Ecological Niche Factor Analysis Model Research Articles

The use of maximum entropy and ecological niche factor analysis to decrease uncertainties in samples for urban gain models

ABSTRACT Uncertainty is a common problem in spatial modeling and geographical information systems (GIS). Furthermore, urban gain modeling (UGM) contains various dimensions and components of uncertainties. Data sampling is important in UGM, and may cause the results of the models to contain many uncertainties as well as affects their precision and accuracy. A poorly sampled or biased dataset can lead to inaccurate predictions and decreased performance of the models. This paper aims to present and develop novel strategies for sampling and building training datasets that can enhance the performance of data-driven models. In other words, the present study used maximum entropy (ME) and ecological niche factor analysis (ENFA) models to select pure non-change samples with minimal uncertainty for training datasets in UGM of Isfahan and Tabriz cities in Iran. The urban gain of two time intervals of 1992–2002 and 2002–2012 were used for Tabriz City and two time intervals of 1994–2004 and 2004–2014 for Isfahan City. Nine and 14 urban gain drivers were used in the UGM of Isfahan and Tabriz cities, respectively. After the ME and ENFA models produced a training dataset with change and non-change samples with the lowest uncertainty, three well-known models, namely random forest (RF), artificial neural network (ANN), and support vector machine (SVM) were used for the modeling. Moreover, the ME and ENFA models that were used to investigate the uncertainty of the sampling procedure were used as the one-class prediction models. Compared to extant studies, the proposed ME – based sampling strategy increased the area under the receiver operating characteristic curve (AUROC), figure of merit, producer’s accuracy, and overall accuracy by 5.5%, 5%, 5%, and 3%, respectively, in the validation phase of Isfahan City and by 5%, 6%, 14%, and 17%, respectively, for Tabriz City. For Isfahan, the accuracies of ME (AUROC = 0.649) and ENFA (AUROC = 0.661) one – class models were closer to that of the ANN – ME (AUROC = 0.646), ANN – ENFA (AUROC = 0.619), and RF – ENFA (AUROC = 0.631) models but differed significantly from that of the RF – ME (AUROC = 0.737) model. For Tabriz, the accuracies of ME (AUROC = 0.657) and ENFA (AUROC = 0.688) one – class models were lower than that of the two class RF-ME (AUROC = 0.852), and ANN-ME (AUROC = 0.778) models. The results showed that the ME model was able to identify relatively pure non-change samples and properly remove impure non-change samples from the training dataset. This study discovered that binary models are preferable to one-class models, and showed that an optimal sampling strategy is an essential step in UGM as it can decrease uncertainty. As such, modelers must adopt efficient sampling methods.

Models for Studying the Distribution of Ticks and Tick-Borne Diseases in Animals: A Systematic Review and a Meta-Analysis with a Focus on Africa.

Ticks and tick-borne diseases (TTBD) are constraints to the development of livestock and induce potential human health problems. The worldwide distribution of ticks is not homogenous. Some places are ecologically suitable for ticks but they are not introduced in these areas yet. The absence or low density of hosts is a factor affecting the dissemination of the parasite. To understand the process of introduction and spread of TTBD in different areas, and forecast their presence, scientists developed different models (e.g., predictive models and explicative models). This study aimed to identify models developed by researchers to analyze the TTBD distribution and to assess the performance of these various models with a meta-analysis. A literature search was implemented with PRISMA protocol in two online databases (Scopus and PubMed). The selected articles were classified according to country, type of models and the objective of the modeling. Sensitivity, specificity and accuracy available data of these models were used to evaluate their performance using a meta-analysis. One hundred studies were identified in which seven tick genera were modeled, with Ixodes the most frequently modeled. Additionally, 13 genera of tick-borne pathogens were also modeled, with Borrelia the most frequently modeled. Twenty-three different models were identified and the most frequently used are the generalized linear model representing 26.67% and the maximum entropy model representing 24.17%. A focus on TTBD modeling in Africa showed that, respectively, genus Rhipicephalus and Theileria parva were the most modeled. A meta-analysis on the quality of 20 models revealed that maximum entropy, linear discriminant analysis, and the ecological niche factor analysis models had, respectively, the highest sensitivity, specificity, and area under the curve effect size among all the selected models. Modeling TTBD is highly relevant for predicting their distribution and preventing their adverse effect on animal and human health and the economy. Related results of such analyses are useful to build prevention and/or control programs by veterinary and public health authorities.

