Dissertations / Theses on the topic 'Habitat predictive model'

The rainbow parrotfish Scarus guacamaia is a prominent herbivore in the coastal waters of southeastern Florida whose life history is strongly linked to a dependence on both mangrove and coral reef habitats. Rainbow parrotfish in turn serve in maintaining the health of coral reefs by keeping algal populations in check. This study used NOAA Fisheries data from the Mangrove Visual Census and the Reef Visual Census in Biscayne Bay and Upper Florida Bay. Observations of abiotic factors at individual sites were used to correlate and predict presence and absence of this species. This was done to visualize habitat presence and ontogenetic shifts present in this species between juvenile and adult stages through ArcGIS mapping. Logistic regression analysis was used to predict presence or absence using the environmental variables of temperature, dissolved oxygen, salinity, average depth, distance from channel openings, mangrove presence, temperature Δ, and salinity Δ. Average depth, distance from channel openings, temperature Δ and salinity Δ were significant in predicting the presence of this species, while salinity, temperature, dissolved oxygen, and mangrove presence were not. Conservation efforts for this species, listed as vulnerable under the IUCN, need to be given greater consideration. The health of this and other parrotfish may have a greater impact on coral reef ecosystems across the Caribbean Sea than currently acknowledged and management breadth and priorities should be adjusted to reflect this role.

It is widely known that the world is losing biodiversity and primarily it is thought to be caused by anthropogenic activities. Many of these activities have been identified. However, we still lack a clear understanding of the causal relationships between human activities and the pressures they place on the environment and biodiversity. We need to know how ecosystems and individual species respond to changes in human activities and therefore how best to moderate our actions and reduce the rate of loss of biodiversity. One of the ways to detect these changes is to use indicators of ecosystem conditions. Indicators are statistics following changes in a particular factor usually over time. These indicators are used to summarise a complex set of data, and are seen as being representative of the wider situation in that field. So it can be assumed that if that particular factor is declining or improving, then the situation in general is also declining or improving. They are used to check the status and trends of biodiversity by both the public and policy makers. Indicators are also used to assess national performance and can be used to identify the actions required at the policy level. In this manner, they provide an important link between policy-makers and scientists collecting the data. The current thesis investigates the possibility of using bird species as indicators of biodiversity for better management of natural terrestrial ecosystems, by identifying their habitats according to various environmental factors. The study is established by drawing upon three main scientific areas: ecology, geographical information system (GIS), and statistical modelling. The Mornington Peninsula and Western Port Biosphere Reserve (MPWPBR) (Victoria, Australia) was chosen for the study area because of the combination of suburban and natural environments that made it optimum for this type of study. Once the study area was defined, the necessary data for the research were obtained from various sources. Birds Australia provided data on recorded observation of 271 bird species within the study area. Based on the nature of this study, seven species were selected for the study. The criteria for this selection are discussed in Chapter 3. Most literature state that the primary determinant for bird abundance is vegetation and land cover. Because of this, Ecological Vegetation Class (EVC) layer was used to determine which type(s) of vegetation have the greatest impact on habitat selection. Each species showed a relationship to a number of v vegetation types. These EVCs were combined to produce vegetation patches, and were considered as potentially suitable habitats of corresponding bird species. For each of the species, these habitat patches were analysed for the different aspects of patch characteristics (such as the level of patchiness, connectivity, size, shape, weighted distance between patches, etc.) by using the Landscape Context Tool (a GIS add-on). This process assisted the understanding of the importance of patch quality in habitat selection among different bird species by analysing the location of bird observation sites relative to habitat patches. In this way, the association between bird presence and the conditions of a habitat patch was identified by performing a discriminant function analysis. To investigate the probability of a species presence according to different environmental factors, a model of species distribution was created. Binary logistic regression was used to indicate the level of effect of each variable. The model was then successfully validated in the field. To define the indicators of environmental factors, it was essential to separate bird species based on their dependency on one or more of the studied variables. For this purpose, One-Way ANOVA was used. This analysis showed that some bird species can be considered as indicators of urban areas, while others could be good indicators of wellpreserved large forests. Finally, it must be mentioned that the type and quality of the datasets are crucial to this type of study, because some species have a higher degree of sensitivity to certain types of vegetation or land cover. Therefore, the vegetation data must be produced as detailed as possible. At the same time, the species data needs to be collected based on the presence and absence (versus presence-only) of the birds.

