Boyce Index Research Articles

Validity of historical volunteered geographic information: Evaluating citizen data for mapping historical geographic phenomena

AbstractStudies on volunteered geographic information (VGI) have focused on examining its validity to reveal geographic phenomena in relatively recent periods. Empirical evaluation of the validity of VGI to reveal geographic phenomena in historical periods (e.g., decades ago) is lacking, although such evaluation is desirable for assessing the possibility of broadening the temporal scope of VGI applications. This article presents an evaluation of the validity of VGI to reveal historical geographic phenomena through a citizen data‐based habitat suitability mapping case study. Citizen data (i.e., sightings) of the black‐and‐white snub‐nosed monkey (Rhinopithecus bieti) were elicited from local residents through three‐dimensional (3D) geovisualization interviews in Yunnan, China. The validity of the elicited sightings to reveal the historical R. bieti distribution was evaluated through habitat suitability mapping using the citizen data in historical periods. The results of controlled experiments demonstrated that suitability maps predicted using the historical citizen data had a consistent spatial pattern (correlation above 0.60) that reflects the R. bieti distribution (Boyce index around 0.90) in areas free of significant environmental change across historical periods. This in turn suggests that citizen data have validity for mapping historical geographic phenomena. It provides supporting empirical evidence for potentially broadening the temporal scope of VGI applications.

Accuracy of climate-based forecasts of pathogen spread.

Species distribution models (SDMs) are a tool for predicting the eventual geographical range of an emerging pathogen. Most SDMs, however, rely on an assumption of equilibrium with the environment, which an emerging pathogen, by definition, has not reached. To determine if some SDM approaches work better than others for modelling the spread of emerging, non-equilibrium pathogens, we studied time-sensitive predictive performance of SDMs for Batrachochytrium dendrobatidis, a devastating infectious fungus of amphibians, using multiple methods trained on time-incremented subsets of the available data. We split our data into timeline-based training and testing sets, and evaluated models on each set using standard performance criteria, including AUC, kappa, false negative rate and the Boyce index. Of eight models examined, we found that boosted regression trees and random forests performed best, closely followed by MaxEnt. As expected, predictive performance generally improved with the length of time series used for model training. These results provide information on how quickly the potential extent of an emerging disease may be determined, and identify which modelling frameworks are likely to provide useful information during the early phases of pathogen expansion.

Ecospat: an R package to support spatial analyses and modeling of species niches and distributions

The aim of the ecospat package is to make available novel tools and methods to support spatial analyses and modeling of species niches and distributions in a coherent workflow. The package is written in the R language (R Development Core Team) and contains several features, unique in their implementation, that are complementary to other existing R packages. Pre‐modeling analyses include species niche quantifications and comparisons between distinct ranges or time periods, measures of phylogenetic diversity, and other data exploration functionalities (e.g. extrapolation detection, ExDet). Core modeling brings together the new approach of ensemble of small models (ESM) and various implementations of the spatially‐explicit modeling of species assemblages (SESAM) framework. Post‐modeling analyses include evaluation of species predictions based on presence‐only data (Boyce index) and of community predictions, phylogenetic diversity and environmentally‐constrained species co‐occurrences analyses. The ecospat package also provides some functions to supplement the ‘biomod2’ package (e.g. data preparation, permutation tests and cross‐validation of model predictive power). With this novel package, we intend to stimulate the use of comprehensive approaches in spatial modelling of species and community distributions.

Distribution of the Habitat Suitability of the Main Malaria Vector in French Guiana Using Maximum Entropy Modeling

Malaria is an important health issue in French Guiana. Its principal mosquito vector in this region is Anopheles darlingi Root. Knowledge of the spatial distribution of this species is still very incomplete due to the extent of French Guiana and the difficulty to access most of the territory. Species distribution modeling based on the maximal entropy procedure was used to predict the spatial distribution of An. darlingi using 39 presence sites. The resulting model provided significantly high prediction performances (mean 10-fold cross-validated partial area under the curve and continuous Boyce index equal to, respectively, 1.11-with a level of omission error of 20%-and 0.42). The model also provided a habitat suitability map and environmental response curves in accordance with the known entomological situation. Several environmental characteristics that had a positive correlation with the presence of An. darlingi were highlighted: nonpermanent anthropogenic changes of the natural environment, the presence of roads and tracks, and opening of the forest. Some geomorphological landforms and high altitude landscapes appear to be unsuitable for An. darlingi. The species distribution modeling was able to reliably predict the distribution of suitable habitats for An. darlingi in French Guiana. Results allowed completion of the knowledge of the spatial distribution of the principal malaria vector in this Amazonian region, and identification of the main factors that favor its presence. They should contribute to the definition of a necessary targeted vector control strategy in a malaria pre-elimination stage, and allow extrapolation of the acquired knowledge to other Amazonian or malaria-endemic contexts.

