Physical habitat modeling for river macroinvertebrate communities.

Habitat models rarely consider macroinvertebrate communities as ecological targets in rivers. Available approaches mainly focus on single macroinvertebrate species, not addressing the ecological needs and functionality of the whole community. This research aimed at providing an approach to model the habitat of the macroinvertebrate communities. The study was carried out in three rivers, located in Italy and characterized by a braiding morphology, gravel riverbeds, and low flows during the summer period. The approach is based on the recently developed Flow-T index, together with a Random Forest (RF) regression, which is employed to apply the Flow-T index at the mesohabitat scale. Using different datasets gathered from field data collection and 2D hydrodynamic simulations, the model was calibrated in the Trebbia River (2019 field campaign) and validated in the Trebbia, Taro, and Enza rivers (2020 field campaign). The RF model selected 12 mesohabitat descriptors as important for the macroinvertebrate community. These descriptors belong to different frequency classes of water depth, flow velocity, substrate grain size, and connectivity to the main river channel. The cross-validation R2 coefficient (R2cv) of the training dataset was 0.71, whereas the R2 coefficient (R2test) for the validation dataset was 0.63. The agreement between the simulated results and the experimental data shows sufficient accuracy and reliability. The outcomes of the study reveal that the model can identify the ecological response of the macroinvertebrate community to possible flow regime alterations and river morphological modifications. Lastly, the proposed approach allowed to extend the MesoHABSIM methodology, widely used for the fish habitat assessment, to a different ecological target community. Further applications of the approach can be related to ecological flows design in both perennial and non-perennial rivers, including river reaches in which fish fauna is absent.

The relationship between remotely-sensed spectral heterogeneity and bird diversity is modulated by landscape type.

To identify areas of high biodiversity and prioritize conservation efforts, it is crucial to understand the drivers of species richness patterns and their scale dependence. While classified land cover products are commonly used to explain bird species richness, recent studies suggest that unclassified remote-sensed images can provide equally good or better results. In our study, we aimed to investigate whether unclassified multispectral data from Landsat 8 can replace image classification for bird diversity modeling. Moreover, we also tested the Spectral Variability Hypothesis. Using the Atlas of Breeding Birds in the Czech Republic 2014-2017, we modeled species richness at two spatial resolutions of approx. 131 km2 (large squares) and 8 km2 (small squares). As predictors of the richness, we assessed 1) classified land cover data (Corine Land Cover 2018 database), 2) spectral heterogeneity (computed in three ways) and landscape composition derived from unclassified remote-sensed reflectance and vegetation indices. Furthermore, we integrated information about the landscape types (expressed by the most prevalent land cover class) into models based on unclassified remote-sensed data to investigate whether the landscape type plays a role in explaining bird species richness. We found that unclassified remote-sensed data, particularly spectral heterogeneity metrics, were better predictors of bird species richness than classified land cover data. The best results were achieved by models that included interactions between the unclassified data and landscape types, indicating that relationships between bird diversity and spectral heterogeneity vary across landscape types. Our findings demonstrate that spectral heterogeneity derived from unclassified multispectral data is effective for assessing bird diversity across the Czech Republic. When explaining bird species richness, it is important to account for the type of landscape and carefully consider the significance of the chosen spatial scale.

Modeling the Putative Ancient Distribution of Aedes togoi (Diptera: Culicidae).

The coastal rock pool mosquito, Aedes (Tanakius) togoi (Theobald) (Diptera: Culicidae), is found in coastal east Asia in climates ranging from subtropical to subarctic. However, a disjunct population in the Pacific Northwest of North America has an ambiguous heritage. Two potential models explain the presence of Ae. togoi in North America: ancient Beringian dispersal or modern anthropogenic introduction. Genetic studies have thus far proved inconclusive. Here we described the putative ancient distribution of Ae. togoi habitat in east Asia and examined the climatic feasibility of a Beringian introduction into North America using modern distribution records and ecological niche modeling of bioclimatic data from the last interglacial period (~120,000 BP), the last glacial maximum (~21,000 BP), and the mid-Holocene (~6000 BP). Our results suggest that suitable climatic conditions existed for Ae. togoi to arrive in North America through natural dispersal as well as to persist there until present times. Furthermore, we find that ancient distributions of suitable Ae. togoi habitat in east Asia may explain the genetic relationships between Ae. togoi populations identified in other studies. These findings indicate the utility of ecological niche modeling as a complementary tool for studying insect phylogeography.