The main purpose of this study was to produce and test the reliability of predictive models of cetacean distributions off the west coast of Scotland. Passive acoustic and visual surveys were carried out from platforms of opportunity between 2003 and 2005. Acoustic identifications were made primarily of harbour porpoises (Phocoena phocoena), delphinids, and sperm whales (Physeter macrocephalus). Generalised Additive Models (GAMs) were used to relate species’ distributions to a range of environmental variables over a range of temporal and spatial scales. Predictive models of delphinid distributions showed both inter-annual and inter-month variations. Combining all data for all months and years resulted in a model that combined the environmental influences from each monthly and yearly model. Overall, delphinids were found to associate with the deep (> 400m) warm water (10.5°C-12.5°C), and in areas of deep thermocline. Relationships between sperm whales and environmental variables were consistent over changes in grain size (9 km or 18 km), but not between areas. Although sperm whales were distributed in deep water characterised by weak thermoclines and strong haloclines in the most northerly area (Faroe-Shetland Channel), they were found in deep productive areas with cold surface temperature in the more southerly waters (Rockall Trough). Within the southern Inner Hebrides, high use areas for harbour porpoises were consistently predicted over time (in years) and with differing survey techniques (acoustic versus visual), but not over space (southern Inner Hebrides versus whole of the Inner Hebrides). Harbour porpoises were mainly distributed in areas with low tidal currents and with higher detection rates during spring tides. The use of prey as a predictor variable within models of delphinid distribution shows some promise: there were correlations between delphinid and herring (Clupea harengus) in shelf-waters in 2005 but not in 2004. These models can be used in mitigating acoustic threats to cetaceans in predicted high use areas off the west coast of Scotland.