Consensus forecasting of intertidal seagrass habitat in the Wadden Sea

Summary After the dramatic eutrophication‐induced decline of intertidal seagrasses in the 1970s, the Wadden Sea has shown diverging developments. In the northern Wadden Sea, seagrass beds have expanded and become denser, while in the southern Wadden Sea, only small beds with low shoot densities are found. A lack of documentation of historical distributions hampers conservation management. Yet, the recovery in the northern Wadden Sea provides opportunity to construct robust habitat suitability models to support management. We tuned habitat distribution models based on 17 years of seagrass surveys in the northern Wadden Sea and high‐resolution hydrodynamics and geomorphology for the entire Wadden Sea using five machine learning approaches. To obtain geographically transferable models, hyperparameters were tuned on the basis of prediction accuracy assessed by non‐random, spatial cross‐validation. The spatial cross‐validation methodology was combined with a consensus modelling approach. The predicted suitability scores correlated amongst each other and with the hold‐out observations in the training area indicating that the models converged and were transferable across space. Prediction accuracy was improved by averaging the predictions of the best models. We graphically examined the relationship between the consensus suitability score and independent presence‐only data from outside the training area using the area‐adjusted seagrass frequency per suitability class (continuous Boyce index). The Boyce index was positively correlated with the suitability score indicating the adequacy of the prediction methodology. We used the plot of the continuous Boyce index against habitat suitability score to demarcate three habitat classes – unsuitable, marginal and suitable – for the entire international Wadden Sea. This information is valuable for habitat conservation and restoration management. Divergence between predicted suitability and actual distributions from the recent past indicates that unaccounted factors limit seagrass development in the southern Wadden Sea. Synthesis and applications. Our methodology and data enabled us to produce a robust and validated consensus habitat suitability model. We identified highly suitable areas where intertidal seagrass meadows may establish and persist. Our work provides scientific underpinning for effective conservation planning in a dynamic landscape and sets monitoring priorities.

Detecting the nesting suitability of the re-introduced Crested Ibis Nipponia nippon for nature restoration program in Japan

The Crested Ibis, became extinct in the wild in Japan in 1981; however, a captive breeding facility was established on Sado Island, Niigata Prefecture in 1999 in the hope of conserving the species. Following successful breeding at the facility, a group of birds was released on Sado Island on 25th September 2008, with further reintroductions in subsequent years. Habitat restoration is also necessary to facilitate the re-introduction program of this endangered species. The aim of this study was to detect suitable nesting areas for the released ibises on Sado Island for subsequent nature restoration. Since little ibis nesting information was available for Sado Island, the nesting model was constructed from nesting information from China, from which results were extrapolated for Sado Island. Land usage was assessed using LANDSAT/ETM+ data for China and SPOT/HRG images for Sado Island. In this study, land-use information was used to define the environmental parameters related to nest site selection of the Crested Ibis. For the nesting model, a generalized linear mixed model, with seven environmental factors, was employed. The multi-spatial scale for Crested Ibis nesting was also analyzed. We took the spatial scale into consideration at intervals of 100m from a buffer size of 100m to 2500m. Because nest site selection was influenced by the results of the previous year, nesting location data in 2005 and 2006 were distinguished and models built accordingly. Several important environmental factors were identified for nest site selection: the ratio of broadleaved deciduous forest to coniferous forest; the shape of the forest edge; and, the distance from rice fields and other wetlands. The model’s accuracy was verified using the Boyce index, and shows the validity of the model’s results. Suitable nesting areas for the Crested Ibis were found mainly on: the Kuninaka plain; the ridgeline between the Kuninaka plain and the Osado or Kosado mountain ranges; the area around Lake Kamo; and, the midwestern area of the Kosado region. Suitable nesting sites for the Crested Ibis were all found to be concentrated in what is known as Yatsuda, a traditional Japanese landscape structure providing a complex mosaic of forest and rice fields with complex ecotones. Eastern Kosado, the region that has been designated by the Ministry of the Environment as the conservation area for the reintroduced birds, was not judged to provide suitable nesting habitat for the Crested Ibis. Unfortunately, no evaluation of habitat suitability was made prior to the release of the first captive-bred Crested Ibis into the wild on Sado Island. Had an evaluation of habitat suitability been performed prior to release, more effective nature restoration could have been planned.