Whales in warming water: Assessing breeding habitat diversity and adaptability in Oceania's changing climate.

In the context of a changing climate, understanding the environmental drivers of marine megafauna distribution is important for conservation success. The extent of humpback whale breeding habitats and the impact of temperature variation on their availability are both unknown. We used 19 years of dedicated survey data from seven countries and territories of Oceania (1,376 survey days), to investigate humpback whale breeding habitat diversity and adaptability to climate change. At a fine scale (1 km resolution), seabed topography was identified as an important influence on humpback whale distribution. The shallowest waters close to shore or in lagoons were favored, although humpback whales also showed flexible habitat use patterns with respect to shallow offshore features such as seamounts. At a coarse scale (1° resolution), humpback whale breeding habitats in Oceania spanned a thermal range of 22.3-27.8°C in August, with interannual variation up to 2.0°C. Within this range, both fine and coarse scale analyses of humpback whale distribution suggested local responses to temperature. Notably, the most detailed dataset was available from New Caledonia (774 survey days, 1996-2017), where encounter rates showed a negative relationship to sea surface temperature, but were not related to the El Niño Southern Oscillation or the Antarctic Oscillation from previous summer, a proxy for feeding conditions that may impact breeding patterns. Many breeding sites that are currently occupied are predicted to become unsuitably warm for this species (>28°C) by the end of the 21st century. Based on modeled ecological relationships, there are suitable habitats for relocation in archipelagos and seamounts of southern Oceania. Although distribution shifts might be restrained by philopatry, the apparent plasticity of humpback whale habitat use patterns and the extent of suitable habitats support an adaptive capacity to ocean warming in Oceania breeding grounds.

Postglacial Colonization of Northern Coastal Habitat by Bottlenose Dolphins: A Marine Leading-Edge Expansion?

Oscillations in the Earth's temperature and the subsequent retreating and advancing of ice-sheets around the polar regions are thought to have played an important role in shaping the distribution and genetic structuring of contemporary high-latitude populations. After the Last Glacial Maximum (LGM), retreating of the ice-sheets would have enabled early colonizers to rapidly occupy suitable niches to the exclusion of other conspecifics, thereby reducing genetic diversity at the leading-edge. Bottlenose dolphins (genus Tursiops) form distinct coastal and pelagic ecotypes, with finer-scale genetic structuring observed within each ecotype. We reconstruct the postglacial colonization of the Northeast Atlantic (NEA) by bottlenose dolphins using habitat modeling and phylogenetics. The AquaMaps model hindcasted suitable habitat for the LGM in the Atlantic lower latitude waters and parts of the Mediterranean Sea. The time-calibrated phylogeny, constructed with 86 complete mitochondrial genomes including 30 generated for this study and created using a multispecies coalescent model, suggests that the expansion to the available coastal habitat in the NEA happened via founder events starting ~15 000 years ago (95% highest posterior density interval: 4 900-26 400). The founders of the 2 distinct coastal NEA populations comprised as few as 2 maternal lineages that originated from the pelagic population. The low effective population size and genetic diversity estimated for the shared ancestral coastal population subsequent to divergence from the pelagic source population are consistent with leading-edge expansion. These findings highlight the legacy of the Late Pleistocene glacial cycles on the genetic structuring and diversity of contemporary populations.

Modeling Temperature Regime and Physical Habitat Impacts from Restored Streamflow.