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.<br>Master of Science

In dieser Arbeit wurden verschiedene GIS-basierte Habitatmodelle für den Weißstorch (Ciconia ciconia) im Gebiet der ehemaligen deutschen Provinz Ostpreußen (ca. Gebiet der russischen Exklave Kaliningrad und der polnischen Woiwodschaft Ermland-Masuren) erstellt. Zur Charakterisierung der Beziehung zwischen dem Weißstorch und der Beschaffenheit seiner Umwelt wurden verschiedene historische Datensätze über den Bestand des Weißstorches in den 1930er Jahren sowie ausgewählte Variablen zur Habitat-Beschreibung genutzt. Die Aufbereitung und Modellierung der verwendeten Datensätze erfolgte mit Hilfe eines geographischen Informationssystems (ArcGIS) und einer statistisch-mathematischen Methode aus den Bereichen „Machine Learning“ und „Data-Mining“ (TreeNet, Salford Systems Ltd.). Unter Verwendung der historischen Habitat-Parameter sowie der Daten zum Vorkommen des Weißstorches wurden quantitative Modelle auf zwei Maßstabs-Ebenen erstellt: (i) auf Punktskala unter Verwendung eines Rasters mit einer Zellgröße von 1 km und (ii) auf Verwaltungs-Kreisebene basierend auf der Gliederung der Provinz Ostpreußen in ihre Landkreise. Die Auswertung der erstellten Modelle zeigt, dass das Vorkommen von Storchennestern im ehemaligen Ostpreußen, unter Berücksichtigung der hier verwendeten Variablen, maßgeblich durch die Variablen ‚forest’, ‚settlement area’, ‚pasture land’ und ‚coastline’ bestimmt wird. Folglich lässt sich davon ausgehen, dass eine gute Nahrungsverfügbarkeit, wie der Weißstorch sie auf Wiesen und Weiden findet, sowie die Nähe zu menschlichen Siedlungen ausschlaggebend für die Nistplatzwahl des Weißstorches in Ostpreußen sind. Geschlossene Waldgebiete zeigen sich in den Modellen als Standorte für Horste des Weißstorches ungeeignet. Der starke Einfluss der Variable ‚coastline’ lässt sich höchstwahrscheinlich durch die starke naturräumliche Gliederung Ostpreußens parallel zur Küstenlinie erklären. In einem zweiten Schritt konnte unter Verwendung der in dieser Arbeit erstellten Modelle auf beiden Skalen Vorhersagen für den Zeitraum 1981-1993 getroffen werden. Dabei wurde auf dem Punktmaßstab eine Abnahme an potentiellem Bruthabitat vorhergesagt. Im Gegensatz dazu steigt die vorhergesagte Weißstorchdichte unter Verwendung des Modells auf Verwaltungs-Kreisebene. Der Unterschied zwischen beiden Vorhersagen beruht vermutlich auf der Verwendung unterschiedlicher Skalen und von zum Teil voneinander verschiedenen erklärenden Variablen. Weiterführende Untersuchungen sind notwendig, um diesen Sachverhalt zu klären. Des Weiteren konnten die Modellvorhersagen für den Zeitraum 1981-1993 mit den vorliegenden Bestandserfassungen aus dieser Zeit deskriptiv verglichen werden. Es zeigt sich hierbei, dass die hier vorhergesagten Bestandszahlen höher sind als die in den Zählungen ermittelten. Die hier erstellten Modelle beschreiben somit vielmehr die Kapazität des Habitats. Andere Faktoren, die die Größe der Weißstorch-Population bestimmen, wie z.B. Bruterfolg oder Mortalität sollten in zukünftige Untersuchungen mit einbezogen werden. Es wurde ein möglicher Ansatz aufgezeigt, wie man mit den hier vorgestellten Methoden und unter Verwendung historischer Daten wertvolle Habitatmodelle erstellen sowie die Auswirkung von Landnutzungsänderungen auf den Weißstorch beurteilen kann. Die hier erstellten Modelle sind als erste Grundlage zu sehen und lassen sich mit Hilfe weitere Daten hinsichtlich Habitatstruktur und mit exakteren räumlich expliziten Angaben zu Neststandorten des Weißstorches weiter verfeinern. In einem weiteren Schritt sollte außerdem ein Habitatmodell für die heutige Zeit erstellt werden. Dadurch wäre ein besserer Vergleich möglich hinsichtlich erdenklicher Auswirkungen von Änderungen der Landnutzung und relevanten Umweltbedingungen auf den Weißstorch im Gebiet des ehemaligen Ostpreußens sowie in seinem gesamten Verbreitungsgebiet.<br>Different habitat models were created for the White Stork (Ciconia ciconia) in the region of the former German province of East Prussia (equals app. the current Russian oblast Kaliningrad and the Polish voivodship Warmia-Masuria). Different historical data sets describing the occurrence of the White Stork in the 1930s, as well as selected variables for the description of landscape and habitat, were employed. The processing and modeling of the applied data sets was done with a geographical information system (ArcGIS) and a statistical modeling approach that comes from the disciplines of machine-learning and data mining (TreeNet by Salford Systems Ltd.). Applying historical habitat descriptors, as well as data on the occurrence of the White Stork, models on two different scales were created: (i) a point scale model applying a raster with a cell size of 1 km2 and (ii) an administrative district scale model based on the organization of the former province of East Prussia. The evaluation of the created models show that the occurrence of White Stork nesting grounds in the former East Prussia for most parts is defined by the variables ‘forest’, ‘settlement area’, ‘pasture land’ and ‘proximity to coastline’. From this set of variables it can be assumed that a good food supply and nesting opportunities are provided to the White Stork in pasture and meadows as well as in the proximity to human settlements. These could be seen as crucial factors for the choice of nesting White Stork in East Prussia. Dense forest areas appear to be unsuited as nesting grounds of White Storks. The high influence of the variable ‘coastline’ is most likely explained by the specific landscape composition of East Prussia parallel to the coastline and is to be seen as a proximal factor for explaining the distribution of breeding White Storks. In a second step, predictions for the period of 1981 to 1993 could be made applying both scales of the models created in this study. In doing so, a decline of potential nesting habitat was predicted on the point scale. In contrast, the predicted White Stork occurrence increases when applying the model of the administrative district scale. The difference between both predictions is to be seen in the application of different scales (density versus suitability as breeding ground) and partly dissimilar explanatory variables. More studies are needed to investigate this phenomenon. The model predictions for the period 1981 to 1993 could be compared to the available inventories of that period. It shows that the figures predicted here were higher than the figures established by the census. This means that the models created here show rather a capacity of the habitat (potential niche). Other factors affecting the population size e.g. breeding success or mortality have to be investigated further. A feasible approach on how to generate possible habitat models was shown employing the methods presented here and applying historical data as well as assessing the effects of changes in land use on the White Stork. The models present the first of their kind, and could be improved by means of further data regarding the structure of the habitat and more exact spatially explicit information on the location of the nesting sites of the White Stork. In a further step, a habitat model of the present times should be created. This would allow for a more precise comparison regarding the findings from the changes of land use and relevant conditions of the environment on the White Stork in the region of former East Prussia, e.g. in the light of coming landscape changes brought by the European Union (EU).

