plausible model for use in future assessments of the population size and dynamics of cetaceans and other highly mobile long-lived species with variable spatial distributions. [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]

The North Pacific spiny dogfish (SPD), Squalus suckleyi, is a commercially exploited shark species that plays an important role in the ecosystem. To elucidate the distribution of the SPD in the North Pacific and to evaluate the effects of sea surface temperature (SST) and prey availability on its distribution, we estimated the probability of SPD presence using a generalized additive model with a binomial error distribution from SPD presence/absence data on 14,824 operations in fisheryindependent gillnet surveys between 1972 and 2011. The habitat model was structured in the east and west to reflect differences in the North Pacific oceanic environments. In the east, a higher probability of SPD presence was identified along the coast from the eastern Gulf of Alaska to Queen Charlotte Sound. In the west, it was identified around northern Japan. The estimated distribution was continuous between the two areas, whereas the probability of SPD presence was relatively low. Although the probability of SPD presence was higher at SSTs between 6°C and 12°C, the SST at the peak probability of SPD presence differed between the west and east. The prey species, Japanese sardine, Sardinops melanostictus, and walleye pollock, Gadus chalcogrammus, in the west and boreal clubhook squid, Onychoteuthis borealijaponica, in the east significantly affected the probability of SPD presence, which was higher if the prey species co-existed with SPD. Therefore, SPD might adapt their distribution to that of available prey species. SPD stock assessment and management in these two important areas are required for its sustainable utilization. [ABSTRACT FROM AUTHOR]

Aim Understanding the spatial ecology of endangered species is crucial to predicting habitat use at scales relevant to conservation and management. Here, we aim to model the influence of biophysical conditions on habitat suitability for endangered fin whales Balaenoptera physalus, with a view to informing management in a heavily impacted ocean region. Location We satellite-tracked the movements of 67 fin whales through the California Current System ( CCS), a dynamic eastern boundary upwelling ecosystem in the Northeast Pacific. Methods We use a multi-scale modelling framework to elucidate biophysical influences on habitat suitability for fin whales in the CCS. Using generalized additive mixed models, we quantify the influence of a suite of remotely sensed variables on broadscale patterns of occupancy and present the first year-round, high-resolution predictions of seasonal habitat suitability. Further, we model the influence of contemporaneous biophysical conditions on individual-level residence times in high-use habitat. Results We present evidence of year-round habitat suitability in the southern California Current System, robust to interannual variability, establishing that North Pacific fin whales do not follow the canonical baleen whale migration model. Within the high-use habitat in the Southern California Bight ( SCB), individual-level residency in localized areas ( n = 16 for >30 days; n = 4 for >6 months) was associated with warm, shallow, nearshore waters (>18°C, <500 m), with cool waters (14-15°C) occurring over complex seafloor topographies and with convergent (sub)mesoscale structures at the surface. Main Conclusions Biophysical conditions in the southern CCS generate productive foraging habitats that can support the fin whale population year-round and allow for extended periods of residency in localized areas. High-use habitats for fin whales are colocated with areas of intense human use, including international shipping routes and a major naval training range. Seasonal habitat suitability maps presented here could inform the management of anthropogenic threats to endangered baleen whales in this globally significant biodiversity hotspot. [ABSTRACT FROM AUTHOR]

ABSTRACT Northern crested caracaras ( Caracara cheriway) in south-central Florida are threatened by habitat loss, which is ongoing at local and regional scales. A tool capable of predicting effects of habitat loss on caracaras, and whose predictions can inform conservation strategies at multiple scales is imperative. We coupled a resource selection function-based habitat model with a geographic information system to predict the effects on the distribution of caracara nesting habitat as a result of a planned conversion of 143 km2 of agricultural land to sugarcane (local scale), and as a result of projected urbanization through 2060 (regional scale). Our model predicted a 100% decrease in the area classified as having a high or the highest relative probability of caracara nesting habitat (129.39 km2, or 13 average caracara home range equivalents) following completion of the local-scale project, and a 34.9% decrease following urbanization projected by 2060 (3,641.52 km2, or 366 average caracara home range equivalents). Our model can help direct compensatory mitigation at the local scale by predicting the amount of habitat likely to be lost because of land conversion at project sites. Predictions from the model also identify 3 conservation areas within the species range in Florida that collectively contain 6,140 km2 and would aid in region-wide conservation of this threatened raptor. © 2017 The Wildlife Society. [ABSTRACT FROM AUTHOR]