Water infrastructure updates at Grand Canyon National Park (GRCA) provide an opportunity to restore natural flow to Bright Angel Creek, adding an additional ~20% to baseflow. This creek provides habitat for endangered humpback chub (Gila cypha) and invasive brown trout (Salmo trutta). We assess how increased flow may alter habitat and how that change may impact native and nonnative species using physical habitat modeling and statistical analysis of stream temperature data. We used System for Environmental Flow Analysis to calculate the change in habitat area for both species in the lower 2.1 km of the creek before and after the increased flow. Results indicate a slight increase in available habitat for juveniles of both species and a slight decrease for spawning brown trout. We used regression modeling to relate daily average air temperature to stream temperature and periods of increased discharge during water system maintenance were used to model the temperatures during likely future conditions. Both high and low stream temperature were dampened due to the added water resulting in fewer days with suitable spawning temperature and more days with suitable growth temperature for humpback chub. Fewer suitable days for growth upstream but more suitable days downstream, were predicted for brown trout. Compared to other streams that sustain populations of humpback chub, flow conditions for Bright Angel Creek provide fewer days throughout the year with suitable temperatures, particularly during the winter months. Juvenile humpback chub rearing may improve through the restoration of flow however the presence of predatory brown trout complicates the net beneficial impact.

Impacts of Channel Morphodynamics on Fish Habitat Utilization.

It is reasonable to expect that hydro-morphodynamic processes in fluvial systems can affect fish habitat availability, but the impacts of morphological changes in fluvial systems on fish habitat are not well studied. Herein we investigate the impact of morphological development of a cohesive meandering stream on the quality of fish habitat available for juvenile yellow perch (Perca flavescens) and white sucker (Catostomus commersonii). A three-dimensional (3D) morphodynamic model was first developed to simulate the hydro-morphodynamics of the study creek. The results of the morphodynamic model were then incorporated into a fish habitat availability assessment. The 3D hydro-morphodynamic model was successfully calibrated using an intensive acoustic Doppler current profiler (ADCP) spatial survey of the entire 3D velocity field and total station surveys of topographic changes in a meander bend in the study creek. Two fish sampling surveys were carried out at the beginning and the end of the study period to determine presence-absence of fish as an indicator of the habitat utilization of each fish species in the study reach. It was shown that morphological development of the stream was a significant factor for the observed changes in the habitat utilization of juvenile yellow perch. It is shown that juvenile yellow perch mostly utilized habitat where deposition occurred whereas they avoided areas of erosion. The results of this study and the proposed methodology could provide some insights into the potential impact of sediment transport processes on the fish occurrence, and distribution and has implications for management of small fluvial systems.

Quantifying shorebird habitat in managed wetlands by modeling shallow water depth dynamics.

Over 50% of Western Hemisphere shorebird species are in decline due to ongoing habitat loss and degradation. In some regions of high wetland loss, shorebirds are heavily reliant on a core network of remaining human-managed wetlands during migration journeys in the spring and fall. While most refuges have been designed and managed to match the habitat needs of waterfowl, shorebirds typically require much shallower water (<10 cm deep). Traditional static habitat modeling approaches at relatively coarse spatial and temporal resolution are insufficient to capture dynamic changes within this narrow water depth range. Our objectives were to (1) develop a method to quantify shallow water habitat distributions in inland non-tidal wetlands, and (2) to assess how water management practices affect the amount of shorebird habitat in Sacramento National Wildlife Refuge Complex. We produced water depth distributions and modeled optimal habitat (<10 cm deep) within 23 managed wetlands using high-resolution topography and fixed-point water depth records. We also demonstrated that habitat availability, specifically suitable water depth ranges, can be tracked from satellite imagery and high-resolution topography. We found that wetlands with lower topographic roughness may have a higher potential to provide shorebird habitat and that strategically reducing water levels could increase habitat extent. Over 50% of the wetlands measured provided optimal habitat across <10% of their area at the peak of migration in early April, and most provided a brief duration of shallow water habitat. Reducing water volumes could increase the proportion of optimal habitat by 1-1,678% (mean = 294%) compared to actual volumes measured at peak spring migration in 2016. For wetlands with a high habitat potential, beginning wetland drawdown earlier and extending drawdown time could dramatically improve habitat conditions at the peak of shorebird migration. Our approach can be adapted to track dynamic hydrologic changes at broader spatial scales as additional high-resolution topographic (e.g., lidar, drone imagery photogrammetry) and optical remote sensing data (e.g., planet imagery, drone photography) become available.

