Predicted Habitat Suitability Research Articles

A Bioclimate-Based Maximum Entropy Model for Comperiella calauanica Barrion, Almarinez and Amalin (Hymenoptera: Encyrtidae) in the Philippines.

Simple SummaryThe discovery of Comperiella calauanica a parasitoid confirmed to be the major natural enemy of the invasive diaspidid, Aspidiotus rigidus, has led to the promise of biological control in sustainable pest management of this devastating coconut pest. In this study, we employed Maximum Entropy (Maxent) to develop a bioclimate-based species distribution model (SDM) for the parasitoid from presence-only data recorded from field surveys conducted in select points the Philippines. Results of assessment of the generated model point to its excellent power in predicting either suitability of habitat, or potential occurrence or distribution of C. calauanica. Since the parasitoid is highly host-specific, the model may also apply to A. rigidus. Field surveys in select areas in the Philippines confirmed the occurrence of the invasive coconut scale in areas predicted by the model as having considerable probability of occurrence, or habitat suitability. Our findings strongly suggest the potential utility of Maxent SDMs as tools for pest invasion forecasting and GIS-aided surveillance for integrated pest management (IPM).Comperiella calauanica is a host-specific endoparasitoid and effective biological control agent of the diaspidid Aspidiotus rigidus, whose outbreak from 2010 to 2015 severely threatened the coconut industry in the Philippines. Using the maximum entropy (Maxent) algorithm, we developed a species distribution model (SDM) for C. calauanica based on 19 bioclimatic variables, using occurrence data obtained mostly from field surveys conducted in A. rigidus-infested areas in Luzon Island from 2014 to 2016. The calculated the area under the ROC curve (AUC) values for the model were very high (0.966, standard deviation = 0.005), indicating the model’s high predictive power. Precipitation seasonality was found to have the highest relative contribution to model development. Response curves produced by Maxent suggested the positive influence of mean temperature of the driest quarter, and negative influence of precipitation of the driest and coldest quarters on habitat suitability. Given that C. calauanica has been found to always occur with A. rigidus in Luzon Island due to high host-specificity, the SDM for the parasitoid may also be considered and used as a predictive model for its host. This was confirmed through field surveys conducted between late 2016 and early 2018, which found and confirmed the occurrence of A. rigidus in three areas predicted by the SDM to have moderate to high habitat suitability or probability of occurrence of C. calauanica: Zamboanga City in Mindanao; Isabela City in Basilan Island; and Tablas Island in Romblon. This validation in the field demonstrated the utility of the bioclimate-based SDM for C. calauanica in predicting habitat suitability or probability of occurrence of A. rigidus in the Philippines.

Predicting the invasion of the acoustic niche: Potential distribution and call transmission efficiency of a newly introduced frog in Cuba

Leptodactylus fragilis is a recently introduced frog in Cuba, where it may impact local populations of amphibians in different ways. Here, we combined two methods to predict the invasion of the acoustic niche of Cuban amphibians by L. fragilis. We first use species distribution models to predict the spread and establishment of L. fragilis in Cuba. We then performed sound propagation experiments to evaluate the potential invasion of the acoustic niche in predicted suitable areas for the presence of L. fragilis. This species could have a successful establishment, spreading mainly in open areas, where its advertisement calls propagate efficiently, with low attenuation and discrete temporal-spectral degradation at short distances. The optimal transmission of acoustic signals in such areas might interfere with the acoustic communication of the endemic bufonid Peltophryne empusa, with potential negative effects on mate choice and breeding success. Predictions of habitat suitability, in combination with evidences of optimal transmission of advertisement calls of invasive amphibians, could be a valuable tool for rapid prediction of the potential impact of such species and the identification of prioritized areas to implement management strategies.

Maxent modelling for habitat suitability of vulnerable tree <i>Dalbergia latifolia</i> in Nepal