Application of modeling techniques for the identification the relationship between environmental factors and plant species in rangelands of Iran

The objective of the present research was to compare Ecological Niche Factor Analysis (ENFA) and Artificial Neural Networks (ANN) to determine the optimum threshold of plant species (Thymus kotschyanus Boiss and Hohen. and Dactylis glomerata L.) in rangelands of Ardabil province. Systematic random sampling of vegetation was performed, and an overall 111 sites were considered and divided into two groups, namely sites with plant species and sites without plant species. Five plots of one square meter were placed in each site. The size of the plots was based on the magnitude of one or double the average area of the most common plant species. Plots were positioned along a 40-m transect (from the bottom of the slope to high altitudes with a distance of 10 m between the plots). Soil sampling was done in a depth of 0–30 cm (selected based on the activity of root plant species). Maps of environmental factors were prepared in ArcGIS 10.4.1 software. The sensitivity and specificity were considered to specify the optimum threshold. The optimal threshold between plant species, determined by modeling methods, showed that the highest accuracy belonged to ENFA model in T. kotschyanus habitat (with optimum threshold = 0.62 and sensitivity = 0.58) and ANN model in D. glomerata habitat (with optimum threshold = 0.37 and sensitivity = 0.02) had the lowest accuracy.

Identification of skipjack tuna (Katsuwonus pelamis) pelagic hotspots applying a satellite remote sensing-driven analysis of ecological niche factors: A short-term run

Skipjack tuna (SJT) pelagic hotspots in the western North Pacific (WNP) were modelled using fishery and satellite remotely sensed data with Ecological Niche Factor Analysis (ENFA) models. Our objectives were to model and predict habitat hotspots for SJT and assess the monthly changes in sub-surface temperatures and mixed layer depths at fishing locations. SJT presence-only monthly resolved data, sea surface temperature, chlorophyll-a, diffuse attenuation coefficient, sea surface heights and surface wind speed were used to construct ENFA models and generate habitat suitability indices using a short-term dataset from March-November 2004. The suitability indices were then predicted for July-October (2007 and 2008). Monthly aggregated polygons of areas fished by skipjack tuna pole and line vessels were also overlaid on the predicted habitat suitability maps. Distributions of sub-surface temperatures and mixed layer depths (MLD) at fishing locations were also examined. Our results showed good fit for ENFA models, as indicated by the absolute validation index, the contrast validation index and the continuous Boyce index. The predicted hotspots showed varying concurrences when compared with 25-degree polygons derived from fished areas. Northward shifts in SJT hotspots corresponded with declining MLDs from March to September. The MLDs were shallower in summer and deeper in autumn and winter months. The habitat hotspots modeled using ENFA were consistent with the known ecology and seasonal migration pattern of SJT. The findings of this work, derived from a short-term dataset, enable identification of SJT hotspots in the WNP, thus contributing valuable information for future research on SJT habitat prediction models.

Raging elephants: effects of human disturbance on physiological stress and reproductive potential in wild Asian elephants.