Hydropeaking presents one of the large impacts on river ecology and is gaining importance because of an increasinlgy volatile energy market with high portions of new renewable energies dependent on local climate conditions. This study presents the application of a fuzzy logic model for the investigation of macrobenthic habitats under hydropeaking conditions in the Norwegian river Surna. Preference data of the three taxa Baetis rhodani, Hydroptila spp. and Allogamus auricollis with distinctively different habitat requirements related to near-bottom flow forces (high/low forces, and narrow range) are used. These data are transferred into the multivariate fuzzy rule-based physical habitat model Computer Aided Simulation of Instream flow and Riparia in order to integrate water depth and river bed substrate as additional parameters. Permanently available habitats (persistent habitats) are assessed for different scenarios of hydropeaking operation. It is found that the amount of persistently high-quality habitat is closely related to the size and range of fluctuations in hydraulic conditions occuring during hydropeaking events. Effects are much more distinct for species with a narrow range of hydraulic preference. The integration of water depth in the simulations has a noticable impact on the amount and quality of predicted habitats. Substrate conditions in the investigation site are homogeneous and, in the specific case considered, do not have a significant impact. The study suggests persistent habitats as a suitable indicator of hydropeaking impact on organisms with low mobility. The persistent habitat approach takes into account that organisms with a low mobility and a distinct range of tolerance related to hydraulic stress tend to settle in areas with permanently stable conditions. Multivariate aspects are accounted through the fuzzy rule-based approach and do clearly affect habitat predictions. Habitat requirements of species particularly sensitive to hydropeaking are proposed for the investigation and application in the future. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Aim Ecological niche modelling can provide valuable insight into species' environmental preferences and aid the identification of key habitats for populations of conservation concern. Here, we integrate biologging, satellite remote-sensing and ensemble ecological niche models (EENMs) to identify predictable foraging habitats for a globally important population of the grey-headed albatross (GHA) Thalassarche chrysostoma. Location Bird Island, South Georgia; Southern Atlantic Ocean. Methods GPS and geolocation-immersion loggers were used to track at-sea movements and activity patterns of GHA over two breeding seasons (n = 55; brood-guard). Immersion frequency (landings per 10-min interval) was used to define foraging events. EENM combining Generalized Additive Models (GAM), MaxEnt, Random Forest (RF) and Boosted Regression Trees (BRT) identified the biophysical conditions characterizing the locations of foraging events, using time-matched oceanographic predictors (Sea Surface Temperature, SST; chlorophyll a, chl-a; thermal front frequency, TFreq; depth). Model performance was assessed through iterative cross-validation and extrapolative performance through cross-validation among years. Results Predictable foraging habitats identified by EENM spanned neritic (<500 m), shelf break and oceanic waters, coinciding with a set of persistent biophysical conditions characterized by particular thermal ranges (3-8 °C, 12-13 °C), elevated primary productivity (chl-a > 0.5 mg m-3) and frequent manifestation of mesoscale thermal fronts. Our results confirm previous indications that GHA exploit enhanced foraging opportunities associated with frontal systems and objectively identify the APFZ as a region of high foraging habitat suitability. Moreover, at the spatial and temporal scales investigated here, the performance of multi-model ensembles was superior to that of single-algorithm models, and cross-validation among years indicated reasonable extrapolative performance. Main conclusions EENM techniques are useful for integrating the predictions of several single-algorithm models, reducing potential bias and increasing confidence in predictions. Our analysis highlights the value of EENM for use with movement data in identifying at-sea habitats of wide-ranging marine predators, with clear implications for conservation and management. [ABSTRACT FROM AUTHOR]

Habitat use is often examined at a species or population level, but patterns likely differ within a species, as a function of the sex, breeding colony, and current breeding status of individuals. Hence, within-species differences should be considered in habitat models when analyzing and predicting species distributions, such as predicted responses to expected climate change scenarios. Also, species' distribution data obtained by different methods (vessel-survey and individual tracking) are often analyzed separately rather than integrated to improve predictions. Here, we eventually fit generalized additive models for Streaked Shearwaters Calonectris leucomelas using tracking data from two different breeding colonies in the Northwestern Pacific and visual observer data collected during a research cruise off the coast of western Japan. The tracking-based models showed differences among patterns of relative density distribution as a function of life history category (colony, sex, and breeding conditions). The integrated tracking-based and vessel-based bird count model incorporated ecological states rather than predicting a single surface for the entire species. This study highlights both the importance of including ecological and life history data and integrating multiple data types (tag-based tracking and vessel count) when examining species-environment relationships, ultimately advancing the capabilities of species distribution models. [ABSTRACT FROM AUTHOR]

Projects that restore river flows can be considered as in situ experiments and should be used to test predictions of the effects of flow changes on fish populations and communities. However, flow restoration projects often lack appropriate monitoring and replication. The Rhône restoration project has included repeated flow changes, in four bypassed reaches of the river, where the increase in minimum daily flow varied from minimal change to a tenfold increase., Fish communities (>55 000 individuals of 36 species) were electrofished at nine sites in the main channels of the four bypassed reaches, for 2-9 years before and for 5-10 years after the flow restoration. An instream hydraulic habitat model, published before restoration and based on observations of fish microhabitat preferences in independent reaches, was applied to the bypassed reaches to predict density changes for 14 species that accounted for 94% of the total fish abundance., In the two bypassed reaches where minimum flow was considerably increased (fivefold and tenfold), the abundance of species preferring fast-flowing and deep microhabitats increased by factors of 1.9 and 2.4, respectively, whereas the abundance of other species strongly decreased. Predicted changes in density made using the habitat model for these two reaches agreed with the observations at several sites and involved several fish species. In contrast, in the two bypassed reaches where flow changes were less, the observed changes in density were weak and less related to the model predictions., Hydraulic habitat models predicted changes of fish populations and the predictions also explained observed community responses to the changed flows. Ten years after the first flow restoration, our results suggest that the Rhône restoration generated perennial changes of the fish community structure, reversing community patterns that were observed prior to the flow restoration. [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]

