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This modeling project was funded by the Navy, Commander, U.S. Pacific Fleet (U.S. Navy), the Bureau of Ocean Energy Management (BOEM), and by the National Oceanic and Atmospheric Administration (NOAA), National Marine Fisheries Service (NMFS), Southwest Fisheries Science Center (SWFSC). The 2018 survey was conducted as part of the Pacific Marine Assessment Program for Protected Species (PacMAPPS), a collaborative effort between NOAA Fisheries, the U.S. Navy, and BOEM to collect data necessary to produce updated abundance estimates for cetaceans in the CCE study area. BOEM funding was provided via Interagency Agreement (IAA) M17PG00025, and Navy funding via IAA N0007018MP4C560, under the Mexican permit SEMARNAT/SGPA/DGVS/013212/18. The methods used to derive uncertainty estimates were developed as part of “DenMod: Working Group for the Advancement of Marine Species Density Surface Modeling” funded by OPNAV N45 and the SURTASS LFA Settlement Agreement, and managed by the U.S. Navy’s Living Marine Resources (LMR) program under Contract No. N39430-17-C-1982. Other permits included INEGI: Oficio núm. 400./331/2018, INEGI.GMA 1.03 SAGARPA de Oficio B00.02.04.1530/2018 NMFS Permit No. 19091. The distribution of wide-ranging cetacean species often cross national or jurisdictional boundaries, which creates challenges for monitoring populations and managing anthropogenic impacts, especially if data are only available for a portion of the species’ range. Many species found off the U.S. West Coast are known to have continuous distributions into Mexican waters, with highly variable abundance within the U.S. portion of their range. This has contributed to annual variability in design-based abundance estimates from systematic shipboard surveys off the U.S. West Coast, particularly for the abundance of warm temperate species such as striped dolphin, Stenella coeruleoalba, which increases off California during warm-water conditions and decreases during cool-water conditions. Species distribution models (SDMs) can accurately describe shifts in cetacean distribution caused by changing environmental conditions, and are increasingly used for marine species management. However, until recently, data from waters off the Baja California peninsula, México, have not been available for modeling species ranges that span from Baja California to the U.S. West Coast. In this study, we combined data from 1992–2018 shipboard surveys to develop SDMs off the Pacific Coast of Baja California for ten taxonomically diverse cetaceans. We used a Generalized Additive Modeling framework to develop SDMs based on line-transect surveys and dynamic habitat variables from the Hybrid Coordinate Ocean Model (HYCOM). Models were developed for ten species: long- and short-beaked common dolphins (Delphinus delphis delphis and D. d. bairdii), Risso’s dolphin (Grampus griseus), Pacific white-sided dolphin (Lagenorhynchus obliquidens), striped dolphin, common bottlenose dolphin (Tursiops truncatus), sperm whale (Physeter macrocephalus), blue whale (Balaenoptera musculus), fin whale (B. physalus), and humpback whale (Megaptera novaeangliae). The SDMs provide the first fine-scale (approximately 9 x 9 km grid) estimates of average species density and abundance, including spatially-explicit measures of uncertainty, for waters off the Baja California peninsula. Results provide novel insights into cetacean ecology in this region as well as quantitative spatial data for the assessment and mitigation of anthropogenic impacts. Publisher PDF

International audience; Under climate change, future species assemblages will be driven by the movements and poleward shift of local species and the arrival of more thermophilic species from lower latitudes. To evaluate the impacts of climate change on marine communities in the Bay of Biscay, we used the hierarchical filters modelling approach. Models integrated 3 vertical depth layers and considered 2 Intergovernmental Panel on Climate Change (IPCC) scenarios (Representative Concentration Pathway, RCP2.6 and RCP8.5) and 2 periods (2041-2050 and 2091-2100) to simulate potential future species distributions. Results predicted potentially suitable future ranges for 163 species as well as future arrivals of non-indigenous southern species. We aggregated these results to map changes in species assemblages. Results revealed that coastal areas would undergo the highest species loss among the Bay of Biscay species, depending on their vertical habitat (benthic, demersal, benthopelagic or pelagic). Benthic and demersal species were projected to experience a westward shift, which would induce a deepening of those species. In contrast, pelagic species were projected to shift northward. The potential ecological niche for half of the studied species, mostly benthic and demersal, was projected to decrease under climate change. In addition, a high rate of southern species arrivals is expected (+28%). Assessment of community composition showed high species replacement within the 0-50 m isobath, driven by the replacement of native species by southern ones. This could lead to a major reorganization of trophic networks and have socio-economic impacts.