Human disturbance has become a widespread threat to wildlife viability. The Asian elephant (Elephas maximus), an endangered and disturbance-prone species, is under severe threat from habitat loss and fragmentation, human-elephant conflict and poaching. Establishing connections between human disturbance, stress responses and reproduction is crucial for assessing the long-term survivability of a species and will provide critical information for conservation management. The current study investigated the effects of human disturbance on population-level stress responses and stress-related effects on reproductive potential of wild Asian elephants in Xishuangbanna Dai Autonomous Prefecture, China. We used a radioimmunoassay to measure the concentration of fecal cortisol and estradiol in 257 samples collected from five local populations at 15 sites over 4years. Human disturbance in Xishuangbanna was quantified based on the Ecological-Niche Factor Analysis model. We found that fecal cortisol concentrations were strongly positively correlated with the degree of human disturbance and increased markedly with the expansion of tea plantations. Percentage of non-stressed individuals in a population was higher depending on the extend of undisturbed area in their home ranges. Fecal estradiol concentrations decreased significantly with increasing stress levels. Our results suggest that human disturbance poses environmental challenges to wild Asian elephant populations, and chronic exposure to human disturbance could lead to population decline. The study demonstrates the efficacy of non-invasive endocrine monitoring for further informing management decisions and developing conservation strategies.

Species distribution models and local ecological knowledge in marine protected areas: The case of Os Miñarzos (Spain)

Habitat suitability (HS) of target species is assessed in the coastal region of Seno de Corcubión, including Os Miñarzos Marine Protected Area (MPA) in west Galicia, NW of Spain. Current MPA was outlined using local environmental knowledge (LEK). Our objective is to test whether maximization of HS of 12 species would select the same MPA as fishers' LEK. A detailed capture database and 13 layers summarizing ecogeographic variables (derived from bathymetry, satellite data and sea bottom classification map) have been used and several Ecological Niche Factor Analysis (ENFA) Species Distribution Models (SDM) have been tested to find the best HS prediction (according to a nonparametric index due to Boyce). Our SDM results show that the LEK defined MPA (comprising 20% of the total area) is over 70% more suitable than the rest of the region for all species but one (European seabass, only 40%). We conclude that stakeholders LEK methodology correctly identifies local species habitats, and that SDM, and in particular ENFA models, can be used to validate and keep track of the MPA boundaries.

Using fisheries by‐catch data to predict octocoral habitat suitability around South Georgia

AbstractAimFor many deep‐sea fisheries, there is an urgent management requirement for information on the presence of vulnerable marine ecosystems (VMEs). Gathering deep‐sea data using conventional techniques can be expensive and time‐consuming. One way to provide a relatively rapid assessment of VME presence is to use data from fisheries by‐catch and historical scientific observations. Our aim was to predict suitable habitat for octocorals around South Georgia and to estimate the extent to which octocoral habitat is currently protected by fisheries management measures. In addition, we attempted to determine the types of terrain in which octocorals and fishing activities occur.LocationSouth Georgia, sub‐Antarctic.MethodsA terrain map of South Georgia was created using Benthic Terrain Modeler (BTM). Georeferenced octocoral data were combined with environmental layers to create an octocoral habitat suitability map using ecological niche factor analysis (ENFA).ResultsMost octocoral by‐catch samples originated from narrow crest (an area representing shelf break and moraines) and steep (continental) slope terrains. Calcite saturation state and apparent oxygen utilization (AOU) were the most influential environmental parameters in determining highly suitable octocoral habitat. The ENFA model highlighted shelf‐break areas to be highly suitable habitat for octocorals and that 38% of this habitat around South Georgia lies within areas currently protected by fisheries management; a further 20% is below the 2000‐m fishing depth, meaning effectively that 58% of predicted highly suitable octocoral habitat is currently protected. Although these results indicate protection levels for octocoral habitat well above international standards/targets, the fishery remains active within a relatively concentrated shelf area at 700–2000 m, potentially having a large impact on the 42% of highly suitable octocoral habitat predicted to lie at these depths.Main conclusionsThis research demonstrates the potential for using fisheries by‐catch data to create habitat suitability maps that can inform fisheries management and future research.