Habitat models were developed from dedicated sighting survey data collected during summers between 1983 and 2006 in the North Pacific Ocean. Our aim was to examine the distribution pattern of the southern form of the short-finned pilot whale ( Globicephala macrorhynchus) in relation to the physical environment. We tested two different types of analytical procedures for habitat estimation: generalized linear models ( GLMs) and ecological niche factor analysis ( ENFA). The area under the receiver operating characteristic curve and the Boyce index suggested that GLM defined the core habitat well, whereas ENFA estimated the suitable habitat more correctly. These models indicated the core habitat within the subtropical gyre. Among the environmental variables used to construct the habitat models, the temperature at a depth of 200 m contributed most to both GLM and ENFA. This corresponds to the fact that the species mainly feeds on mesopelagic prey and that the axis of the Kuroshio Current, in the northwestern part of the subtropical gyre, is characterized by its temperature at 200 m. Habitat suitability in the coastal waters off southern Japan also correlated with the Kuroshio meander patterns. Thus, the southern form of the short-finned pilot whale appears to be particularly well adapted to the ecosystem of the North Pacific subtropical gyre. [ABSTRACT FROM AUTHOR]

Due to socioeconomic differences, the accuracy and extent of reporting on the occurrence of native species differs among countries, which can impact the performance of species distribution models. We assessed the importance of geographical biases in occurrence data on model performance using Hydrilla verticillata as a case study. We used Maxent to predict potential North American distribution of the aquatic invasive macrophyte based upon training data from its native range. We produced a model using all available native range occurrence data, then explored the change in model performance produced by omitting subsets of training data based on political boundaries. We also compared those results with models trained on data from which a random sample of occurrence data was omitted from across the native range. Although most models accurately predicted the occurrence of H. verticillata in North America (AUC > 0.7600), data omissions influenced model predictions. Omitting data based on political boundaries resulted in larger shifts in model accuracy than omitting randomly selected occurrence data. For well-documented species like H. verticillata, missing records from single countries or ecoregions may minimally influence model predictions, but for species with fewer documented occurrences or poorly understood ranges, geographic biases could misguide predictions. Regardless of focal species, we recommend that future species distribution modeling efforts begin with a reflection on potential spatial biases of available occurrence data. Improved biodiversity surveillance and reporting will provide benefit not only in invaded ranges but also within under-reported and unexplored native ranges. [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]

There is currently much interest in replacing the design-based component of conventional distance sampling methods by a modelling approach where animal densities are related to environmental covariates. These models allow identification of relationships between density and covariates. One of the uses of such models is to assess the effects of some intervention on numbers for species of conservation interest in designed distance sampling experiments., In this context, we use an integrated likelihood approach for modelling sample counts, adopting a Poisson model and allowing imperfect detectability on the sample plots. We use the method of Royle, Dawson & Bates (2004, Ecology, 85, 1591), extended to model heterogeneity in detection probabilities using either multiple covariate distance sampling methods or stratification. Moreover, we include a random effect for site in the plot abundance model to accommodate correlation in repeat counts at a single site., These developments were motivated by a large-scale experimental study to assess the effects of establishing conservation buffers along field margins on indigo buntings in several US states. We analyse the data using an integrated likelihood and include model selection for both the Poisson rate of counts and detection probabilities. We assess model performance by comparing our results with those using a two-stage approach (Buckland et al. 2009, Journal of Agricultural, Biological, and Environmental Statistics, 14, 432) which we extended by including a random effect for site in the plot abundance model., The two methods led to the same selected models and gave similar results for parameters, which revealed significant beneficial effects of buffers on indigo bunting densities. Densities on buffered fields were on average 35% higher than on unbuffered fields. Using a detection function stratified by state captured some of the heterogeneity in detection probabilities between the nine states included in the analyses., Synthesis and applications. We develop and compare two methods for analysing data from large-scale distance sampling experiments with imbalanced repeat measures. By including a random site effect in the plot abundance model, we relax the assumption of independent sample counts which is generally made for distance sampling methods, and we allow inference to be drawn for the wider region that the sites represent. [ABSTRACT FROM AUTHOR]