A citizen data-based approach to predictive mapping of spatial variation of natural phenomena

The vast accumulation of environmental data and the rapid development of geospatial visualization and analytical techniques make it possible for scientists to solicit information from local citizens to map spatial variation of geographic phenomena. However, data provided by citizens (referred to as citizen data in this article) suffer two limitations for mapping: bias in spatial coverage and imprecision in spatial location. This article presents an approach to minimizing the impacts of these two limitations of citizen data using geospatial analysis techniques. The approach reduces location imprecision by adopting a frequency-sampling strategy to identify representative presence locations from areas over which citizens observed the geographic phenomenon. The approach compensates for the spatial bias by weighting presence locations with cumulative visibility (the frequency at which a given location can be seen by local citizens). As a case study to demonstrate the principle, this approach was applied to map the habitat suitability of the black-and-white snub-nosed monkey (Rhinopithecus bieti) in Yunnan, China. Sightings of R. bieti were elicited from local citizens using a geovisualization platform and then processed with the proposed approach to predict a habitat suitability map. Presence locations of R. bieti recorded by biologists through intensive field tracking were used to validate the predicted habitat suitability map. Validation showed that the continuous Boyce index (Bcont(0.1)) calculated on the suitability map was 0.873 (95% CI: [0.810, 0.917]), indicating that the map was highly consistent with the field-observed distribution of R. bieti. Bcont(0.1) was much lower (0.173) for the suitability map predicted based on citizen data when location imprecision was not reduced and even lower (−0.048) when there was no compensation for spatial bias. This indicates that the proposed approach effectively minimized the impacts of location imprecision and spatial bias in citizen data and therefore effectively improved the quality of mapped spatial variation using citizen data. It further implies that, with the application of geospatial analysis techniques to properly account for limitations in citizen data, valuable information embedded in such data can be extracted and used for scientific mapping.

Habitat suitability models of mountain ungulates: identifying potential areas for conservation

Determining the distribution of species and of suitable habitats is a fundamental part of conservation planning. We used slope and ruggedness of the terrain, forest type and distance to the nearest village to construct habitat suitability maps for three mountain ungulates (barking deer (Muntiacus muntjak), Himalayan goral (Naemorhedus goral) and Himalayan serow (Capricornis thar)) inthe midhills of western Nepal. We used locations of sightings and signs of presence of these mountain ungulates collected during surveys along transect to derive a suitability value for each variable using Jacob's index. A multiplication approach was used to combine environmental variables andproduce a habitat suitability map for each of the three species. An independent dataset was used to evaluate the maps using Boyce's index. This approach provides an overview of the probable distributions of the species in question. We predict that of the total area studied, 57% is suitable for M. muntjak, 67% for N. goral and 41% for C. thar. Although there are suitable habitats for all three species throughout the study area, the availability of high-quality habitats for these species varied considerably. Suitable habitats for N. goral and C. thar were fragmented and mostly confined to the southern and northern parts of the study area. This study provides important baseline information for conservation biologists concerned with maintaining biodiversity in the midhills of Nepal.