Predicting range expansion of invasive raccoons in northern Iran using ENFA model at two different scales

Invasive alien species are considered to be one of the most important causes of extinction and decline of wild native species. The raccoon (Procyon lotor) is native to North and Central America but at present it also occurs in several European and Asian countries. In 1991, the raccoon was recorded for the first time in Iran from Lavandevil Wildlife Refuge. In order to examine how a variation in the extent of the study area influences habitat selection of the raccoon, we ran models at two different scales at Lavandevil Wildlife Refuge and the Gilan Province. We used the Ecological Niche Factor Analysis (ENFA) to describe the invasive raccoons' realized niche and to identify areas exposed to the invasion of the raccoon in northern Iran. Our results showed that the spatial distribution of the raccoon is heavily influenced by natural variables, landscape variables, and human-related variables at Lavandevil Wildlife Refuge scale and topography and vegetation variables at Gilan Province scale. This prediction indicates that the raccoon has a potential to become one of the most numerous mammals in northern Iran.

Prediction of Kashmir markhor habitat suitability in Chitral Gol National Park, Pakistan

The Kashmir markhor, Capra falconeri cashmiriensis, is classified as endangered throughout its range because of habitat loss, unsustainable hunting and competition with domestic livestock. We investigated environmental factors influencing species distribution in Chitral Gol National Park (CGNP) based on the ‘presence-only’ dataset and predicted the potential distribution of markhor in order to compute a habitat suitability (HS) map using ecological niche factor analysis (ENFA). Kashmir markhor presence-only data was recorded for a period of four years during 2003–2007 from 487 point locations in summer, spring and winter. The geographical position of animals and the related topographic and habitat variables were used to accurately assess species occurrence within the study area and to predict its distribution. ENFA is used to describe distribution patterns and to identify key variables influencing the ecological niche of markhor. The ENFA model revealed that markhor occurred at a mean elevation of 2818 m with a mean slope of 33.4°. The species requirements seem different from the average habitat conditions available in the study area. Whereas the specialisation factor indicated some specific habitat requirements of markhor, it was found that they still occupied a wider ecological niche. The model suggested that markhor avoid higher elevations but showed a strong relationship with comparatively steep slopes. ENFA computed 25% of the area of CGNP under suitable and optimal conditions for the long-term existence of Kashmir markhor in CGNP. ENFA computed a HS map for markhor showing its widespread distribution within the study area. Steep slopes and a lower to medium elevation range were mostly preferred by markhor.

Assessment of Predictive Habitat Models for Bighorn Sheep in California's Peninsular Ranges

ABSTRACT 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.

An improved approach for identifying suitable habitat of Sambar Deer (Cervus unicolor Kerr) using ecological niche analysis and environmental categorization: Case study at Phu-Khieo Wildlife Sanctuary, Thailand

To assess habitat suitability (HS) has become an increasingly important component of species/ecosystem management. HS assessment is usually based on presence/absence data related to environmental variables. An exception is Ecological Niche Factor Analysis (ENFA), which uses only presence data and which does not require absence data. Most HS modelling is based on input of all environmental parameters (EnvPs) without environmental categorization, and does not take into account species interaction and human intervention for an assessment of HS. In this study, the EnvPs are arranged into four features: geographical features, consumable features, human-factor features, and species–human interaction features. These features affect species with respect to movement, behavior and activity. The research presented here has used an already existing dataset of wildlife species and human activities/visitations, which was compiled during 2004–2006 in Phu-Khieo Wildlife Sanctuary (PKWS). Data from 2004 to 2005 were used to produce HS maps, while the data of 2006 were used for evaluating these maps. Sambar Deer (SD) was chosen to predict its own HS. Six HS maps of SD were analyzed using ENFA in the following manner: (1) inputting all EnvPs together, (2) inputting each feature, separately and (3) integrating the four resulting HS maps by model averaging. It was found that model averaging was capable of predicting the HS of SD more reliably than the model with all EnvPs put in together. Multiple linear regressions were computed between the HS map with all EnvPs and the HS maps with each feature. The results show that the HS map with only geographical features has the highest coefficient value (0.516) while the coefficient values of other HS maps with the above features are 0.296, 0.53 and −0.006, respectively. This indicates that the geographical features have an influence on the other features and that the predicting power is lower when all EnvPs are computed in the ENFA model. Therefore, in order to generate HS, each feature should at first be put into the model separately. Following that, the average of all features will be combined.