Roxb., commonly known as rosewood, is one of the highly valuable tropical timber species of Nepal. The tree species was widely distributed in the past, however, over-exploitation of natural habitat, deforestation, forest conversion for agriculture, illegal logging and the invasion of alien species resulted in the classification of this species as vulnerable by the IUCN (International Union for Conservation of Nature) category. So, the prediction of habitat suitability and potential distribution of the species is required to develop restoration mechanisms and conservation interventions. In this study, we modelled the suitable habitat of over the entire possible range of Nepal using a Maxent model. We compiled 23 environmental variables (19 bioclimatic, 3 topographic and a vegetative layer), however, only 12 least correlated variables along with 43 spatially representative presence locations were retained for model prediction. We used a receiver operating characteristic (ROC) curve to assess the model’s performance and a Jackknife procedure to evaluate the relative importance of predictor variables. The model was statistically significant with an area under the curve (AUC) value of 0.969. The internal Jackknife test indicated that elevation was the most important variable for the model prediction with 71.3% contribution followed by mean temperature of driest quarter (9.8%). The most (>0.6) suitable habitat for the was 235 484 hectares with large sections of area in two provinces whereas, the western most provinces were not suitable for as per Maxent model. The information presented here can provide a framework for nature conservation planning, monitoring and habitat management of this rare and endangered species.Dalbergia latifoliaD. latifoliaD. latifoliaD. latifolia

Habitat suitability evaluation for Paeonia decomposita, based on a MaxEnt model

Paeonia decomposita is on the IUCN’s Red List of endangered species, and occurs only in the northwest part of Sichuan Province, China. For the effective protection of the species, it is important to evaluate the suitability of potential habitats for P. decomposita and natural factors that influence the species. Based on the actual distribution points of P. decomposita in northwest Sichuan from 2016 to 2018, the Maximum Entropy Model (MaxEnt) was used to analyse the main factors affecting its habitat, and to predict suitable habitats. The results show that: (1) the model has high accuracy and is suitable for the prediction and evaluation of habitat suitability for P. decomposita; (2) temperature, slope, precipitation and moisture index will all greatly affect P. decomposita’s distribution; (3) the areas that are potentially suitable for P. decomposita are mainly in Mianyang, Aba, Ganzi and Liangshan, which are greatly affected by human activities; effective protection measures have not been taken. It is proposed that new reserves for the introduction of P. decomposita should be established in areas of high or moderately high suitability. A programme of cultivation of this rare species should also be set up.

Predicting suitable habitats of ginkgo biloba L. fruit forests in China

Ginkgo fruit can be used for food and medicine with high economic values. It is of great importance to ensure its sustainable production and genetic resource protection under climate change. In this study, niche models built with climate and soil variables, respectively, were used to assess the impact of climate change on its potential suitable habitat. The model performance was excellent for the climate model (AUC = 0.92) and good for the soil model (AUC = 0.84). Three climate variables (degree-days below zero, mean coldest month temperature, and mean annual precipitation) and two soil variables (subsoil cation exchange capacity and topsoil cation exchange capacity) were the main factors determining the distribution of ginkgo fruit forests. The level of predicted habitat suitability was consistent with the differences observed in fruit traits, suggesting that our model predictions make biological and economic sense. The high- and medium-suitable habitats of this species would decrease in future climates under both the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 climate change scenarios. This study contributed to a better understanding of the impact of climate change on ginkgo fruit forests and provided potential geographical areas for the cultivation and conservation of this species.

Predicting habitat suitability of Selaginella adunca A.Br. ex Hieron., an endangered and endemic fern-allies of Western Himalaya

Selaginella adunca is a quite distinct and rare species of Selaginella found in Western Himalaya. This species is reported only from few populations occurring in India and Nepal. Since most of its reported habitats are under anthropogenic pressure, therefore for proper conservation of this species it is necessary to mark the suitable habitat for its conservation and reintroduction. The present study was aimed to find out the suitable habitat of this species through ecological niche modelling (ENM) technique using Maxent model. This will also help in relocating the species in other preferred habitat type and its reintroduction as well.

Prediction of habitat suitability for the desert monitor (Varanus griseus caspius) under the influence of future climate change

Varanus griseus caspius (desert monitor) is a predator included appendix I of Convention on International Trade in Endangered Species; therefore, it is crucial to plan for its conservation. In this study, the suitable habitats of the desert monitor, Varanus griseus caspius, were evaluated in Iran, considering 10 environmental factors, for three periods of time: mid-Holocene, current, and years 2041–2060. Three individual models were produced to create an ensemble model: Generalized Boosting Model (GBM), Generalized Additive Model (GAM), and Random Forests (RF). Next, highly suitable areas in current and future periods were compared to the boundaries of conservation areas (CA). Among the different models projected, the ensemble model performed the best. The results indicated that so far, the distribution range of the species has shifted profoundly and will continue to grow in the future, while some currently suitable CAs will turn into unsuitable habitats for the species. We suggest further studies focus on redefining and broadening the CAs borders. Also, studying the species connectivity in current and potential future habitats can help to protect the species.