Species distribution models (SDMs) are important management tools for highly mobile marine species because they provide spatially and temporally explicit information on animal distribution. Two prevalent modeling frameworks used to develop SDMs for marine species are generalized additive models (GAMs) and boosted regression trees (BRTs), but comparative studies have rarely been conducted; most rely on presence‐only data; and few have explored how features such as species distribution characteristics affect model performance. Since the majority of marine species BRTs have been used to predict habitat suitability, we first compared BRTs to GAMs that used presence/absence as the response variable. We then compared results from these habitat suitability models to GAMs that predict species density (animals per km2) because density models built with a subset of the data used here have previously received extensive validation. We compared both the explanatory power (i.e., model goodness of fit) and predictive power (i.e., performance on a novel dataset) of the GAMs and BRTs for a taxonomically diverse suite of cetacean species using a robust set of systematic survey data (1991–2014) within the California Current Ecosystem. Both BRTs and GAMs were successful at describing overall distribution patterns throughout the study area for the majority of species considered, but when predicting on novel data, the density GAMs exhibited substantially greater predictive power than both the presence/absence GAMs and BRTs, likely due to both the different response variables and fitting algorithms. Our results provide an improved understanding of some of the strengths and limitations of models developed using these two methods. These results can be used by modelers developing SDMs and resource managers tasked with the spatial management of marine species to determine the best modeling technique for their question of interest., Species distribution models (SDMs) were developed for a taxonomically diverse suite of cetaceans using a robust set of systematic survey data within the California Current Ecosystem. Both boosted regression tress and generalized additive models were developed and their explanatory and predictive performance compared in light of features such as species distribution characteristics. Results have direct relevance for understanding the accuracy of SDMs and associated implications for marine management and conservation.

Seafloor characteristics can help in the prediction of fish distribution, which is required for fisheries and conservation management. Despite this, only 5%–10% of the world's seafloor has been mapped at high resolution, as it is a time‐consuming and expensive process. Multibeam echo‐sounders (MBES) can produce high‐resolution bathymetry and a broad swath coverage of the seafloor, but require greater financial and technical resources for operation and data analysis than singlebeam echo‐sounders (SBES). In contrast, SBES provide comparatively limited spatial coverage, as only a single measurement is made from directly under the vessel. Thus, producing a continuous map requires interpolation to fill gaps between transects. This study assesses the performance of demersal fish species distribution models by comparing those derived from interpolated SBES data with full‐coverage MBES distribution models. A Random Forest classifier was used to model the distribution of Abalistes stellatus, Gymnocranius grandoculis, Lagocephalus sceleratus, Loxodon macrorhinus, Pristipomoides multidens, and Pristipomoides typus, with depth and depth derivatives (slope, aspect, standard deviation of depth, terrain ruggedness index, mean curvature, and topographic position index) as explanatory variables. The results indicated that distribution models for A. stellatus, G. grandoculis, L. sceleratus, and L. macrorhinus performed poorly for MBES and SBES data with area under the receiver operator curves (AUC) below 0.7. Consequently, the distribution of these species could not be predicted by seafloor characteristics produced from either echo‐sounder type. Distribution models for P. multidens and P. typus performed well for MBES and the SBES data with an AUC above 0.8. Depth was the most important variable explaining the distribution of P. multidens and P. typus in both MBES and SBES models. While further research is needed, this study shows that in resource‐limited scenarios, SBES can produce comparable results to MBES for use in demersal fish management and conservation., This study assesses the performance of demersal fish species distribution models by comparing those derived from interpolated singlebeam (SBES) data with full‐coverage multibeam (MBES) distribution models. The results indicated that the performance of the SBES distribution models was not significantly different from that of the MBES models. While further research is needed, this study shows that in resource‐limited scenarios, SBES can produce comparable results to MBES for use in demersal fish management and conservation.