Modeling the distribution of Apennine brown bears during hyperphagia to reduce the impact of wild boar hunting

The Apennine brown bear (Ursus arctos marsicanus) survives today in a relict population of about 50 bears in the central Apennines, even though they have been long protected and habitat suitability at the landscape scale does not appear to be a limiting factor for population recovery. Multiple uses within the bear range may increase disturbance and chances of human-caused mortality, including wild boar hunting drives with dogs conducted in the fall and coinciding with hyperphagia in bears. Although wild boar hunting drives represent a very popular recreational activity throughout the central Apennines, this is a very invasive practice and is thought to exert both direct and indirect detrimental effects on bears. In order to foster the progressive abandonment of wild boar hunting drives in the bear range in favor of less impacting hunting methods, we developed a species distribution model (SDM) to identify areas of likely bear presence during hyperphagia. This SDM could represent the basis to develop a hunting zoning system useful to modulate wild boar hunting regimes according to the bear occurrence. Starting from a large dataset of bear occurrences collected from September to November (2005–2010) within the core distribution range of the bear (n = 5746 GPS- and VHF-telemetry locations on 25 adult and subadult bears, plus other signs of bear presence), we modeled the distribution of bears during hyperphagia and selected candidate models using second order corrected Akaike information criteria (AICc). The final model included 16 environmental, topographic, and anthropogenic variables and was evaluated using the continuous Boyce index (0.91). By intersecting the bear occurrence model with a proxy of wild boar hunting intensity, we identified the areas where the banning of wild boar hunting drives represents a priority, and where less impacting hunting regimes are urgently needed. By discussing how such a model can be used to facilitate consensus toward alternative hunting management scenarios, we believe our approach can be extended to other small populations of bears, and of other species as well, that live in multiple-use landscapes and are in need of recovery.

Rare species habitat suitability assessment and reliability evaluation of an expert-based model: A case study of the insectivorous plant Pinguicula crystallina Sibth. et Smith subsp. hirtiflora (Ten.) Strid (Lentibulariaceae)

The development of habitat suitability models requires a large amount of data which are rarely available. In this case, researchers need to get information on the ecological features of the studied species, based on the opinion of experts or on the literature, to construct a qualitative model. However, such models cannot be rigorously evaluated, as in most cases absence points are not available. In this paper, we assess the habitat suitability for a vulnerable insectivorous plant, Pinguicula crystallina Sibth. et Smith subsp. hirtiflora (Ten.) Strid (Lentibulariaceae) in the Campania region. Our aim was to develop an expert-based, presence-only model in support of possible conservation actions. Topographic and geological features of this species suggested by the literature were used in our model. Both the Boyce index and field surveys were chosen to evaluate the model's reliability. During field surveys, 31 absence sites and 1 new presence site were identified, and differences between sites with regard to water chemistry and quality were investigated, water being an element in the species habitat. Factors that affect reliability of the model, such as the lack of a large amount of information on the species and the limited spatial resolution of geographical information system data, are discussed.

Modeling the potential distribution of sun bear in Krau wildlife reserve, Malaysia

Information and data on the distribution of wildlife species become important when strategizing the management and conservation of the species. Species distribution models have increasingly been used as a predictive tool in wildlife conservation planning and management. This research assessed the distribution of the sun bear and identified the relevant distribution descriptive variables in the Krau Wildlife Reserve, Pahang, Malaysia using Ecological Niche Factor Analysis (ENFA). Marginality and specialization factors showed that the sun bear habitat is very specific compared to the entire study area and its niche breadth was slightly narrow. Among all the habitat suitability algorithms available in Biomapper software package, median with the extreme optimum algorithm was found to have the best predictive capabilities by means of a continuous Boyce index. Results showed that sun bear can rarely be found in high altitudes, prefers lowland Dipterocarp, and avoids Mountain Ericaceous and Mixed Hill Dipterocarp forests. It also prefers to live nearby rivers, tends to avoid villages and prefers regions with fine silty-clay and loamy textures soils.

Characterizing the potential distribution of the invasive Asian longhorned beetle (Anoplophora glabripennis) in Worcester County, Massachusetts