Codling Moth (Lepidoptera: Tortricidae) Establishment in China: Stages of Invasion and Potential Future Distribution.

Codling moth (Cydia pomonella L.) is an internal feeding pest of apples and can cause substantial economic losses to fruit growers due to larval feeding which in turn degrades fruit quality and can result in complete crop loss if left uncontrolled. Although this pest originally developed in central Asia, it was not known to occur in China until 1953. For the first three decades the spread of codling moth within China was slow. Within the last three decades, addition of new commercial apple orchards and improved transportation, this pest has spread to over 131 counties in seven provinces in China. We developed regional (China) and global ecological niche models using MaxEnt to identify areas at highest potential risk of codling moth establishment and spread. Our objectives were to 1) predict the potential distribution of codling moth in China, 2) identify the important environmental factors associated with codling moth distribution in China, and 3) identify the different stages of invasion of codling moth in China. Human footprint, annual temperature range, precipitation of wettest quarter, and degree days ≥10 °C were the most important predictors associated with codling moth distribution. Our analysis identified areas where codling moth has the potential to establish, and mapped the different stages of invasion (i.e., potential for population stabilization, colonization, adaptation, and sink) of codling moth in China. Our results can be used in effective monitoring and management to stem the spread of codling moth in China.

Environmental flow in the River Ondas basin in Bahia, Brazilian Cerrado.

This paper aimed to estimate the environmental flow of a water basin located in the Brazilian Cerrado using the bidimensional model River2D. The study was carried out in a stretch of the lower portion of the River Ondas in the western part of the state of Bahia, Brazil. To carry out the ecohydrological modeling, the following were used: topobathymetry, hydraulic characterization, the streamflows with the probability of non-exceedances (Q50, Q60, Q70, Q80, Q90, and Q95), and the Habitat Suitability Index for species of the genus Hypostomus. In the River2D, the weighted usable areas (WUAs) pertaining to the streamflows associated with different non-exceedances were simulated for the later construction of optimization and identification matrices of the streamflows that maximize the habitat area throughout the year. For juvenile Hypostomus, WUA increased as streamflow increased, with higher values associated with Q50. For adult specimens, lower WUA values were observed associated with Q50, while higher values were associated with Q95, which shows a preference for lower streamflows. The environmental flows found for the stretch of the River Ondas varied over the course of the year. The lowest environmental flows were observed in September (30.31 m(3) s(-1)) and October (29.98 m(3) s(-1)), while the highest were observed in February (44.22 m(3) s(-1)) and March (43.16 m(3) s(-1)). The environmental flow regime obtained restricts the water availability in the basin, for the purpose of water capture, which shows the importance of ecohydrological studies in forming a basis for water resource management actions.

Predicting bottlenose dolphin distribution along Liguria coast (northwestern Mediterranean Sea) through different modeling techniques and indirect predictors.

Habitat modeling is an important tool to investigate the quality of the habitat for a species within a certain area, to predict species distribution and to understand the ecological processes behind it. Many species have been investigated by means of habitat modeling techniques mainly to address effective management and protection policies and cetaceans play an important role in this context. The bottlenose dolphin (Tursiops truncatus) has been investigated with habitat modeling techniques since 1997. The objectives of this work were to predict the distribution of bottlenose dolphin in a coastal area through the use of static morphological features and to compare the prediction performances of three different modeling techniques: Generalized Linear Model (GLM), Generalized Additive Model (GAM) and Random Forest (RF). Four static variables were tested: depth, bottom slope, distance from 100 m bathymetric contour and distance from coast. RF revealed itself both the most accurate and the most precise modeling technique with very high distribution probabilities predicted in presence cells (90.4% of mean predicted probabilities) and with 66.7% of presence cells with a predicted probability comprised between 90% and 100%. The bottlenose distribution obtained with RF allowed the identification of specific areas with particularly high presence probability along the coastal zone; the recognition of these core areas may be the starting point to develop effective management practices to improve T. truncatus protection.

Escaping to the summits: phylogeography and predicted range dynamics of Cerastium dinaricum, an endangered high mountain plant endemic to the western Balkan Peninsula.