The mountain lion range once extended throughout the state of Minnesota. The breeding population has been greatly reduced with time, new roads, and timber harvesting, which have broken large tracts of contiguous forest into isolated patches that are too small and no longer suitable for the breeding mountain lion population. The objective of this study is to use suitability analysis to determine the most suitable habitat to conserve mountain lion populations threatened by habitat fragmentation. To attain our objective, we created three sub models that contribute to the overarching goal of the suitability model. A habitat sub model was developed for finding the best habitat, a food sub model for access to the maximum amount of food needed, and a security sub model focusing on the distance from houses, roads, and urban development. Using the Weighted Sum tool, the three sub models were combined to produce a suitability surface based on the trade-off of the preferences of the goals represented by each sub model. Our suitability model shows large areas of high-quality mountain lion habitat in the northern and north-eastern sections of the state. These areas contain favourable locations for mountain lion habitat, such as forested land cover, low-density populations, steep slopes, short distances to streams, and area unimpeded by major roads. The southern and western parts of the state are characterized by lower slopes, more agricultural land, grassland, developed land, and higher population density, which results in lower quality habitat. The twin cities have the worst mountain lion habitat.

The Kemp's ridley sea turtle (Lepidochelys kempii) is the most endangered sea turtle species in the world, largely due to the limited geographic range of its nesting habitat. There has been limited research regarding the connection between beach geomorphology and Kemp's ridley nesting patterns, but studies concerning other sea turtle species suggest that certain beach geomorphology variables, such as beach slope and width, influence nest site selection. This research attempts to address the literature gap by quantifying the terrestrial habitat variability of the Kemp's ridley and investigating the connection between beach geomorphology characteristics and Kemp's ridley nesting preferences on Padre Island, Texas, USA. Geomorphology characteristics, such as beach width and slope, were extracted from lidar-derived digital elevation models and associated with Kemp's nest coordinates and pseudo-absence points randomly created within the study area. Generalized linear models and random forest models were used to assess the significance of variables for nesting preferences. Kemp's ridley nest presence was successfully modeled using beach geomorphology characteristics, and elevation, distance from shoreline, maximum dune slope, and average beach slope were the relatively most important variables in the models. Kemp's ridleys exhibit a preference for a limited range of the study area and avoid nesting on beaches with beach characteristics of extreme values. The results of this study include new information regarding Kemp's ridley terrestrial habitat and nesting preferences that have many applications for species conservation and management.

Background Forest management affects the viability of forest grouse populations, causing alterations to habitat quality. At the regional level, common targets for wood harvesting and safeguarding of specific habitats are negotiated between various stakeholders. Analysing potential trade-offs between forest grouse habitats and wood production in the region could support resource-smart decision making. Methods In this paper, we compiled trade-off curves represented as production possibility frontiers demonstrating the relationship between forest grouse habitat area and wood removal, using a Finnish forestry dynamics model and the Finnish Multi-Source National Forest Inventory. For the modelling of forest grouse habitats, a landscape-level occurrence model based on nationwide wildlife triangle census data was used. Five alternative forest scenarios in terms of wood removal were compiled for two study areas in Finland representing two different landscape structures. Results and conclusions Results showed that impacts on forest grouse habitats were case-specific. In the southern study area, increasing the roundwood harvesting rate affected grouse habitats more strongly as forests were already fragmented for other land uses and became more spruce-dominated over time. If the maximum sustainable removal rate was implemented, predicted grouse habitat area was 55% less than in a no-removal scenario. In the eastern study area, a more heavily forested region, the decrease was far lower at 22%. Scenario results were also compared to levels of recorded (business as usual) wood removal and that envisaged by valid regional forestry programmes, and their sustainability in terms of grouse habitat area was discussed. The production possibility frontiers calculated in this study support the evaluation of the loss of suitable habitat caused by different wood harvesting rates, or vice versa, the economic cost of increasing habitat areas.