This study focuses on the habitats of cetaceans in the Mid-Atlantic Bight, a region characterized by bathymetric diversity and the presence of distinct water masses (i.e. the shelf water, slope water, and Gulf Stream). The combination of these features contributes to the hydrographic complexity of the area, which furthermore influences biological productivity and potential prey available for cetaceans. The collection of cetacean sighting data together with physical oceanographic data can be used to examine cetacean habitat associations. Cetacean habitat modeling is a mechanism for predicting cetacean distribution patterns based on environmental variables such as bathymetric and physical properties, and for exploring the potential ecological implications that contribute to cetacean spatial distributions. We can advance conservation efforts of cetacean populations by expanding our knowledge of their habitats and distribution. Generalized additive models (GAMs) were developed to predict the spatial distribution patterns of sperm whales (Physeter macrocephalus), pilot whales (Globicephala spp.), bottlenose dolphins (Tursiops truncatus), and Atlantic spotted dolphins (Stenella frontalis) based on significant physical parameters along the continental shelf-break region in the Mid-Atlantic Bight. Data implemented in the GAMs were collected in the summer of 2006 aboard the NOAA R/V Gordon Gunter. These included visual cetacean survey data collected along with physical data at depth via expendable bathythermograph (XBT), and conductivity-temperature-depth (CTD) instrumentation. Additionally, continual surface data were collected via the ship’s flow through sensor system. Interpolations of physical data were created from collected point data using the inverse distant weighted method (IDW) to estimate the spatial distribution of physical data within the area of interest. Interpolated physical data, as well as bathymetric (bottom depth and slope) data were extracted to overlaid cetacean sightings, so that each sighting had an associated value for nine potentially significant physical habitat parameters. A grid containing 5x5 km grid cells was created over the study area and cetacean sightings along with the values for each associated habitat parameter were summarized in each grid cell. Redundant parameters were reduced, resulting in a full model containing temperature at 50 m depth, mixed layer depth, bottom depth, slope, surface temperature, and surface salinity. GAMs were fit for each species based on these six potentially significant parameters. The resultant fit models for each species predicted the number of individuals per km2 based on a unique combination of environmental parameters. Spatial prediction grids were created based on the significant habitat parameters for each species to illustrate the GAM outputs and to indicate predicted regions of high density. Predictions were consistent with observed sightings. Sperm whale distribution was predicted by a combination of depth, sea surface temperature, and sea surface salinity. The model for pilot whales included bottom slope, and temperature at 50 m depth. It also indicated that mixed layer depth, bottom depth and surface salinity contributed to group size. Similarly, temperature at 50 m depth was significant for Atlantic spotted dolphins. Predicted bottlenose dolphin distribution was determined by a combination of bottom slope, surface salinity, and temperature at 50 m depth, with mixed layer depth contributing to group size. Distribution is most likely a sign of prey availability and ecological implications can be drawn from the habitat parameters associated with each species. For example, regions of high slope can indicate zones of upwelling, enhanced vertical mixing and prey availability throughout the water column. Furthermore, surface temperature and salinity can be indicative of patchy zones of productivity where potential prey aggregations occur. The benefits of these models is that collected point data can be used to expand our knowledge of potential cetacean “hotspots” based on associations with physical parameters. Data collection for abundance estimates, higher resolution studies, and future habitat surveys can be adjusted based on these model predictions. Furthermore, predictive habitat models can be used to establish Marine Protected Areas with boundaries that adapt to dynamic oceanographic features reflecting potential cetacean mobility. This can be valuable for the advancement of cetacean conservation efforts and to limit potential vessel and fisheries interactions with cetaceans, which may pose a threat to the sustainability of cetacean populations.