The Balkans are a major European biodiversity hotspot, however, almost nothing is known about processes of intraspecific diversification of the region's high-altitude biota and their reaction to the predicted global warming. To fill this gap, genome size measurements, AFLP fingerprints, plastid and nuclear sequences were employed to explore the phylogeography of Cerastium dinaricum. Range size changes under future climatic conditions were predicted by niche-based modeling. Likely the most cold-adapted plant endemic to the Dinaric Mountains in the western Balkan Peninsula, the species has conservation priority in the European Union as its highly fragmented distribution range includes only few small populations. A deep phylogeographic split paralleled by divergent genome size separates the populations into two vicariant groups. Substructure is pronounced within the southeastern group, corresponding to the area's higher geographic complexity. Cerastium dinaricum likely responded to past climatic oscillations with altitudinal range shifts, which, coupled with high topographic complexity of the region and warmer climate in the Holocene, sculptured its present fragmented distribution. Field observations revealed that the species is rarer than previously assumed and, as shown by modeling, severely endangered by global warming as viable habitat was predicted to be reduced by more than 70% by the year 2080.

Assessing risk to birds from industrial wind energy development via paired resource selection models.

When wildlife habitat overlaps with industrial development animals may be harmed. Because wildlife and people select resources to maximize biological fitness and economic return, respectively, we estimated risk, the probability of eagles encountering and being affected by turbines, by overlaying models of resource selection for each entity. This conceptual framework can be applied across multiple spatial scales to understand and mitigate impacts of industry on wildlife. We estimated risk to Golden Eagles (Aquila chrysaetos) from wind energy development in 3 topographically distinct regions of the central Appalachian Mountains of Pennsylvania (United States) based on models of resource selection of wind facilities (n = 43) and of northbound migrating eagles (n = 30). Risk to eagles from wind energy was greatest in the Ridge and Valley region; all 24 eagles that passed through that region used the highest risk landscapes at least once during low altitude flight. In contrast, only half of the birds that entered the Allegheny Plateau region used highest risk landscapes and none did in the Allegheny Mountains. Likewise, in the Allegheny Mountains, the majority of wind turbines (56%) were situated in poor eagle habitat; thus, risk to eagles is lower there than in the Ridge and Valley, where only 1% of turbines are in poor eagle habitat. Risk within individual facilities was extremely variable; on average, facilities had 11% (SD 23; range = 0-100%) of turbines in highest risk landscapes and 26% (SD 30; range = 0-85%) of turbines in the lowest risk landscapes. Our results provide a mechanism for relocating high-risk turbines, and they show the feasibility of this novel and highly adaptable framework for managing risk of harm to wildlife from industrial development.

Combining potential and realized distribution modeling of telemetry data for a bycatch risk assessment.

Establishing marine species distributions is essential for guiding management and can be estimated by identifying potential favorable habitat at a population level and incorporating individual-level information (e.g., movement constraints) to inform realized space use. In this research, we applied a combined modeling approach to tracking data of adult female and juvenile South American sea lions (Otaria flavescens; n = 9) from July to November 2011 to make habitat predictions for populations in northern Chile. We incorporated topographic and oceanographic predictors with sea lion locations and environmentally based pseudo-absences in a generalized linear model for estimating population-level distribution. For the individual approach, we used a generalized linear mixed-effects model with a negative exponential kernel variable to quantify distance-dependent movement from the colony. Spatial predictions from both approaches were combined in a bivariate color map to identify areas of agreement. We then used a GIS-based risk model to characterize bycatch risk in industrial and artisanal purse-seine fisheries based on fishing set data from scientific observers and artisanal fleet logs (2010-2015), the bivariate sea lion distribution map, and criteria ratings of interaction characteristics. Our results indicate population-level associations with productive, shallow, low slope waters, near to river-mouths, and with high eddy activity. Individual distribution was restricted to shallow slopes and cool waters. Variation between approaches may reflect intrinsic factors restricting use of otherwise favorable habitat; however, sample size was limited, and additional data are needed to establish the full range of individual-level distributions. Our bycatch risk outputs identified highest risk from industrial fisheries operating nearshore (within 5 NM) and risk was lower, overall, for the artisanal fleet. This research demonstrates the potential for integrating potential and realized distribution models within a spatial risk assessment and fills a gap in knowledge on this species' distribution, providing a basis for targeting bycatch mitigation outreach and interventions.