Understanding the factors that contribute to a population's habitat use is important for conservation planners and managers to identify reasons behind a population's distribution. Habitat use often differs between sexes, however few studies on sexually monomorphic species document this difference, resulting in misleading ecological interpretations and non-targeted management actions. The aim of this study was to test for sex-specific differences in the seasonal habitat use of Indo-Pacific bottlenose dolphins (Tursiops aduncus) off Bunbury, Australia. Systematic, boat-based, photographic identification dolphin surveys (n=587) were conducted across seasons over 6years during 2007-2013. Generalised additive models explored relationships between the presence-absence of dolphins and sex, water depth and benthic habitat type. Results highlighted that: (i) habitat use differed seasonally for males and females, (ii) depth had a strong influence on habitat use, which differed between sexes for summer, winter and spring, and (iii) there were no sex differences in habitat use in autumn, which coincides with the peak breeding season. In summer and autumn dolphins were concentrated in shallow, near-shore waters predominantly over reef and sand, and in winter and spring dolphins had a broader distribution over reef and mud/silt with the use of deeper, offshore waters. This pattern is consistent with the seasonally-dependent dolphin abundance that has been documented for this population. Identification of sex differences in habitat use provides management agencies with insights to implement informed actions for the conservation of this coastal dolphin population which is forecast to decline by 50% in the next two decades.

Germany is an EU member state and is thus obliged to monitor and protect its marine and coastal areas in the context of international treaties (e.g. EU Marine Strategy Framework Directive, EU Natura2000 directives, Trilateral Monitoring and Assessment Programme, OSPAR) to achieve the good environmental status (GES). Suitable indicators thus need to be developed to assess the status of relevant ecosystems. Intertidal bivalve beds belong to the most productive habitats in the shallow coastal waters of the Wadden Sea but are highly dynamic and difficult to monitor. Extensive benthos and sediment sampling are required to study their development and distribution. We therefore evaluated the suitability of herring gulls (Larus argentatus) as a bioindicator species for monitoring these habitats. As flexible and opportunistic top-predators, herring gulls prefer to forage on intertidal flats and feed on bivalves (Cerastoderma edule, Mytilus edulis, Ensis leei) and crustaceans, with intertidal habitats near the breeding colony preferred during the incubation period. We used state of the art tracking devices to analyse habitat selection by herring gulls, and assessed how this was driven by biotic and abiotic factors. We then designed a habitat model to predict the occurrence of intertidal bivalve beds for directed monitoring, based on GPS logger data from herring gulls and hydrodynamic data. Both methods showed high correlations of herring gull foraging patterns with bivalve biomass and inundation time, depending on the distance from the breeding colony. The movement patterns of herring gulls clearly mirrored intertidal bivalve distributions. These results showed that combining GPS-logger data for top-predators, such as birds, with environmental data has great potential for monitoring and assessing the GES. We propose that herring gulls may be used as an indicator to locate bivalve beds in the Wadden Sea and to detect changes in the food web.

International audience; 1. Projects that restore river flows can be considered as in situ experiments and should be used to test predictions of the effects of flow changes on fish populations and communities. However, flow restoration projects often lack appropriate monitoring and replication. The Rho^ne restoration project has included repeated flow changes, in four bypassed reaches of the river, where the increase in minimum daily flow varied from minimal change to a tenfold increase.2. Fish communities (>55 000 individuals of 36 species) were electrofished at nine sites in the main channels of the four bypassed reaches, for 2–9 years before and for 5–10 years after the flow restora- tion. An instream hydraulic habitat model, published before restoration and based on observations of fish microhabitat preferences in independent reaches, was applied to the bypassed reaches to predict density changes for 14 species that accounted for 94% of the total fish abundance.3. In the two bypassed reaches where minimum flow was considerably increased (fivefold and tenfold), the abundance of species preferring fast-flowing and deep microhabitats increased by factors of 1.9 and 2.4, respectively, whereas the abundance of other species strongly decreased. Predicted changes in density made using the habitat model for these two reaches agreed with the observations at several sites and involved several fish species. In contrast, in the two bypassed reaches where flow changes were less, the observed changes in density were weak and less related to the model predictions.4. Hydraulic habitat models predicted changes of fish populations and the predictions also explained observed community responses to the changed flows. Ten years after the first flow restoration, our results suggest that the Rho^ne restoration generated perennial changes of the fish community structure, reversing community patterns that were observed prior to the flow restoration.