1. Identification of suitable habitats for small, endangered populations is important to preserve key areas for potential augmentation. However, replicated spatial data from a sufficient number of individuals are often unavailable for such populations, leading to unreliable habitat models. This is the case for the endangered Pyrenean brown bear Ursus arctos population, with only about 20 individuals surviving in two isolated groups. 2. We conducted habitat suitability analyses at two spatial scales (coarse and local). Given the limited available data, we used information from the nearby Cantabrian brown bear population in Spain to develop a two-dimensional model (human and natural variables) at a coarse scale, based on logistic regression, which we applied in the Pyrenees. At a local scale, we used bear presence in the Pyrenees to describe the population's ecological niche and develop a habitat suitability model using presence-only methods. We combined these models to obtain a more integrative understanding of bear requirements. 3. The coarse-scale model showed a good transferability to the Pyrenees, identifying preference for areas with high forest connectivity, masting trees, rugged terrain and shrubs and avoidance of areas with anthropogenic structures. The local-scale model was consistent with the coarse-scale model. Bears showed a trade-off between food resources (scarcer at high elevations) and human presence (higher at low elevations). 4. Our models illustrated that there is unoccupied good habitat for bears in the Pyrenees that could host new individuals. Combining two scales allowed us to identify areas that should be prioritized for management actions and also those that should be easier to manage for bears. 5. Synthesis and applications. Our study illustrates how a nested-scale approach, combining coarse data from a different population and fine-scale local data, can aid in the management of small populations with limited data. This was applied to remnant brown bear populations to identify priorities for conservation management. [ABSTRACT FROM AUTHOR]

We combined observations of bobcats ( Lynx rufus) from bowhunters with remotely-sensed data to build models that describe habitat and relative abundance of this species in the agricultural landscape of Iowa, USA. We calculated landscape composition and configuration from publicly available land cover, census, road, hydrologic, and elevation data. We used multiple regression models to examine county-level associations between several explanatory variables and relative abundance of bobcats reported by surveyed bowhunters in each county. The most influential explanatory variables in the models were metrics associated with the presence of grassland, including Conservation Reserve, along with configuration of this perennial habitat with forests, although human population density and abundance of eastern cottontails ( Sylvilagus floridanus) also correlated with abundance of bobcats. Validation of predictions against 3 years of independent data provided confidence in the models, with 66% of predictions within 1 bobcat/1,000 hunter-hours and 95% within 5 bobcats/1,000 hunter-hours of observed values. Once we accounted for landscape differences, no residual spatial trend was evident, despite relatively recent bobcat recolonization of Iowa. Models suggested that future range expansion of the bobcat population may be possible in some northern Iowa counties where habitat composition is similar to counties in southern Iowa where bobcats are abundant. Results from the county-level model have been useful to the Iowa Department of Natural Resources in evaluating the expansion of this once rare species and for delineating harvest opportunities. © 2011 The Wildlife Society. [ABSTRACT FROM AUTHOR]

Summary 1. Classifications that group rivers and streams with similar ecological characteristics are used increasingly to underpin conservation and resource management planning. Uses include identifying systems that may respond similarly to human activities or management actions, setting guidelines and standards to manage human impacts, interpreting data from inventory (survey) and monitoring, and identifying priority sites for conservation management. 2. Traditional approaches to river classification have been based mostly on delineating landscape units (ecoregions), often by grouping adjacent catchments having similar ecological character. However, use of this approach can be complicated by marked local heterogeneity of river systems. Instead, classifications may be more ecologically relevant if individual river or stream segments having similar environmental conditions are grouped together, independent of their geographic locations. The latter approach also allows the use of more rigorous classification procedures, including newly emerging techniques that optimise the ability of a classification to discriminate patterns in parallel sets of biological data. 3. Here, we explore the use of one of these newer techniques, generalised dissimilarity modelling (GDM), an extension of generalised regression techniques, that defines an optimal set of transformations of candidate environmental predictors to maximise explanation of species turnover in site-based biological data. 4. Using two biological data sets describing the distributions of freshwater fish and macroinvertebrates and a candidate set of functionally relevant environmental variables, we used GDM to identify the variables, weightings and transformations that best explain biological dissimilarities across sites. We then used these as input to a multivariate classification of 567 000 river and stream segments throughout New Zealand. Weightings and transformations of these variables were also specified from the GDM analysis. The matrix of transformed environmental predictors was classified in a two-stage process, using non-hierarchical mediod clustering to define an initial set of 400 groups, with relationships between these groups then defined using hierarchical clustering. 5. The resulting classification better discriminates sites with similar biological character than previous classifications, particularly at higher levels of classification detail. Key factors contributing to this success include the use of detailed, segment-specific environmental variables, coherently accounting for the longitudinal connectivity inherent in rivers including its implications for the construction of biologically relevant predictors, and the use of a modelling technique (GDM) designed to specifically analyse biological turnover and its relationships with environment. [ABSTRACT FROM AUTHOR]