Assessing habitat quality for conservation using an integrated occurrence‐mortality model

Summary Habitat suitability models are usually produced using species presence or habitat selection, without taking into account the demographic performance of the population considered. These models cannot distinguish between sink and source habitats, causing problems especially for species with low reproductive rates and high susceptibility to low levels of mortality as in the case of the critically endangered Apennine brown bear Ursus arctos marsicanus. We developed a spatial model based on bear presence (2544 locations) and mortality data (37 locations) used as proxies for demographic performance. We integrated an occurrence and a mortality‐risk Ecological Niche Factor Analysis model into a final two‐dimensional model that can be used to distinguish between attractive sink‐like and source‐like habitat. Our integrated model indicates that a traditional habitat suitability model can provide misleading management and conservation indications, as 43% of the area suitable for the occurrence model is associated with high mortality risk. Areas of source‐like habitat for the Apennine bears (highly elevated areas rich in beech forests, far from roads, and with low human density and cultivated fields) are still present, including outside the currently occupied range. However, attractive sink‐like habitat (associated with high levels of pasture, low extent of mountain meadows, low steepness, low elevation, and closeness to secondary and urban roads) are present even inside protected areas. Synthesis and applications. Identifying attractive sink‐like habitat is crucial to prevent inappropriate management and to effectively address conservation issues: whereas existing source‐like habitats should be preserved to halt habitat loss and degradation, attractive sink‐like habitat should be managed to mitigate mortality risks or to decrease their attractiveness. In particular, area‐specific management interventions and proactive actions (increased patrolling, road closure, human activity management, threat monitoring, etc.) aimed at reducing human‐caused mortality are critical for the Apennine brown bear.

Living on the edge—Modelling habitat suitability for species at the edge of their fundamental niche

Predictive species distribution models have become increasingly common in conservation management. Among them, envelope-based approaches like the Ecological Niche Factor Analysis (ENFA) are particularly advantageous, as they require only presence data. Based on the assumption that the absolute frequency of species presence is a direct indicator of habitat suitability (HS), habitat suitability indices (HSI) are computed. However, this assumption may be misleading when the scarcity of optimal habitat forces most of the individuals to live in suboptimal conditions. This often happens when the environmental conditions in the study area represent only a marginal part of the species fundamental niche. In this study we propose three new HS algorithms for ENFA models, which address such ‘edge of niche’ situations. The first algorithm (area-adjusted median, M a) takes the availability of environmental conditions in the study area into account, the second (median + extremum, M e) addresses situations where the species’ optimum is at or beyond the extremum of the investigated environmental gradient, and the third (area-adjusted median + extremum, M ae) combines both approaches. These algorithms were applied to two populations of capercaillie ( Tetrao urogallus), situated in different positions relative to the environmental gradient represented in the respective study area, and compared with the classical median algorithm (M). We evaluated the models using cross-validation and a comparison with an expert model based on external data. In both study areas, the HS maps obtained with the three new algorithms differed visibly from those calculated with the median algorithm. Cross-validation and comparison with external data showed that the new algorithms always provided better models, with the extremum-based algorithms (M e and M ae) performing best. We conclude that the new algorithms can extend the applicability of ENFA-models to a broader range of conservation-relevant species by improving HS calculations for skewed species–habitat relationships in marginal habitats.