Eddy impacts on abundance and habitat distribution of a large predatory squid off Peru.

The pelagic cephalopod species jumbo flying squid Dosidicus gigas is ecologically and economically important in the Humboldt ecosystem off Peru. This squid species is sensitive to oceanic environmental changes, and regional oceanographical variability is one of the important factors driving its redistribution. Off Peruvian waters, mesoscale eddies are ubiquitous and dominate the biogeochemical processes in this region. This study first explored the role of mesoscale eddies in regulating the environments and their effects on the abundance and habitat distribution of D. gigas off Peru by analyzing squid distribution in eddy-centric coordinates and building a habitat suitability index (HSI) model. Results indicated that the abundance and habitat distribution of D. gigas in mesoscale eddies varied across months, with significant differences observed between anticyclonic eddies (AE) and cyclonic eddies (CE). In AE, a higher abundance and proportion of suitable habitat occurred. While in CE, the abundance was relatively low and the suitable habitat was relatively less, concentrating at the periphery of CE. Based on the HSI model results, sea surface temperature (SST) and 50 m water temperature (T50m) in AE were more favorable for D. gigas, which was 0.3-0.5 °C lower than that in CE, yielding high-quality habitats and higher abundance of D. gigas. Our findings emphasized that mesoscale eddies have a significant impact on water temperature conditions and nutrient concentrations off Peruvian waters.

Mapping the Future: Revealing Habitat Preferences and Patterns of the Endangered Chilean Dolphin in Seno Skyring, Patagonia.

Species distribution modeling helps understand how environmental factors influence species distribution, creating profiles to predict presence in unexplored areas and assess ecological impacts. This study examined the habitat use and population ecology of the Chilean dolphin in Seno Skyring, Chilean Patagonia. We used three models-random forest (RF), generalized linear model (GLM), and artificial neural network (ANN)-to predict dolphin distribution based on environmental and biotic data like water temperature, salinity, and fish farm density. Our research has determined that the RF model is the most precise tool for predicting the habitat preferences of Chilean dolphins. The results indicate that these dolphins are primarily located within six kilometers of the coast, strongly correlating with areas featuring numerous fish farms, sheltered waters close to the shore with river inputs, and shallow productive zones. This suggests a potential association between dolphin presence and fish-farming activities. These findings can guide targeted conservation measures, such as regulating fish-farming practices and protecting vital coastal areas to improve the survival prospects of the Chilean dolphin. Given the extensive fish-farming industry in Chile, this research highlights the need for greater knowledge and comprehensive conservation efforts to ensure the species' long-term survival. By understanding and mitigating the impacts of fish farming and other human activities, we can better protect the habitat and well-being of Chilean dolphins.

Machine and deep learning approaches to understand and predict habitat suitability for seabird breeding.

The way animals select their breeding habitat may have great impacts on individual fitness. This complex process depends on the integration of information on various environmental factors, over a wide range of spatiotemporal scales. For seabirds, breeding habitat selection integrates both land and sea features over several spatial scales. Seabirds explore these features prior to breeding, assessing habitats' quality. However, the information-gathering and decision-making process by seabirds when choosing a breeding habitat remains poorly understood. We compiled 49 historical records of larids colonies in Cuba from 1980 to 2020. Then, we predicted potentially suitable breeding sites for larids and assessed their breeding macrohabitat selection, using deep and machine learning algorithms respectively. Using a convolutional neural network and Landsat satellite images we predicted the suitability for nesting of non-monitored sites of this archipelago. Furthermore, we assessed the relative contribution of 18 land- and marine-based environmental covariates describing macrohabitats at three spatial scales (i.e. 10, 50 and 100 km) using random forests. Convolutional neural network exhibited good performance at training, validation and test (F1-scores >85%). Sites with higher habitat suitability (p > .75) covered 20.3% of the predicting area. Larids breeding macrohabitats were sites relatively close to main islands, featuring sparse vegetation cover and high chlorophyll-a concentration at sea in 50 and 100 km around colonies. Lower sea surface temperature at larger spatial scales was determinant to distinguish the breeding from non-breeding sites. A more comprehensive understanding of the seabird breeding macrohabitats selection can be reached from the complementary use of convolutional neural networks and random forest models. Our analysis provides crucial knowledge in tropical regions that lack complete and regular monitoring of seabirds' breeding sites.