The SNTEMP (U.S. Fish and Wildlife Service), QUAL2E (U.S. Environmental Protection Agency), and RQUAL (Tennessee Valley Authority) stream temperature prediction models were evaluated. All models had high predictive ability with the majority of predictions, >80% for Back Creek (Roanoke County, VA) and >90% for the Smith River tailwater (SRT) (Patrick County, VA), within 3°C of the measured water temperature. Sensitivity of model input parameters was found to differ between model, stream system, and season. The most sensitive of assessed parameters, dependent on model and stream, were lateral inflow, starting-water, air, and wet-bulb temperature. All three models predicted well, therefore, selecting a model to assess alternative water management scenarios was based on model capabilities. The RQUAL model, used to predict SRT temperatures under alternative hydropower release regimes, illustrated potential thermal habitat improvement for brown trout (Salmo trutta) compared to existing conditions. A 7-day/week morning 1 hr release was determined to best concurrently increase occurrence of brown trout optimal growth temperatures (+10.2% mean), decrease 21°C (state standard) exceedances (99% prevention), and decrease hourly changes in temperature (-1.6°C mean) compared to existing thermal conditions. The SNTEMP model was used to assess thermal habitat under flow, shade, and channel width changes occurring from future urbanization within the Back Creek watershed. Predictions reveal that additional urban development could limit thermal habitat for present fish species by elevating summer mean daily temperature up to 1°C and cause 31°C (state standard) exceedances compared to existing conditions. Temperature impacts were lessened by single rather than cumulative changes suggesting mitigation measures may maintain suitable thermal habitat.<br>Master of Science

Usnea hirta and Xanthoparmelia cumberlandia are commonly used as bio-monitors of air quality. In order to more accurately and efficiently determine the distribution of these two sensitive indicator species, we have developed a probabilistic distribution map as a function of 9 macroclimatic and topographic variables for the White River National Forest, Colorado using Non-Parametric Multiplicative Regression (NPMR) analysis. Furthermore, we also developed a logistic regression (LR) model for X. cumberlandia in order to evaluate the strengths and limitations of the NPMR model. The best model for U. hirta included four variables - solar radiation, average monthly precipitation, average monthly minimum and maximum temperature (log β = 3.68). The presence rate for U. hirta based on field validated test sites was 45.5%, 65.4%, and 70.4% for low, medium, and high probability areas, respectively. The best model for X. cumberlandia generated by both NPMR and LR involved the same variables - solar radiation, average monthly maximum temperature, average monthly precipitation, and elevation as the best predictor variables (log β = 5.10). The occurrence rate for X. cumberlandia using the NPMR model was 32%, 44.4%, and 20% for the low, medium, and high probability areas respectively while the LR model had 26%, 50%, and 38% for low, medium and high probability areas respectively. Although the LR model predicted a smaller high probability area compared to the NPMR model there was substantial overlap between the two. The U. hirta model performed better than the X. cumberlandia model. The reduced performance of our model especially for X. cumberlandia may be due in part to the absence of field measured data in the development of the model. Our study also suggested that the northeast and western part of the forest should be preferentially considered for establishing future air quality bio-monitoring reference sites. Finally, in the future a well defined sampling design with sufficient sampling sites, field measured predictor variables, and microclimatic data should be used in the development of predictive models.

[Truncated abstract] Spatially explicit marine habitat data is required for effective resource planning and management across large areas, although mapped boundaries typically lack rigour in explaining what factors influence habitat distributions. Accurate, quantitative methods are needed. In this thesis I aimed to assess the utility of ecological models to determine what factors limit the spatial extent of marine habitats. I assessed what types of modeling methods were able to produce the most accurate predictions and what influenced model results. To achieve this, initially a broad scale marine habitat survey was undertaken in the Recherche Archipelago, on the south coast of Western Australia using video and sidescan sonar. Broad and more detailed functional habitats types were mapped for 1054km2 of the Archipelago. Broad habitats included high and low profile reefs, sand, seagrass and extensive rhodolith beds, although considerable variation could be identified from video within these broad types. Different densities of seagrass were identified and reefs were dominated by macroalgae, filter feeder communities, or a combination of both. Geophysical characteristics (depth, substrate, relief) and dominant benthic biota were recorded and then modelled using decision trees and a combination of generalised additive models (GAMs) and generalised linear models (GLMs) to determine the factors influencing broad and functional habitat variation. Models were developed for the entire Archipelago (n=2769) and a subset of data in Esperance Bay (n=797), which included exposure to wave conditions (mean maximum wave height and mean maximum shear stress) calculated from oceanographic models. Additional distance variables from the mainland and islands were also derived and used as model inputs for both datasets. Model performance varied across habitats, with no one method better than the other in terms of overall model accuracy for each habitat type, although prevalent classes (>20%) such as high profile reefs with macroalgae and dense seagrass were the most reliable (Area Under the Curve >0.7). ... This highlighted not only issues of data prevalence, but also how ecological models can be used to test the reliability of classification schemes. Care should be taken when mapping predicted habitat occurrence with broad habitat models. It should not be assumed that all habitats within the type will be defined spatially, as this may result in the distribution of distinctive and unique habitats such as filterfeeders being underestimated or not identified at all. More data is needed to improve prediction of these habitats. Despite the limitations identified, the results provide direction for future field sampling to ensure appropriate variables are sampled and classification schemes are carefully designed to improve descriptions of habitat distributions. Reliable habitat models that make ecological sense will assist future assessments of biodiversity within habitats as well as provide improved data on the probability of habitat occurrence. This data and the methods developed will be a valuable resource for reserve selection models that prioritise sites for management and planning of marine protected areas.