The Asian longhorned beetle (ALB) (Anoplophora glabripennis) is an invasive insect pest that has established populations in Worcester County, Massachusetts as of 2008. ALB predominantly targets red maple (Acer rubrum), sugar maple (Acer saccharum), which are prominent in New England hardwood forests, and Norway maple (Acer platanoides), which was planted in built environments as street trees in response to severe weather and invasive insect and pathogen disturbances. Mahalanobis Typicality models related presence locations of ALB presence-only locations in Worcester County towns from 2008 to 2012 to biotic, abiotic, and anthropogenic variables to predict the potential distribution of ALB and to determine locations most characteristic of infestation. k-fold cross-validation and a continuous Boyce Index were employed to validate model performance and to identify threshold values at which continuous models of typicality could be reclassified into categorical maps. Distance-to-roads (r2 = 0.19) and probability of maple presence (r2 = 0.13) were the most important predictor variables in the ALB model. Locations that were most consistently susceptible to ALB infestation had significant high maple presence (p < 0.001) and significant low distance-to-roads (p < 0.001) compared to the whole study area, suggesting that ALB in Worcester County, MA, prefers maple-dominant edge habitats at the current stage of invasion. The localized ALB potential distribution model was consistently accurate (Boyce Continuous Index = 0.84) despite the lack of absence locations and incomplete knowledge of ALB niche breadth in both native and invaded ranges. The results from this study could be used as a baseline for effective adaptive management policies that could help prioritize the need for early detection/eradication measures and address the potential ecological and social ramifications of the current ALB outbreak in Worcester County. It is hoped that the model employed could be further tested for future outbreaks as they are discovered in the USA.

Modelling suitable estuarine habitats for Zostera noltii, using Ecological Niche Factor Analysis and Bathymetric LiDAR

Predicting species distribution and habitat suitability is of considerable use in supporting the implementation of environmental legislation, protection and conservation of marine waters and ecosystem-based management. As other seagrasses, Zostera noltii has declined worldwide, mainly due to human pressures, such as eutrophication and habitat loss. In the case of the Basque Country (northern Spain), the species is present only in 3 out of 12 estuaries. From the literature, it is known that at least 6 of these estuaries were formerly vegetated by this seagrass. Consequently, efforts to monitor and restore (potential) habitats have been enhanced. Therefore, we aim: (i) to determine the main environmental variables explaining Zostera noltii distribution, within the Basque estuaries based upon the Oka estuary; (ii) to model habitat suitability for this species, as a wider applicable management-decision tool for seagrass restoration; and (iii) to assess the applicability and predicted accuracy of the model by using internal and external validation methods. For this purpose, Ecological Niche Factor Analysis (ENFA) has been used to model habitat suitability, based upon topographical variables, obtained from bathymetric Light Detection And Ranging (LiDAR); sediment characteristics variables; and hydrodynamic variables. The results obtained from the ecological factors of the ENFA (Marginality: 1.00; Specialization: 2.59) indicate that the species habitat differs considerably from the mean environmental conditions over the study area; likewise, that the species is restrictive in the selection of the range of conditions within which it dwells. The main environmental variables relating to the species distribution, in order of importance, are: mean grain size; redox potential; intertidal height; sediment sorting; slope of intertidal flat; percentage of gravels; and percentage of organic matter content. The model has a high predicted accuracy (Boyce index: 0.92). Model validation using an independent dataset in the Bidasoa estuary has shown the applicability but also the limitations in extrapolating the habitat suitability model to select suitable transplantation areas in other estuaries with similar morphological and biogeographical characteristics. ENFA-technique, applied with an accurate selection of environmental predictors, could be a promising tool for predicting seagrass habitat suitability which could assist on seagrass conservation and restoration programs worldwide.

Remotely sensed hydroacoustics and observation data for predicting fish habitat suitability

This paper investigates the use of using remotely sensed observation and full coverage hydroacoustic datasets to quantify habitat suitability for a marine demersal fish, the blue-throated wrasse. Because of issues surrounding the detection of species using remotely sensed video techniques, the application of presence-only techniques are well suited for modeling demersal fish habitat suitability. Ecological-Niche Factor Analysis is used to compare analyses conducted using seafloor variables derived from hydroacoustics at three spatial scales; fine (56.25 m 2), medium (506.25 m 2) and coarse (2756.25 m 2), to determine which spatial scale was most influential in predicting blue-throated wrasse habitat suitability. The coarse scale model was found to have the best predictive capabilities with a Boyce Index of 0.80±0.26. The global marginality and specialization values indicated that, irrespective of spatial scale, blue-throated wrasse prefer seafloor characteristics that are different to the mean available within the study site, but exhibit a relatively wide niche. Although variable importance varied over the three spatial scale models, blue-throated wrasse showed a strong preference for regions of shallow water, close to reef, with high rugosity and maximum curvature and low HSI-B values. Generally the spatial patterns in habitat suitability were well represented in the Marine National Park compared to adjacent waters. However, some significant differences in spatial patterns were observed. Interspersion and Juxtaposition Indexes for unsuitable and highly suitable habitat were significantly smaller inside the Marine National Park, while the Mean Shape Index of unsuitable habitat in the Marine National Park was significantly larger.