Evaluating MODIS data for mapping wildlife habitat distribution

Habitat distribution models have a long history in ecological research. With the development of geospatial information technology, including remote sensing, these models are now applied to an ever-increasing number of species, particularly those located in areas in which it is logistically difficult to collect habitat data in the field. Many habitat studies have used data acquired by multi-spectral sensor systems such as the Landsat Thematic Mapper (TM), due mostly to their availability and relatively high spatial resolution (30 m/pixel). The use of data collected by other sensor systems with lower spatial resolutions but high frequency of acquisitions has largely been neglected, due to the perception that such low spatial resolution data are too coarse for habitat mapping. In this study we compare two models using data from different satellite sensor systems for mapping the spatial distribution of giant panda habitat in Wolong Nature Reserve, China. The first one is a four-category scheme model based on combining forest cover (derived from a digital land cover classification of Landsat TM imagery acquired in June, 2001) with information on elevation and slope (derived from a digital elevation model obtained from topographic maps of the study area). The second model is based on the Ecological Niche Factor Analysis (ENFA) of a time series of weekly composites of WDRVI (Wide Dynamic Range Vegetation Index) images derived from MODIS (Moderate Resolution Imaging Spectroradiometer – 250 m/pixel) for 2001. A series of field plots was established in the reserve during the summer–autumn months of 2001–2003. The locations of the plots with panda feces were used to calibrate the ENFA model and to validate the results of both models. Results showed that the model using the seasonal variability of MODIS-WDRVI had a similar prediction success to that using Landsat TM and digital elevation model data, albeit having a coarser spatial resolution. This suggests that the phenological characterization of the land surface provides an appropriate environmental predictor for giant panda habitat mapping. Therefore, the information contained in remotely sensed data acquired with low spatial resolution but high frequency of acquisitions has considerable potential for mapping the habitat distribution of wildlife species.

Application of ecological-niche factor analysis in habitat assessment of giant pandas

Ecological-niche factor analysis (ENFA) is a multivariate approach to study geographic distribution of species on a large scale with only “presence” data. It has been widely applied in many fields including wildlife management, habitat assessment and habitat prediction. In this paper, this approach was applied in habitat suitability assessment for giant pandas in Pingwu County, Sichuan Province, China. With “presence” data of giant pandas and remote sensing data, habitat suitability of pandas in this county was evaluated based on ENFA model, and spatial distribution pattern of nature reserves and conservation gaps were then evaluated. The results show that giant pandas in this county prefer high-elevation zones (> 2128 m) dominated by coniferous forest, and mixed coniferous and deciduous broadleaf forest, and avoid deciduous broadleaf forest and shrubs. Pandas avoid staying at habitats with human disturbances. Farmland is a major factor threatening panda habitat. Panda habitat is mainly distributed in north and west of Pingwu with a total area of 234033 hm 2, 106345hm 2 for suitable habitat and 127688 hm 2 for marginally suitable habitat). 3 nature reserves were located in Pingwu, covering over 49.2% of total suitable habitat and 45.6% of total marginally suitable habitat. Although 47.2% of panda habitat was in reserves under protection, connectivity between reserves was weak and a conservation gap existed in the north part of Pingwu. Thus, a new nature reserve in Baima and Mupi should be established to link the isolated habitats.

Prospects for population expansion of the exotic aoudad (Ammotragus lervia; Bovidae) in the Iberian Peninsula: clues from habitat suitability modelling

ABSTRACTWe studied the geographical distribution and habitat suitability of an introduced ungulate, the aoudad (Ammotragus lervia), that is currently expanding its range in south‐eastern Iberian Peninsula. We assessed the niche of the species using Ecological Niche Factor Analysis (ENFA) on (1) environmental variables (climate and habitat type), and (2) potential aoudad landscape avoidance and human disturbance variables. We compared both niche descriptions to study the impact of human interference on niche selection of the species. ENFA models were calibrated using data on the population expanded from the original release location, in Sierra Espuña mountains, and validated using data from another free‐ranging population, originated independently in the Alicante province. The habitat suitability model for the purely environmental niche predicts a potential distribution along a SW–NE axis in the study area, following the Cordillera Sub‐Bética mountain range, being constrained by low winter precipitation, high altitude, high terrain slope, and the presence of forest. In addition to these ecological traits, roads and landscape use restricted the environmental range potentially available for the species. Since the aoudad is a potential competitor of native ungulates and a threat to endemic flora, prospects for its potential dispersion might be of great conservation value.