Chust, G., Castellani, C., Licandro, P., Ibaibarriaga, L., Sagarminaga, Y., and Irigoien, X. 2014. Are Calanus spp. shifting poleward in the North Atlantic? A habitat modelling approach. – ICES Journal of Marine Science, 71: 241–253. In the last decade, the analysis based on Continuous Plankton Recorder survey in the eastern North Atlantic Ocean detected one of the most striking examples of marine poleward migration related to sea warming. The main objective of this study is to verify the poleward shift of zooplankton species (Calanus finmarchicus, C. glacialis, C. helgolandicus, C. hyperboreus) for which distributional changes have been recorded in the North Atlantic Ocean and to assess how much of this shift was triggered by sea warming, using Generalized Additive Models. To this end, the population gravity centre of observed data was compared with that of a series of simulation experiments: (i) a model using only climate factors (i.e. niche-based model) to simulate species habitat suitability, (ii) a model using only temporal and spatial terms to reconstruct the population distribution, and (iii) a model using both factors combined, using a subset of observations as independent dataset for validation. Our findings show that only C. finmarchicus had a consistent poleward shift, triggered by sea warming, estimated in 8.1 km per decade in the North Atlantic (16.5 per decade for the northeast), which is substantially lower than previous works at the assemblage level and restricted to the Northeast Atlantic. On the contrary, C. helgolandicus is expanding in all directions, although its northern distribution limit in the North Sea has shifted northward. Calanus glacialis and C. hyperboreus, which have the geographic centres of populations mainly in the NW Atlantic, showed a slight southward shift, probably responding to cool water penetrating southward in the Labrador Current. Our approach, supported by high model accuracy, shows its power in detecting species latitudinal shifts and identifying its causes, since the trend of occurrence observed data is influenced by the sampling frequency, which has progressively concentrated to lower latitudes with time.

The ixodid tick Dermacentor marginatus (Sulzer, 1776) is endemic throughout southern Europe in the range of 33-51 (°) N latitude. In Germany, however, D. marginatus was exclusively reported in the Rhine valley and adjacent areas. Its northern distribution limit near Giessen is located at the coordinates 8.32 (°) E/50.65 (°) N. Particularly with regard to the causative agents of rickettsioses, tularemia, and Q fever, the observed locations as well as the potential distribution of the vector D. marginatus in Germany are of special interest. Applying a dataset of 118 georeferenced tick locations, the ecological niche for D. marginatus was calculated. It is described by six climate parameters based on temperature and relative humidity and another six environmental parameters including land cover classes and altitude. The final ecological niche is determined by the frequency distributions of these 12 parameters at the tick locations. Main parameters are the mean annual temperature (frequency distribution characterized by the minimum, median, and maximum of 6.1, 9.9, and 12.2 (°)C), the mean annual relative humidity (73.7, 76.7, and 80.9 %), as well as the altitude (87, 240, 1108 m). The climate and environmental niche is used to estimate the habitat suitability of D. marginatus in Germany by applying the BIOCLIM model. Finally, the potential spatial distribution of D. marginatus was calculated and mapped by determining an optimal threshold value of the suitability index, i.e., the maximum of sensitivity and specificity (Youden index). The model performance is expressed by AUC = 0.91.

Small hydropower is a renewable energy technology that is used for electricity generation worldwide, but still has potential for further development. However, during the installation of small hydropower, the ecological impacts of the power plants need to be thoroughly investigated. In addressing the challenges of energy production and minimizing the environmental impacts of small hydropower installation and operation, this study has applied an ecohydraulic model to investigate river hydrodynamics, hydromorphology, habitat, and the population impacts of small hydropower, and presented the Mum River as a case study. Two scenarios were implemented in this research to simulate the hydrodynamic, sedimentation, habitat, and population status in order to assess the potential effects caused by the TUM plant. At the Mum River, two scenarios were proposed: the TUM plant was not considered in scenario S1, but was considered in scenario S2. The model results for scenario S2 indicated that the habitat was suitable for fish species living in the Mum River, with fish population numbers between 4.6 × 103 and 6.6 × 103. The S2 results indicated that the impacts of the TUM plant were negligible when compared with S1. Although the impact of the TUM plant on the Mum River is relatively large when the discharge is high (19 m3/s), calculations based on stable flow shows that the TUM plant could function well on the river ecosystem when the discharge is low or at normal rates. Therefore, this study shows that the TUM plant would be a good option to meet the needs of energy generation whilst having a minimal impact on river habitats and changes in fish species population in similar small rivers and streams.