392 Basal-phenotypes in breast carcinoma – morphological, immunohistochemical and clinical analysis

The Crested Ibis, became extinct in the wild in Japan in 1981; however, a captive breeding facility was established on Sado Island, Niigata Prefecture in 1999 in the hope of conserving the species. Following successful breeding at the facility, a group of birds was released on Sado Island on 25th September 2008, with further reintroductions in subsequent years. Habitat restoration is also necessary to facilitate the re-introduction program of this endangered species. The aim of this study was to detect suitable nesting areas for the released ibises on Sado Island for subsequent nature restoration. Since little ibis nesting information was available for Sado Island, the nesting model was constructed from nesting information from China, from which results were extrapolated for Sado Island. Land usage was assessed using LANDSAT/ETM+ data for China and SPOT/HRG images for Sado Island. In this study, land-use information was used to define the environmental parameters related to nest site selection of the Crested Ibis. For the nesting model, a generalized linear mixed model, with seven environmental factors, was employed. The multi-spatial scale for Crested Ibis nesting was also analyzed. We took the spatial scale into consideration at intervals of 100 m from a buffer size of 100 m to 2500 m. Because nest site selection was influenced by the results of the previous year, nesting location data in 2005 and 2006 were distinguished and models built accordingly. Several important environmental factors were identified for nest site selection: the ratio of broadleaved deciduous forest to coniferous forest; the shape of the forest edge; and, the distance from rice fields and other wetlands. The model’s accuracy was verified using the Boyce index, and shows the validity of the model’s results. Suitable nesting areas for the Crested Ibis were found mainly on: the Kuninaka plain; the ridgeline between the Kuninaka plain and the Osado or Kosado mountain ranges; the area around Lake Kamo; and, the midwestern area of the Kosado region. Suitable nesting sites for the Crested Ibis were all found to be concentrated in what is known as Yatsuda, a traditional Japanese landscape structure providing a complex mosaic of forest and rice fields with complex ecotones. Eastern Kosado, the region that has been designated by the Ministry of the Environment as the conservation area for the reintroduced birds, was not judged to provide suitable nesting habitat for the Crested Ibis. Unfortunately, no evaluation of habitat suitability was made prior to the release of the first captive-bred Crested Ibis into the wild on Sado Island. Had an evaluation of habitat suitability been performed prior to release, more effective nature restoration could have been planned.

Do habitat suitability models reliably predict the recovery areas of threatened species?

1. Identifying those areas suitable for recolonization by threatened species is essential to support efficient conservation policies. Habitat suitability models (HSM) predict species’ potential distributions, but the quality of their predictions should be carefully assessed when the species-environment equilibrium assumption is violated. 2. We studied the Eurasian otter Lutra lutra, whose numbers are recovering in southern Italy. To produce widely applicable results, we chose standard HSM procedures and looked for the models’ capacities in predicting the suitability of a recolonization area. We used two fieldwork datasets: presence-only data, used in the Ecological Niche Factor Analyses (ENFA), and presence-absence data, used in a Generalized Linear Model (GLM). In addition to cross-validation, we independently evaluated the models with data from a recolonization event, providing presences on a previously unoccupied river. 3. Three of the models successfully predicted the suitability of the recolonization area, but the GLM built with data before the recolonization disagreed with these predictions, missing the recolonized river’s suitability and badly describing the otter’s niche. Our results highlighted three points of relevance to modelling practices: (1) absences may prevent the models from correctly identifying areas suitable for a species spread; (2) the selection of variables may lead to randomness in the predictions; and (3) the Area Under Curve (AUC), a commonly used validation index, was not well suited to the evaluation of model quality, whereas the Boyce Index (CBI), based on presence data only, better highlighted the models’ fit to the recolonization observations. 4. For species with unstable spatial distributions, presence-only models may work better than presence-absence methods in making reliable predictions of suitable areas for expansion. An iterative modelling process, using new occurrences from each step of the species spread, may also help in progressively reducing errors. 5. Synthesis and applications. Conservation plans depend on reliable models of the species’ suitable habitats. In non-equilibrium situations, such as the case for threatened or invasive species, models could be affected negatively by the inclusion of absence data when predicting the areas of potential expansion. Presence-only methods will here provide a better basis for productive conservation management practices.