The Mahalanobis distance statistic (D2) has emerged as an effective tool to identify suitable habitat from presence data alone, but there has been no mechanism to select among potential habitat covariates. We propose that the best combination of explanatory variables for a D2 model can be identified by ranking potential models based on the proportion of the entire study area that is classified as potentially suitable habitat given that a predetermined proportion of occupied locations are correctly classified. In effect, our approach seeks to minimize errors of commission, or maximize specificity, while holding the omission error rate constant. We used this approach to identify potentially suitable habitat for the Olympic marmot (Marmota olympus), a declining species endemic to Olympic National Park, Washington, USA. We compared models built with all combinations of 11 habitat variables. A 7-variable model identified 21,143 ha within the park as potentially suitable for marmots, correctly classifying 80% of occupied locations. Additional refinements to the 7-variable model (e.g., eliminating small patches) further reduced the predicted area to 18,579 ha with little reduction in predictive power. Although we sought a model that would allow field workers to find 80% of Olympic marmot locations, in fact, <3% of 376 occupied locations and <9% of abandoned locations were >100 m from habitat predicted by the final model, suggesting that >90% of occupied marmot habitat could be found by observant workers surveying predicted habitat. The model comparison procedure allowed us to identify the suite of covariates that maximized specificity of our model and, thus, limited the amount of less favorable habitat included in the final prediction area. We expect that by maximizing specificity of models built from presence-only data, our model comparison procedure will be useful to conservation practitioners planning reintroductions, searching for rare species, or identifying habitat for protection. [ABSTRACT FROM AUTHOR]

Long-term monitoring programs must use informative yet cost-effective methods. Occupancy estimates that incorporate detection probabilities are used with increasing frequency to describe species status and make management recommendations. Estimating changes in the occupancy of points over time in response to management actions or environmental changes may be especially useful for management of the Palm Springs round-tailed ground squirrel (Spermophilus tereticaudus chlorus), a subspecies covered under the Coachella Valley Multiple Species Habitat Conservation Plan and Natural Community Conservation Plan. In 2002 and 2003, we estimated occupancy and detection probability of ground squirrels across lands modeled as ground squirrel habitat by the Scientific Advisory Committee for the Habitat Conservation Plan and tested a priori hypotheses about how occupancy varied among vegetation and substrate types. In the 2003 study, we asked whether these associations were affected by winter rains after the 2002 drought year. Occupancy in 2003 was estimated at 0.99 (S^E = 0.01) in Western honey mesquite (Prosopis glandulosa) on dunes and hummocks, and occupancy of the remaining modeled habitat was best described by distance to mesquite, with the occupancy probability decreasing with increasing distance from mesquite on dunes or hummocks. The best-supported model in 2002 described the distribution of ground squirrels as a function of only vegetation and substrate type. However, the best-supported models in 2003 suggested that distance to mesquite was a component of the occupancy of non-mesquite vegetation. Mesquite seems to provide high-quality habitat that can support ground squirrels at high occupancy probabilities that may breed successfully every year. In contrast, other vegetation types provide low-quality habitat that can only support ground squirrels at low occupancy probabilities that may only breed occasionally. Mesquite could be an essential refugium during drought years, and the 4 best-supported models in 2003 suggest that restoration of mesquite beginning near currently occupied mesquite patches could be critical for maintaining ground squirrel populations on the preserves. [ABSTRACT FROM AUTHOR]

Reduction of bycatch through spatial management is one possible approach in fisheries management. One requirement is description of the habitat of the species in question, and this is now possible for a range of species using data collected from electronic tags. Here we report on a spatial management approach used to limit unwanted capture of southern bluefin tuna (SBT, Thunnus maccoyii) in the eastern Australia longline fishery. The approach combines a habitat model conditioned with temperature preference data from 56 pop-up satellite archival tags deployed on SBT, and an ocean model to produce near real-time habitat predictions utilized by fishery managers during the fishing season. Three habitat types based on probability of SBT occurrence are identified in the fishing region, and spatial zoning by managers is used to regulate fisher access to these zones. This management approach has been used since 2003, and despite dynamic spatial management often being resisted by fishers or managers because of perceived implementation complexity, there has been an increase in complexity of the management actions over the 6-yr period. Managers now request more habitat predictions each season, update the spatial zoning more frequently, and apply more complicated management lines dividing the habitat types. This has resulted in an improved fit to the modeled habitat predictions, and as catch per unit effort (CPUE) of SBT is related to the habitat type, has contributed to the overall success of the management approach. This implemented management approach illustrates that managers and fishers can successfully implement complex dynamic spatial zoning. [ABSTRACT FROM AUTHOR]