Quantifying Planococcus ficus (Hemiptera: Pseudococcidae) Invasion in Northern California Vineyards to Inform Management Strategy.

The spread and impact of invasive species in exotic ranges can be mitigated by increased understanding of pest invasion dynamics. Here, we used geospatial analyses and habitat suitability modeling to characterize the invasion of an important vineyard pest, vine mealybug (Planococcus ficus Signoret, Hemiptera: Pseudococcidae), using nearly 15,000 trapping records from throughout Napa County, California, between 2012 and 2017. Spatial autocorrelation among P. ficus detections was strongest at distances of ~250 m and detectable at regional scales (up to 40 km), estimates of the rate and directionality of spread were highly idiosyncratic, and P. ficus detection hotspots were spatiotemporally dynamic. Generalized linear model, boosted regression tree, and random forest modeling methods performed well in predicting habitat suitability for P. ficus. The most important predictors of P. ficus occurrence were a positive effect of precipitation in the driest month, and negative effects of elevation and distance to nearest winery. Our results indicate that 250-m quarantine and treatment zones around P. ficus detections are likely sufficient to encompass most local establishment and spread, and that implementing localized regulatory procedures may limit inadvertent P. ficus spread via anthropogenic pathways. Finally, surveys of P. ficus presence at >300 vineyard sites validated that habitat suitability estimates were significantly and positively associated with P. ficus frequency of occurrence. Our findings indicate that habitat suitability predictions may offer a robust tool for identifying areas in the study region at risk to future P. ficus invasion and prioritizing locations for early detection and preventative management efforts.

Predicting habitat suitability of filter-feeder communities in a shallow marine environment, New Zealand

The distribution of benthic ecosystems, dominated by filter-feeding communities, is highly influenced by the seabed geomorphology. However, the spatial variation in settlement of these species is also affected by near-bottom currents and any changes in light, nutrient concentration and food quality often associated with increases of suspended sediment concentrations within the water column. Detailed predictions of the geographic distribution of filter-feeder species and a deeper understanding of the physical processes influencing their distribution patterns is key for effective management and conservation. To date, predictive distribution modelling has been derived essentially from geomorphological parameters, mainly using spatially limited observations. In this study, seabed mapping, oceanographic modelling, hydrographic records and biological observations are integrated to provide high-resolution prediction of filter-feeder habitat distribution within Queen Charlotte Sound/Tōtaranui and Tory Channel/Kura Te Au, South Island of New Zealand. The aim is to evaluate potential suitable habitat areas for filter-feeders to inform where habitat restoration management should focus efforts to recover communities such as the horse mussel (Atrina zelandica) or the green-lipped mussel (Perna canaliculus), both of which have high economic impact in New Zealand. To accomplish this, Maximum Entropy (MaxEnt) predictive modelling was used to produce Habitat Suitability (HS) maps, using geomorphological parameters and seafloor classification information. Final HS maps also incorporated oceanographic and sediment dynamic information, showing that filter-feeder habitat distribution is highly influenced by the hydrodynamics and sedimentary processes apart from the seafloor geomorphology. Filter-feeder communities inhabit quiescent areas, limited by depth, slope and sediment type; and coincide with regions presenting low near-bottom currents and low turbidity levels. Additionally, the obtained results reveal the effects of the coastal settlements and major marine traffic routes, limiting the suitable habitats to areas with less human impact. This study demonstrates that a multidisciplinary approach is crucial to better predict the spatial distribution of benthic communities, which is key to improve benthic habitat restoration and recovery assessments.

Modelling habitat suitability in Jordan for the cutaneous leishmaniasis vector (Phlebotomus papatasi) using multicriteria decision analysis.