Descriptive biogeography of Tomicus (Coleoptera: Scolytidae) species in Spain

AbstractAim, location Tomicus (Coleoptera, Scolytidae) species are some of the principal pests of Eurasian forest and are represented by three coexisting species in Spain, Tomicus piniperda (Linnaeus, 1758), Tomicus destruens (Wollaston, 1865) and Tomicus minor (Harting, 1834). The distribution of two taxa are unknown as they have until recently been considered separate species. Therefore, we model the potential distribution centres and establish the potential distribution limits of Tomicus species in Iberia. We also assess the effectiveness of different models by comparing predicted results with observed data. These results will have application in forest pest management.Methods Molecular and morphological techniques were used to identify species from 254 specimens of 81 plots. For each plot, a Geographical Information System was used to extract a set of 14 environmental (one topographic, six climatic) and biotic variables (seven host tree distributions). General Additive Models and Ecological Niche Factor Analysis models are applied for modelling and predicting the potential distribution of the three especies of Tomicus.Results The results of both modelling methodologies are in agreement. Tomicus destruens is the predominant species in Spain, living in low and hot areas. Tomicus piniperda occurs in lower frequency and prefers wet and cold areas of north‐central Spain. We detected sympatric populations of T. destruens and T. piniperda in Northern coast of Spain, infesting mainly P. pinaster. Tomicus minor is the rarest species, and it occupies a fragmented distribution located in high and wet areas. The remarkable biotic variable is the distribution of P. sylvestris, incorporated into the models of T. destruens and T. piniperda.Main conclusions These results indicate that in wet areas of north‐central Spain where T. piniperda occurs (and possibly the high altitudes of the southern mountains), T. destruens has a climatic distribution limit. In the northern border of this area, both species overlap their distributions and some co‐occurrences were detected. Tomicus minor potentially occurs in high and wet fragmented areas.

Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns

Identification of areas containing high biological diversity (‘hotspots’) from species presence-only data has become increasingly important in species and ecosystem management when presence/absence data is unavailable. However, as presence-only data sets lack any information on absences and as they suffer from many biases associated with the ad hoc or non-stratified sampling, they are often assumed problematic and inadequate for most statistical modeling methods. In this paper, this supposition is investigated by comparing generalized additive models (GAM) fitted with 43 native New Zealand fern species presence/absence data, obtained from a survey of 19 875 forested plots, to GAM models and ecological niche factor analysis (ENFA) models fitted with identical presence data and, in the case of GAM models, computer generated ‘pseudo’ absences. By using the same presence data for all models, absence data is isolated as the varying factor allowing different techniques for generating ‘pseudo’ absences used in the GAM models to be analyzed and compared over three principal levels of investigation. GAM models fitted with an environmentally weighted distribution of ‘pseudo’ absences and ENFA models selected environmental variables more similar to the GAM presence/absence models than did the GAM models fitted with randomly distributed ‘pseudo’ absences. Average contributions for the GAM presence/absence model showed mean annual temperature and mean annual solar radiation as the most important factors followed by lithology. Comparisons of prediction results show GAM models incorporating an environmentally weighted distribution of ‘pseudo’ absences to be more closely correlated to the GAM presence/absence models than either the GAM models fitted with randomly selected ‘pseudo’ absences or the ENFA models. ENFA models were found to be the least correlated to the GAM presence/absence models. These latter models were also shown to give the most optimistic predictions overall, however, as ENFA predicts habitat suitability rather than probability of presence this was expected. Summation of species predictions used as a measure of species potential biodiversity ‘hotspots’ also shows ENFA models to give the highest and largest distribution of potential biodiversity. Additionally, GAM models incorporating ‘pseudo’ absences were more highly correlated to the GAM presence/absence model than was ENFA. However, ENFA identified more areas of potential biodiversity ‘hotspots’ similar to the GAM presence/absence model, than either GAM model incorporating ‘pseudo’ absences.