Aim To determine whether different abundances of introduced species of Cinchona (Rubiaceae) affect species composition and facilitate species richness in managed tropical forests, to test whether any facilitative effects on understorey species depend on forest type, and to investigate whether facilitative effects can be attributed to the ‘substitutive facilitation model’. Location Makawao Forest Reserve on Maui, Hawai’i, USA. Methods Cinchona species ( Cinchona pubescens and Cinchona calisaya) were mapped within various forest types. In three forest types (ageing Eucalyptus and Pinus plantations, and near-natural Acacia koa forests), we analysed environmental parameters (e.g. canopy cover, litter cover, pH value and soil depth) and the species composition of Cinchona-invaded and non-invaded plots; data were compared based on Cinchona cover and forest types. Habitat modelling for several endemic species and tree ferns was carried out to test whether Cinchona cover is an important variable for the probability of occurrence of these endemics. Results Cinchona species have naturalized mainly in Eucalyptus and Pinus plantations and Acacia koa forests and here add an additional shrub layer. In contrast to other studies, we revealed facilitative effects of Cinchona on native species within all forest types. Species richness is about 20% higher in invaded plots than in non-invaded plots, and these show a nearly 50% higher proportion of endemic species, including tree ferns. The proportion of endemics even increases with increasing Cinchona cover. For several endemics, Cinchona is found to be an important variable for the probability of occurrence, and the removal of Cinchona cover as an explanatory variable lowers the model fit. In addition to Cinchona, variables delineating vegetation structure and light availability have a strong effect on the model fit. Main conclusions In the structurally simplified Hawaiian forests studied, Cinchona facilitated endemic species in accordance with the ‘substitutive facilitation model’. This contrasts with the results of an earlier study in the naturally treeless Galápagos highlands, which revealed a sharp decrease in the abundance of endemics under Cinchona canopy. These results illustrate that, through the same structural change (addition of a vegetation layer), an invasive species may exert divergent effects across different ecosystem types. The facilitation of endemic understorey species by invasive tree species in managed forests leads to a dilemma in conservation but also to new perspectives for ecosystem restoration. [ABSTRACT FROM AUTHOR]

The article presents a study on the efficient use of light detection and ranging (LIDAR)-derived measurements to measure the vegetation structure for passerine birds in riparian habitat at the Central Valley in California. The use of LIDAR imagery is capable of generating estimates of canopy height, stem diameter and biomass. Moreover, the study intends to support the predictive performance of vegetation measurements of LIDAR imagery at one hectare scale.

Because habitat loss and fragmentation threaten giant pandas (Ailuropoda melanoleuca), habitat protection and restoration are important conservation measures for this endangered species. However, distribution and value of potential habitat to giant pandas on a regional scale are not fully known. Therefore, we identified and ranked giant panda habitat in Foping Nature Reserve, Guanyinshan Nature Reserve, and adjacent areas in the Qinling Mountains of China. We used Mahalanobis distance and 11 digital habitat layers to develop a multivariate habitat signature associated with 247 surveyed giant panda locations, which we then applied to the study region. We identified approximately 128 km² of giant panda habitat in Foping Nature Reserve (43.6% of the reserve) and 49 km² in Guanyinshan Nature Reserve (33.6% of the reserve). We defined core habitat areas by incorporating a minimum patch-size criterion (5.5 km²) based on home-range size. Percentage of core habitat area was higher in Foping Nature Reserve (41.8% of the reserve) than Guanyinshan Nature Reserve (26.3% of the reserve). Within the larger analysis region, Foping Nature Reserve contained 32.7% of all core habitat areas we identified, indicating regional importance of the reserve. We observed a negative relationship between distribution of core areas and presence of roads and small villages. Protection of giant panda habitat at lower elevations and improvement of habitat linkages among core habitat areas are important in a regional approach to giant panda conservation. [ABSTRACT FROM AUTHOR]

We developed predictive habitat models for a bighorn sheep (Ovis canadensis) population in the Peninsular Ranges of southern California, USA, using 2 Geographic Information System modeling techniques, Ecological Niche Factor Analysis (ENFA) and Genetic Algorithm for Rule-set Production (GARP). We used .16,000 Global Positioning System locations from 34 animals in 5 subpopulations to develop and test ENFA and GARP models, and we then compared these models to each other and to the expert-based model presented in the United States Fish and Wildlife Service's Recovery Plan for this population. Based on a suite of evaluation methods, we found both ENFA and GARP to provide useful predictions of habitat; however, models developed with GARP appeared to have higher predictive power. Habitat delineations resulting from GARP models were similar to the expert-based model, affirming that the expert-based model provided a useful delineation of bighorn sheep habitat in the Peninsular Ranges. In addition, all 3 models identified continuous bighorn sheep habitat from the northern to southern extent of our study area, indicating that the Recovery Plan's recommendation of maintaining habitat connectivity throughout the range is an appropriate goal. [ABSTRACT FROM AUTHOR]

Questions: 1. How many traits associated with persistence and regeneration are necessary to predict the response of plants to soil fertility and disturbance? 2. Are correlated changes in trait expressions linked to the response of functional groups to fertility and disturbance? Locution: Lower Frankonia, Germany. Methods: On 120 plots located in managed and abandoned grasslands, fields, and vineyards, we recorded species composition, disturbance intensity, soil water and nutrients, and ten candidate traits for 75 species. We used a novel method which is based on three steps: (1) logistic regression to separate responsive from non-responsive species; (2) iterative clustering of all possible combinations of the candidate traits including regression of each cluster in response to the environmental variables; (3) selection of the trait combination that performed best with respect to goodness of fit of all clusters from this combination. Bivariate trait relationships across functional groups were analysed with reduced major axis regression (RMA). Results: The parsimonious trait combination consisted of life span, specific leaf area (SLA), canopy height, and seed number. The 'acquisitive' functional groups in terms of SLA and height were linked to higher fertility and earlier disturbance, while the 'retentive' groups related to lower fertility and later disturbance. Investment in reproduction, however, displayed a reverse relation. SLA and canopy height showed correlated shifts in two pairs of co-occursing functional groups. Conclusions: A small number of traits is sufficient to predict the response of species. Plants need a higher increment in SLA to reach the same height, if start of disturbance is earlier. Linkages between traits shift from generative to vegetative with increasing fertility and earlier start of disturbance. Functional groups enable shifts in scaling relationships between traits to be analysed, in contrast to the analysis of single traits. [ABSTRACT FROM AUTHOR]