Cutaneous leishmaniasis (CL) is a zoonotic vector-borne neglected tropical disease transmitted by female Phlebotomine sand flies. It is distributed globally but a large proportion of cases (70–75%) are found in just ten countries. CL is endemic in Jordan yet there is a lack of robust entomological data and true reporting status is unknown. This study aimed to map habitat suitability of the main CL vector, Phlebotomus papatasi, in Jordan as a proxy for CL risk distribution to (i) identify areas potentially at risk of CL and (ii) estimate the human population at risk of CL. A literature review identified potential environmental determinants for P. papatasi occurrence including temperature, humidity, precipitation, vegetation, wind speed, presence of human households and presence of the fat sand rat. Each predictor variable was (a) mapped; (b) standardized to a common size, resolution and scale using fuzzy membership functions; (c) assigned a weight using the analytical hierarchy process (AHP); and (d) included within a multicriteria decision analysis (MCDA) model to produce monthly maps illustrating the predicted habitat suitability (between 0 and 1) for P. papatasi in Jordan. Suitability increased over the summer months and was generally highest in the north-western regions of the country and along the Jordan Valley, areas which largely coincided with highly populated parts of the country, including areas where Syrian refugee camps are located. Habitat suitability in Jordan for the main CL vector—P. papatasi—was heterogeneous over both space and time. Suitable areas for P. papatasi coincided with highly populated areas of Jordan which suggests that the targeted implementation of control and surveillance strategies in defined areas such as those with very high CL vector suitability (>0.9 suitability) would focus only on 3.42% of the country’s total geographic area, whilst still including a substantial proportion of the population at risk: estimates range from 72% (European Commission’s Global Human Settlement population grid) to 89% (Gridded Population of the World) depending on the human population density data used. Therefore, high impact public health interventions could be achieved within a reduced spatial target, thus maximizing the efficient use of resources.

Predicting Habitat Suitability of Snow Leopards in the Western Himalayan Mountains, India

The population of snow leopard (Panthera uncia) is declining across their range, due to poaching, habitat fragmentation, retaliatory killing, and a decrease of wild prey species. Obtaining information on rare and cryptic predators living in remote and rugged terrain is important for making conservation and management strategies. We used the Maximum Entropy (MaxEnt) ecological niche modeling framework to predict the potential habitat of snow leopards across the western Himalayan region, India. The model was developed using 34 spatial species occurrence points in the western Himalaya, and 26 parameters including, prey species distribution, temperature, precipitation, land use and land cover (LULC), slope, aspect, terrain ruggedness and altitude. Thirteen variables contributed 98.6% towards predicting the distribution of snow leopards. The area under the curve (AUC) score was high (0.994) for the training data from our model, which indicates predictive ability of the model. The model predicted that there was 42 432 km2 of potential habitat for snow leopards in the western Himalaya region. Protected status was available for 11 247 km2 (26.5%), but the other 31 185 km2 (73.5%) of potential habitat did not have any protected status. Thus, our approach is useful for predicting the distribution and suitable habitats and can focus field surveys in selected areas to save resources, increase survey success, and improve conservation efforts for snow leopards.

Multi-scale habitat selection and impacts of climate change on the distribution of four sympatric meso-carnivores using random forest algorithm

BackgroundThe habitat resources are structured across different spatial scales in the environment, and thus animals perceive and select habitat resources at different spatial scales. Failure to adopt the scale-dependent framework in species habitat relationships may lead to biased inferences. Multi-scale species distribution models (SDMs) can thus improve the predictive ability as compared to single-scale approaches. This study outlines the importance of multi-scale modeling in assessing the species habitat relationships and may provide a methodological framework using a robust algorithm to model and predict habitat suitability maps (HSMs) for similar multi-species and multi-scale studies.ResultsWe used a supervised machine learning algorithm, random forest (RF), to assess the habitat relationships of Asiatic wildcat (Felis lybica ornata), jungle cat (Felis chaus), Indian fox (Vulpes bengalensis), and golden-jackal (Canis aureus) at ten spatial scales (500–5000 m) in human-dominated landscapes. We calculated out-of-bag (OOB) error rates of each predictor variable across ten scales to select the most influential spatial scale variables. The scale optimization (OOB rates) indicated that model performance was associated with variables at multiple spatial scales. The species occurrence tended to be related strongest to predictor variables at broader scales (5000 m). Multivariate RF models indicated landscape composition to be strong predictors of the Asiatic wildcat, jungle cat, and Indian fox occurrences. At the same time, topographic and climatic variables were the most important predictors determining the golden jackal distribution. Our models predicted range expansion in all four species under future climatic scenarios.ConclusionsOur results highlight the importance of using multiscale distribution models when predicting the distribution and species habitat relationships. The wide adaptability of meso-carnivores allows them to persist in human-dominated regions and may even thrive in disturbed habitats. These meso-carnivores are among the few species that may benefit from climate change.