Effective management of natural populations depends heavily on the capacity for understanding and predicting their habitat requirements. For endemic and threatened species like the mara (Dolichotis patagonum), this information is extremely necessary for populations survival. I investigated and constructed a mathematical habitat model for the mara, with the capacity to predict the probability of finding animals in a particular environment. In the Monte Desert, maras prefer habitats with low shrub density and high percentage of bare soil. The area used by each couple of maras was 7.73 ha and shows an aggregation pattern. Contrary to expectation, about 94% of these areas were in habitats created by human activities. Grazed, burned and crop areas seem to be prefered by maras when natural open habitat disappear. This information and the results of habitat requirements can be used to monitor the status of mara populations and predict potential population extinctions according to habitat variables. Fil: Rodriguez, Maria Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; Argentina. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales; Argentina

Reserve design is concerned with optimal selection of sites for new conservation areas. Spatial reserve design explicitly considers the spatial pattern of the proposed reserve network and the effects of that pattern on reserve cost and/or ability to maintain species there. The vast majority of reserve selection formulations have assumed a linear problem structure, which effectively means that the biological value of a potential reserve site does not depend on the pattern of selected cells. However, spatial population dynamics and autocorrelation cause the biological values of neighboring sites to be interdependent. Habitat degradation may have indirect negative effects on biodiversity in areas neighboring the degraded site as a result of, for example, negative edge effects or lower permeability for animal movement. In this study, I present a formulation and a spatial optimization algorithm for nonlinear reserve selection problems in grid-based landscapes that accounts for interdependent site values. The method is demonstrated using habitat maps and nonlinear habitat models for threatened birds in the Netherlands, and it is shown that near-optimal solutions are found for regions consisting of up to hundreds of thousands grid cells, a landscape size much larger than those commonly attempted even with linear reserve selection formulations.

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

Habitat selection has long been viewed as a multi-scale process. Observed species responses to resource gradients are influenced by variation at the scale of the individual, population, metapopulation, and geographic range. Understanding how species interact with habitat at multiple levels presents a complete picture of an organism and is necessary for conservation of endangered species. The main goal of this dissertation is to evaluate distribution, relative abundance, and habitat selection of a rare species, the pygmy rabbit Brachylagus idahoensis, at multiple scales in order to improve management and conservation for this species. At the broadest scale, pygmy rabbit occurrence and relative abundance were modeled in the Duck Creek allotment of northern Utah using a hierarchical spatial model. Pygmy rabbits are not easily observable, and the model used two levels of indirect detection to make statistically rigorous spatial predictions. We found that the model predicted the general pattern of rabbit occurrence and abundance within the study area, and that there was spatial heterogeneity in the probability of pygmy rabbit occurrence within a study domain that was known to be occupied. The resulting model framework could be used to develop a long-term monitoring program for pygmy rabbits and other species for which hierarchically nested levels of indirect observation are collected. The mid-scale analysis evaluated pygmy rabbit home range placement and movement with respect to sagebrush removal treatments using null models based on an optimal central place foraging behavior. While placement of home-range centers did not appear to be affected by the treatments, within-home range movements were farther from treatments than expected by the null models for two rabbits (of eight), and rabbits that approached treatment edges were less likely to enter treatments than expected by chance. Rabbits are not extirpated from sites that have been treated, but the observed reluctance to enter treated patches calls for caution when conducting sagebrush removal treatments near occupied pygmy rabbit burrows. At the finest level of resolution, the spatial ecology of pygmy rabbit use of burrows was evaluated. Both the placement of burrows in general and pygmy rabbit use of burrows were clustered. While the habitat gradients experienced by each of the rabbits evaluated affected the modeled habitat selection responses, some generalities were observed. Selection of high cover suggests that pygmy rabbit use of burrows may be linked to predator avoidance behavior. Additionally, pygmy rabbit use of clustered burrows affects management actions including: habitat modeling, monitoring, and species introduction. Explicit attention to resource distribution will improve efforts to predict species responses to management actions.