Habitat size, habitat isolation and habitat quality are regarded as the main determinants of butterfly occurrence in fragmented landscapes. To analyze the relationship between the occurrence of the butterfly Cupido minimus and these factors, patch occupancy of the immature stages in patches of its host plant Anthyllis vulneraria was studied in the nature reserve Hohe Wann in Bavaria (Germany). In 2001 and 2002, 82 A. vulneraria patches were surveyed for the presence of C. minimus larvae. The occurrence was largely affected by the size of the food plant patches. In a habitat model that uses multiple logistic regression, the type of management and habitat connectivity are further determinants of species distribution. Internal and temporal validation demonstrate the stability and robustness of the developed habitat models. Additionally, it was proved that the colonization rate of C. minimus was significantly influenced by the distance to the next occupied Anthyllis patch. Concerning long-term survival of (meta-) populations in fragmented landscapes, the results show that lower habitat quality may be compensated by higher connectivity between host plant patches. [ABSTRACT FROM AUTHOR]

We estimated carrying capacity for sea otters (Enhydra lutris) in the coastal waters of British Columbia, Canada, by characterizing habitat according to the complexity of nearshore intertidal and sub-tidal contours. We modeled the total area of complex habitat on the west coast of Vancouver Island by first calculating the complexity of the Checleset Bay--Kyuquot Sound (CB--KS) region, where sea otters have been at equilibrium since the mid-1990s. We then identified similarly complex areas on the west coast of Vancouver Island (WCVI model), and adapted the model to identify areas of similar complexity along the entire British Columbia coast (BC model). Using survey data from the CB--KS region, we calculated otter densities for the habitat predicted by the 2 models. The density estimates for CB--KS were 3.93 otters/km² and 2.53 otters/km² for the WCVI and BC models, respectively, and the resulting 2 estimates of west coast of Vancouver Island complex habitat carrying capacity were not significantly different (WCVI model: 5,123, 95%CI=3,337--7,104; BC model: 4,883, 95%CI=3,223--6,832). The BC model identified the region presently occupied by otters on the central British Columbia coast, but the amount of coast-wide habitat it predicted (5,862 km²) was relatively small, and the associated carrying capacity estimate (14,831, 95%CI=9,790--20,751) was low compared to historical accounts. We suggest that our model captured a type of high-quality or optimum habitat prevalent on the west coast of Vancouver Island, typified by the CB--KS region, and that suitable sea otter habitat elsewhere on the coast must include other habitat characteristics. We therefore calculated a linear, coast-wide carrying capacity of 52,459 sea otters (95% CI = 34,264--73,489)--a more realistic upper limit to sea otters in British Columbia. Our carrying capacity estimates are helping set population recovery targets for sea otters in Canada, and our habitat predictions represent a first step in Critical Habitat identification. This habitat-based approach to estimating carrying capacity is likely suitable for other nonmigratory, density-dependent species. [ABSTRACT FROM AUTHOR]

The article analyzes recent survey data and maps environmental variables integrated over a home-range-scale habitat models for fisher (Martes Pennanti) in California. It aims to determine habitat factors associated with the current distribution of fishers, and test whether those factors differ for widely disjunct northern and southern populations. The authors note that the analyses were designed to probe whether poor habitat quality can explain the current absence of fishers in the historically occupied central and northern Sierra Nevada region. Results and implications of the study are discussed.

Controversy over bobcat (Lynx rufus) management in the northern Lower Peninsula of Michigan (NLP), USA, stimulated a need for information on the distribution of Michigan bobcats. From March 2003 to October 2004, we conducted a radiotelemetry and scentstation survey study of bobcats in the NLP. We developed a spatial model to predict bobcat distribution throughout the NLP based on bobcat area requirements, habitat and landscape variables derived from remotely sensed land-cover data, and a multivariate distance statistic. Bobcat 50% minimum convex polygon core areas were comprised of more lowland forest (51%), nonforested wetlands (9%), and streams (3%) than the surrounding NLP. The NLP was comprised primarily of upland forest (44%) and field (32%). Habitat in the northeast and central regions of the NLP was most similar to the habitat composition of bobcat core areas. This model will be useful in aiding Michigan wildlife management agencies with assessing the status and distribution of the NLP bobcat population by identifying areas important to bobcats and supporting the development of regional strategies for carnivore conservation. [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]