Balancing transferability and complexity of species distribution models for rare species conservation

AbstractAimSpecies distribution models (SDMs) are valuable for rare species conservation and are commonly used to extrapolate predictions of habitat suitability geographically to regions where species occurrence is unknown (i.e., transferability). Spatially structured cross‐validation can be used to infer transferability, yet, few studies have evaluated how delineation of cross‐validation folds affects model complexity and predictions. We developed SDMs using multiple cross‐validation approaches to understand the implications for predicting habitat suitability for northern Idaho ground squirrels, a rare, federally threatened species that has been extensively surveyed in regions where known populations occur, resulting in >8000 presence locations.LocationIdaho, USA.MethodsWe delineated cross‐validation folds by mimicking the manner in which predictions would be geographically extrapolated or by using existing dispersal barriers. We varied the distance between, number, and directionality of folds. We conducted a grid search on statistical regularization parameters to optimize model complexity, covering a range of values exceeding that typically implemented. For each cross‐validation approach, we selected optimal regularization and model complexity based on out‐of‐sample predictive ability.ResultsDelineation of cross‐validation folds substantially affected resulting model complexity and extrapolated predictions. All cross‐validation approaches resulted in models with apparently high out‐of‐sample predictive ability, yet optimal model complexity varied substantially among the approaches. Regularization demonstrated a noisy relationship between model complexity and prediction, where local optima in predictive performance were common at small values.Main conclusionSubtle modelling decisions can have large consequences for predictions of habitat suitability and transferability of SDMs. When transferability is the goal, cross‐validation approaches should be considered carefully and mimic the manner in which spatial extrapolation will occur, else overly complex models with inflated assessments of predictive accuracy may result. Further, spatially structured cross‐validation may not guard against over‐parameterization, and assessing a broader range of regularization parameters may be necessary to optimize model complexity for transferability.

First assessment of habitat suitability and connectivity for the golden jackal in north-eastern Italy

Compared with the rapid expansion across Europe, the golden jackal colonization of Italy is still limited and slow. No study focused on the habitat selection or landscape connectivity for this species was performed in Italy; thus, the potential distribution and dispersal patterns in the country remain unknown. Our objectives were to evaluate the suitability of the Friuli-Venezia Giulia region (north-eastern Italy) for the golden jackal, as well as to identify the ecological corridors connecting the areas currently occupied by the species. Corridors modelling allowed us both to hypothesize the dispersal dynamics occurring in the study region and to identify possible obstacles to future range expansion. We surveyed golden jackal presence in two study areas, covering an area of 500 km2, from March 2017 to February 2018. Using collected data, we modelled the species home-range scale habitat suitability based on an ensemble modelling approach. Subsequently, a habitat suitability prediction at a finer scale was used to estimate landscape resistance, starting from which, we modelled dispersal corridors among areas currently occupied by the species using a factorial least cost path and a cumulative resistant kernel approach. Our results indicated a moderate potential for large parts of the study region to support the occurrence of golden jackal family groups, whose presence seems to be mainly driven by the presence of wide areas covered by broadleaved forests and shrublands and by the absence of wide intensive agricultural areas. The predicted connectivity networks showed that three main permeable corridors are likely to connect golden jackal occurrence areas within the study region, while all the other corridors are characterized by a very low path density. Both the habitat selection and connectivity analyses showed a strong negative impact of the intensive cultivated plain on species stable presence and movement providing critical information for the conservation of the golden jackal in Italy.

Landscape Connectivity Limits the Predicted Impact of Fungal Pathogen Invasion.

Infectious diseases are major drivers of biodiversity loss. The risk of fungal diseases to the survival of threatened animals in nature is determined by a complex interplay between host, pathogen and environment. We here predict the risk of invasion of populations of threatened Mediterranean salamanders of the genus Lyciasalamandra by the pathogenic chytrid fungus Batrachochytrium salamandrivorans by combining field sampling and lab trials. In 494 samples across all seven species of Lyciasalamandra, B. salamandrivorans was found to be absent. Single exposure to a low (1000) number of fungal zoospores resulted in fast buildup of lethal infections in three L. helverseni. Thermal preference of the salamanders was well within the thermal envelope of the pathogen and body temperatures never exceeded the fungus’ thermal critical maximum, limiting the salamanders’ defense opportunities. The relatively low thermal host preference largely invalidates macroclimatic based habitat suitability predictions and, combined with current pathogen absence and high host densities, suggests a high probability of local salamander population declines upon invasion by B. salamandrivorans. However, the unfavorable landscape that shaped intraspecific host genetic diversity, lack of known alternative hosts and rapid host mortality after infection present barriers to further, natural pathogen dispersal between populations and thus species extinction. The risk of anthropogenic spread stresses the importance of biosecurity in amphibian habitats.