Habitat modeling can assist in managing potentially widespread but poorly known biological resources such as the federally endangered Delmarva fox squirrel (DFS; Sciurus niger cinereus). The ability to predict or identify suitable habitat is a necessary component of this species' recovery. Habitat identification is also an important consideration when evaluating impacts of land development on this species distribution, which is limited to the Delmarva Peninsula. The goal of this study was to build a predictive model of DFS occurrence that can be used towards the effective management of this species. I developed 5 a'priori global models to predict DFS occurrence based on literature review, past models, and professional experience. I used infrared photomonitors to document habitat use of Delmarva fox squirrels at 27 of 86 sites in the southern Maryland portion of the Delmarva Peninsula. All data were collected on the U.S. Fish and Wildlife Service Chesapeake Marshlands National Wildlife Refuge in Dorchester County, Maryland. Preliminary analyses of 27 DFS present (P) and 59 DFS absent (A) sites suggested that DFS use in my study area was significantly (Wilcoxon Mann-Whitney, P < 0.10) correlated with tree stems > 50 cm dbh/ha (Pmean = 16 + 3.8, Amean = 8+ 2.2), tree stems > 40 cm dbh/ha (Pmean = 49 + 8.1, Amean = 33 + 5.5), understory height (Pmean = 11 m + 0.8, Amean = 9 m + 0.5), overstory canopy height (Pmean = 31 m + 0.6, Amean = 28 m + 0.6), percent overstory cover (Pmean = 82 + 3.9, Amean = 73 + 3.1), shrub stems/ha (Pmean = 8068 + 3218, Amean = 11,119 + 2189), and distance from agricultural fields (Pmean = 964 m + 10, Amean = 1308 m + 103). Chi-square analysis indicated a correlation with shrub evenness (observed on 7% of DFS present sites and 21% of DFS absent sites). Using logistic regression and the Information Theoretic approach, I developed 7 model sets (5 a priori and 2 post hoc) to predict the probability of Delmarva fox squirrel habitat use as a function of micro- and macro-habitat characteristics. Of over 200 total model arrays tested, the model that fit the statistical, biological, and pragmatic criteria postulated was a post hoc integrated model: DFS use = percent overstory cover + shrub evenness + overstory canopy height. This model was determined to be the best of its subset (wi = 0.54), had a high percent concordance (>75%), a significant likelihood ratio (P = 0.0015), and the lowest AICc value (98.3) observed. Employing this predictive model of Delmarva fox squirrel occurrence can benefit recovery and consultation processes by facilitating systematic rangewide survey efforts and simplifying site screenings. Master of Science

Amphibians, because of their semi-permeable skin, sensitivity to changing microclimates, and important role in ecosystems, are often viewed as indicators of ecosystem health. They make excellent organisms for studies on the effects of silvicultural practices. My goal was to provide recommendations for forest management in the southern Appalachians so that harvesting operations are compatible with maintaining healthy populations of forest amphibians. I tested previously created habitat models that determined the most important habitat characteristics for salamanders. I counted salamanders in 240 10 x 10 m plots located in the MeadWestvaco Wildlife and Ecosystem Research Forest in north-central West Virginia. We also collected a variety of habitat data in these plots to predict salamander abundance with previously created models. These simple linear regression analyses of predicted versus observed values suggest for most models (7 out of 9) a weak relationship between predicted and observed values (R2 from 0.0033 to 0.2869, p from < 0.0001 to 0.7490). However, one of the models showed characteristics suggesting that it predicted new data as well or better than the original data, and therefore was the most accurate at predicting salamander abundance, and could be used for management purposes, although there was still much unexplained variation. This model included the variables woody stems (< 7.5 cm DBH), available rock, riparian status (i.e., within 15 m of a stream), percent overstory canopy cover, and available highly decomposed woody debris (decomposition classes 3 to 5). All of these relationships were positive except for woody stems, suggesting that in order to maintain healthy populations of salamanders, we should protect areas next to streams, with high amounts of rock, decomposed woody debris, overstory canopy cover, and few woody stems. I also examined the immediate effects of clearcuts on salamander movement and population estimates. I batch marked salamanders in plots at the edges of a clearcut, and in a control plot. Using the Schnabel estimator, I estimated population sizes in each plot. I then compared population estimates pre- and post-harvest on the interior (harvested) and exterior (unharvested) sides of the plots, taking into account the control plot. I also examined adult-juvenile ratios and movements from one side of the plot to the other. I found no significant changes (p > 0.05) following harvest in any of these measures, suggesting that salamanders do not move out of the harvested area post-harvest, at least over the short term (10 months of this study). This suggests that a longer period of time (> 1 year) is required to observe the population declines detected in most studies. Master of Science