Predicting suitable habitat for the European lobster ( Homarus gammarus), on the Basque continental shelf (Bay of Biscay), using Ecological-Niche Factor Analysis

Predicting species distribution and habitat suitability (HS) modelling, across broad spatial scales, is now a major challenge in marine ecology. The resulting knowledge is of considerable use in supporting the implementation of environmental legislation, integrated coastal zone management and ecosystem-based fisheries management. This contribution considers the identification of seafloor morphological characteristics, together with wave energy conditions, that determine the presence of European lobster ( Homarus gammarus); and it predicts suitable habitats over the Basque continental shelf (Bay of Biscay), in summer. The results obtained, by applying Ecological-Niche Factor Analysis (ENFA), indicate that lobster habitat differs considerably from the mean environmental condition over the study area; likewise, that it is restrictive in terms of the range of conditions in which they dwell. The best of the environmental predictors found to be: distance to the rock substrate; Benthic Position Index; wave flux over the seafloor; and the underlying bathymetry. A habitat suitability map was produced, with a high model quality (Boyce index: 0.98 ± 0.06). The most suitable habitat for European lobster are locations at the boundary between sedimentary- and rocky-bottoms, coincident with seafloor depressions with a steep slope, with medium to high wave energy conditions, and located within a range of water depths of 35–40 m. This approach demonstrates the applicability of the method in case studies where only presence data are available, together with the inclusion of environmental variables obtained from different sources.

Evaluating the ability of habitat suitability models to predict species presences

Models predicting species spatial distribution are increasingly applied to wildlife management issues, emphasising the need for reliable methods to evaluate the accuracy of their predictions. As many available datasets (e.g. museums, herbariums, atlas) do not provide reliable information about species absences, several presence-only based analyses have been developed. However, methods to evaluate the accuracy of their predictions are few and have never been validated. The aim of this paper is to compare existing and new presence-only evaluators to usual presence/absence measures. We use a reliable, diverse, presence/absence dataset of 114 plant species to test how common presence/absence indices (Kappa, MaxKappa, AUC, adjusted D 2) compare to presence-only measures (AVI, CVI, Boyce index) for evaluating generalised linear models (GLM). Moreover we propose a new, threshold-independent evaluator, which we call “continuous Boyce index”. All indices were implemented in the BIOMAPPER software. We show that the presence-only evaluators are fairly correlated ( ρ > 0.7) to the presence/absence ones. The Boyce indices are closer to AUC than to MaxKappa and are fairly insensitive to species prevalence. In addition, the Boyce indices provide predicted-to-expected ratio curves that offer further insights into the model quality: robustness, habitat suitability resolution and deviation from randomness. This information helps reclassifying predicted maps into meaningful habitat suitability classes. The continuous Boyce index is thus both a complement to usual evaluation of presence/absence models and a reliable measure of presence-only based predictions.

Evaluation of the Logan & Boyce plaque index for the study of dental plaque accumulation in dogs

The objectives of this study were to assess intra-examiner (experienced examiner) and inter-examiner agreements (experienced versus non-experienced examiners) of scores assessed with the Logan & Boyce plaque index and to evaluate whether a modification of this index, where anatomical landmarks are used for horizontal division [mod L&B-AL] and dye references are used for assessing intensity of dye (plaque thickness) [mod L&B-DR], would improve repeatability. The Logan & Boyce index was found to be inaccurate when scoring plaque coverage as it underestimated the total crown surface. The contribution of the gingival part to the total tooth score was minimized by the Logan & Boyce index compared to the mod L&B-AL/DR. Precision of global plaque scorings was significantly improved by the mod L&B-AL/DR. Intra-examiner agreement of plaque thickness and plaque coverage scorings on the gingival part of the tooth was significantly improved by the mod L&B-AL/DR. Studies evaluating plaque accumulation in dogs should therefore use the mod L&B-AL/DR rather than the Logan & Boyce index.