Two Decades of Coastal Dolphin Population Surveys in Israel, Eastern Mediterranean.

Along the Mediterranean coast of Israel, two near-shore dolphin species are prevalent; Tursiops truncatus (least concern, IUCN) and Delphinus delphis (endangered, IUCN). Ship-board surveys and sporadic sightings over the last two decades have shown that the two differ in distribution-T. truncatus is found along the entire coast and D. delphis only in the south. The environmental and anthropological factors affecting these species' spatial distribution and determining their habitat preferences in this area are largely unknown. This work is a first attempt at summarizing 20 years of observations and studying habitat preferences for both species, by use of Generalized Additive Models. T. truncatus was found to be present in all areas of the continental shelf where survey effort coverage was sufficient, with a high affinity towards bottom trawlers. Model results showed D. delphis distribution to be associated to (shallow) water depths, though the factors driving their limited latitudinal distribution currently remain unknown. It is evident that T. truncatus and D. delphis are present in segregated areas of the Israeli continental shelf and T. truncatus currently sustains a delicate balance with continuously shifting human activities, while the drivers of D. delphis distribution are more specified, yet still not fully understood.

Distinctive, fine-scale distribution of Eastern Caribbean sperm whale vocal clans reflects island fidelity rather than environmental variables.

Environmental variables are often the primary drivers of species' distributions as they define their niche. However, individuals, or groups of individuals, may sometimes adopt a limited range within this larger suitable habitat as a result of social and cultural processes. This is the case for Eastern Caribbean sperm whales. While environmental variables are reasonably successful in describing the general distribution of sperm whales in the region, individuals from different cultural groups have distinct distributions around the Lesser Antilles islands. Using data collected over 2 years of dedicated surveys in the Eastern Caribbean, we conducted habitat modeling and habitat suitability analyses to investigate the mechanisms responsible for such fine-scale distribution patterns. Vocal clan-specific models were dramatically more successful at predicting distribution than general species models, showing how a failure to incorporate social factors can impede accurate predictions. Habitat variation between islands did not explain vocal clan distributions, suggesting that cultural group segregation in the Eastern Caribbean sperm whale is driven by traditions of site/island fidelity (most likely maintained through conformism and homophily) rather than habitat type specialization. Our results provide evidence for the key role of cultural knowledge in shaping habitat use of sperm whales within suitable environmental conditions and highlight the importance of cultural factors in shaping sperm whale ecology. We recommend that social and cultural information be incorporated into conservation and management as culture can segregate populations on fine spatial scales in the absence of environmental variability.

Modeling Geographic Uncertainty in Current and Future Habitat for Potential Populations of Ixodes pacificus (Acari: Ixodidae) in Alaska.

Ixodes pacificus Cooley & Kohls is the primary vector of Lyme disease spirochetes to humans in the western United States. Although not native to Alaska, this tick species has recently been found on domestic animals in the state. Ixodes pacificus has a known native range within the western contiguous United States and southwest Canada; therefore, it is not clear if introduced individuals can successfully survive and reproduce in the high-latitude climate of Alaska. To identify areas of suitable habitat within Alaska for I. pacificus, we used model parameters from two existing sets of ensemble habitat distribution models calibrated in the contiguous United States. To match the model input covariates, we calculated climatic and land cover covariates for the present (1980-2014) and future (2070-2100) climatologies in Alaska. The present-day habitat suitability maps suggest that the climate and land cover in Southeast Alaska and portions of Southcentral Alaska could support the establishment of I. pacificus populations. Future forecasts suggest an increase in suitable habitat with considerable uncertainty for many areas of the state. Repeated introductions of this non-native tick to Alaska increase the likelihood that resident populations could become established.