Political borders impact associations between habitat suitability predictions and resource availability

ContextBy linking species of conservation concern to their abiotic and biotic requirements, habitat suitability models (HSM) can assist targeted conservation measures. Yet, conservation measures may fail if HSM are unable to predict crucial resources. HSM are typically developed using remotely sensed land-cover classification data but not information on resources per se.ObjectivesWhile a certain land-cover class may correlate with crucial resources in the area of calibration, political boundaries can abruptly alter these associations. We investigate this potential discrepancy in a well-known study system highly relevant for farmland bird conservation.MethodsWe compared land cover, land-use intensity and resource availability between plots of highest habitat suitability for little owls (Athene noctua) among two neighbouring, but politically separated areas (i.e. south-western Germany vs. northern Switzerland).ResultsLand cover and land-use richness did not differ between German and Swiss plots. Yet there were marked differences in terms of land-use intensity and the availability of resources. Land-use intensity was significantly higher and resource availability lower in Swiss compared to German plots.ConclusionsWhile accounting well for remotely sensed data such as land cover, HSM may fail to predict land-use intensity and resources across borders. The relationship between geodata used as proxies and ecologically relevant resources may differ according to history, policies and socio-cultural context, constraining the viability of HSM across political borders. This study emphasises the need for fine-scale resource assessments complementing landscape-scale suitability models. Conservation measures need to consider the availability of crucial resources and their socio-economic moderators to be effective.

Political borders impact associations between habitat suitability predictions and resource availability

Political borders impact associations between habitat suitability predictions and resource availability

Keeping up with the times: Mapping range-wide habitat suitability for endangered species in a changing environment

Biologists and policy-makers have the difficult task of allocating limited resources to habitat conservation and management for endangered species in the face of changing environmental conditions. Satellite remote sensing can inform conservation because it is an efficient means to obtain environmental data over broad spatial and temporal extents. Yet, the challenges of accessing, processing, and analyzing remote sensing data hinder wider application of these techniques in conservation planning. We used Landsat data and hierarchical statistical models to link satellite-derived habitat measurements with abundance of endangered Yuma Ridgway's rails (Rallus obsoletus yumanensis) within the Lower Colorado River Basin and Salton Sink, USA. We addressed many of the challenges facing the application of remote sensing techniques by using the web-based, freely-available Google Earth Engine to process Landsat datasets, apply habitat models, and generate maps to predict habitat suitability at a fine spatial grain (30 m) across the range of the species. These maps are shareable, interactive, and easy to update annually as habitat conditions change using a Google Earth Engine App we developed. Thus, we provide a framework for building habitat suitability models and maps to help target adaptive habitat management over broad extents for sensitive species, enabling biologists to improve conservation and restoration efforts regularly as conditions change in highly variable ecosystems. We demonstrate this approach for Yuma Ridgway's rails, but our methods for merging hierarchical statistical models with open-source mapping software to describe spatial-temporal heterogeneity in habitat quality are applicable to any species, and are especially helpful to species inhabiting highly variable ecosystems.

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.

Predicting the invasion range for a highly polyphagous and widespread forest herbivore

Here we compare the environmental niche of a highly polyphagous forest Lepidoptera species, the winter moth (Operophtera brumata), in its native and invaded range. During the last 90 years, this European tree folivore has invaded North America in at least three regions and exhibited eruptive population behavior in both its native and invaded range. Despite its importance as both a forest and agricultural pest, neither the potential extent of this species’ invaded range nor the geographic source of invading populations from its native range are known. Here we fit a climatic niche model, based on the MaxEnt algorithm, to historical records of winter moth occurrence in its native range and compare predictions of suitable distributions to records from the invaded range. We modeled this distribution using three spatial bins to overcome sampling bias for data obtained from public databases and averaged the multi-continental suitable habitat prediction. Results indicate that this species is distributed across a wide range of climates in its native range but occupies a narrower range in its invaded habitat. Furthermore, the lack of a close fit between climatic conditions in parts of its invaded range and its known native range suggests the possibility that this species has adapted to new climatic conditions during the invasion process. These models can be used to predict suitable habitats for winter moth invasions worldwide and to gain insight into possible origins of North American populations.