The eastern diamondback rattlesnake (Crotalus adamanteus) and timber rattlesnake (Crotalus horridus) are sympatric throughout most of southern Georgia, USA. We used rattlesnake sightings to quantify and compare habitat use by these 2 species in the Gulf Coastal Plain. At the largest scale examined, univariate statistics and logistic regression models indicated that eastern diamondback rattlesnakes were associated with roads but not with any of the specific habitat types we examined. In contrast, timber rattlesnakes were closely associated with hardwood habitat and riverine systems but not with roads and edges. To effectively conserve and manage both species in the Southeast, a habitat matrix of large intact patches of both hardwood and pine (Pinus spp.) forest may be necessary. [ABSTRACT FROM AUTHOR]

Capercaillie (Tetrao urogallus) is a large, endangered forest grouse species with narrow habitat preferences and large spatial requirements that make it susceptible to habitat changes at different spatial scales. Our aim was to evaluate the relative power of variables relating to forest versus landscape structure in predicting capercaillie occurrence at different spatial scales. We investigated capercaillie-habitat relationships at the scales of forest stand and forest-stand mosaic in 2 Swiss regions. We assessed forest structure from aerial photographs in 52 study plots each 5 km². We classified plots into one of 3 categories denoting the observed local population trend (stable, declining, extinct), and we compared forest structure between categories. At the stand scale, we used presence-absence data for grid cells within the plots to build predictive habitat models based on logistic regression. At this scale, habitat models that included only variables relating to forest structure explained the occurrence of capercaillie only in part, whereas variables selected by the models differed between regions. Including variables relating to landscape features improved the models significantly. At the scale of stand mosaic, variables describing forest structure (e.g., mean canopy cover, proportion of open forest, and proportion of multistoried forest) differed between plot categories. We conclude that small-scale forest structure has limited power to predict capercaillie occurrence at the stand scale, but that it explains well at the scale of the stand mosaic. Including variables for landscape structure improves predictions at the forest-stand scale. Habitat models built with data from one region cannot be expected to predict the species occurrence in other regions well. Thus, multiscale approaches are necessary to better understand species-habitat relationships. Our results can help regional authorities and forest-management planners to identify areas where suitable habitat for capercaillie is not available in the required proportion and, thus, where management actions are needed to improve habitat suitability. [ABSTRACT FROM AUTHOR]

Current circumstances — that the majority of species distribution records exist as presence-only data (e.g. from museums and herbaria), and that there is an established need for predictions of species distributions — mean that scientists and conservation managers seek to develop robust methods for using these data. Such methods must, in particular, accommodate the difficulties caused by lack of reliable information about sites where species are absent. Here we test two approaches for overcoming these difficulties, analysing a range of data sets using the technique of multivariate adaptive regression splines (MARS). MARS is closely related to regression techniques such as generalized additive models (GAMs) that are commonly and successfully used in modelling species distributions, but has particular advantages in its analytical speed and the ease of transfer of analysis results to other computational environments such as a Geographic Information System. MARS also has the advantage that it can model multiple responses, meaning that it can combine information from a set of species to determine the dominant environmental drivers of variation in species composition. We use data from 226 species from six regions of the world, and demonstrate the use of MARS for distribution modelling using presence-only data. We test whether (1) the type of data used to represent absence or background and (2) the signal from multiple species affect predictive performance, by evaluating predictions at completely independent sites where genuine presence–absence data were recorded. Models developed with absences inferred from the total set of presence-only sites for a biological group, and using simultaneous analysis of multiple species to inform the choice of predictor variables, performed better than models in which species were analysed singly, or in which pseudo-absences were drawn randomly from the study area. The methods are fast, relatively simple to understand, and useful for situations where data are limited. A tutorial is included. [ABSTRACT FROM AUTHOR]

Aggregation of reserve networks is generally considered desirable for biological and economic reasons: aggregation reduces negative edge effects and facilitates metapopulation dynamics, which plausibly leads to improved persistence of species. Economically, aggregated networks are less expensive to manage than fragmented ones. Therefore, many reserve-design methods use qualitative heuristics, such as distance-based criteria or boundary-length penalties to induce reserve aggregation. We devised a quantitative method that introduces aggregation into reserve networks. We call the method the boundary-quality penalty (BQP) because the biological value of a land unit (grid cell) is penalized when the unit occurs close enough to the edge of a reserve such that a fragmentation or edge effect would reduce population densities in the reserved cell. The BQP can be estimated for any habitat model that includes neighborhood (connectivity) effects, and it can be introduced into reserve selection software in a standardized manner. We used the BQP in a reserve-design case study of the Hunter Valley of southeastern Australia. The BQP resulted in a more highly aggregated reserve network structure. The degree of aggregation required was specified by observed (albeit modeled) biological responses to fragmentation. Estimating the effects of fragmentation on individual species and incorporating estimated effects in the objective function of reserve-selection algorithms is a coherent and defensible way to select aggregated reserves. We implemented the BQP in the context of the Zonation method, but it could as well be implemented into any other spatially explicit reserve-planning framework. [ABSTRACT FROM AUTHOR]