<p>White croaker (<i>Genyonemus lineatus</i>) are a sentinel fish species for contamination due to their direct interaction with contaminated sediments through benthic foraging. White croaker within the Los Angeles and Long Beach Harbor exhibited hierarchical habitat selection: avoiding dredged areas while selecting for areas of high sediment total organic carbon (4.8&ndash;8.1%), high polychaete density (406&ndash;700 polychaetes/0.1 m²), and small sediment grain size (&lt;23.5 &micro;m). Model results suggest that these fish are moving into shallower waters at night to forage and may refuge more during the day to avoid predation. The predictive model for white croaker habitat use indicated three important areas of use within the LA-LB Harbor: Consolidated Slip, Inner LB Harbor, and Fish Harbor. The areas containing the most preferable habitat to white croaker are also areas of high sediment contamination, and thus are the likely locations where these fish are acquiring contaminants. </p>

Predicting species occurrence based upon landscape-scale characteristics is a fundamental goal of ecology and conservation biology. Accurately predicting the potential occurrence of a species is fundamental to management activities that involve large areas where sampling is difficult due to logistical or financial constraints. During the summers of 2001-2003 mist nets were used to capture bats in Whiskeytown National Recreation Area (WNRA), Lassen Volcanic National Park (LVNP), and Lassen National Forest (LNF) in northern California. I used logistic regression and Akaike's Information Criterion (AIQ to model species distributions. Models developed a priori were used to determine which variables best discriminated between capture sites and non-capture sites. Prediction models were mapped using Geographic Information Systems. In WNRA, for all bat species combined total edge was most parsimonious, whereas in LVNP elevation was best for all species. Elevation and tree size were important in predicting the occurrence of pallid bats (Antrozous pallidus), in LNF. Results of this study are important to wildlife managers within the study areas because the models can be used to minimize deleterious impacts on bats. Moreover, distribution maps are valuable to bat conservation efforts because they provide baseline data important for evaluating and predicting population responses to management activities.<br>Department of Biology

West Nile virus has become a major risk to humans since its first appearance in New York City in 1999. Physicians and state health officials are interested in new and more efficient methods for monitoring disease spread and predicting future outbreaks. This study modeled habitat suitability for mosquitoes that carry West Nile virus. Habitat characteristics were used to derive risk maps for the entire state of Mississippi. Statistical significance tests yielded objective evidence for choosing among many habitat variables. Variables that were significantly correlated with diagnosed human cases for 2002 were combined in weighted linear algebraic models using a geographic information system (GIS). Road density, slope, and summer precipitation minus evaporation (P-E) were the most significant variables. GIS-based model results were compared with results from logistic regression models. The algebraic model was preferred when validated by 2003 human cases. If adopted, GIS-based risk models can help guide mosquito control efforts.

O esgana-gata (G. aculeatus L.) é um peixe de água doce, classificado como Em Perigo (EN) pelo Livro Vermelho dos Vertebrados de Portugal. Contudo, devido à falta de conhecimentos relativamente à sua distribuição e preferências de habitat, poucas foram as medidas propostas com o objetivo de proteger as suas populações. Este estudo permitiu modelar a potencial distribuição da espécie, através de um método de previsão por ensemble. Verificou-se que o esgana-gata tende a ocorrer em habitats aquáticos onde o substrato arenoso é dominante e os níveis de escoamento e de precipitação durante o mês mais seco são mais elevados, evitando áreas de declive acentuado e com elevados níveis de precipitação média anual. Baseado nos resultados obtidos, foi elaborado um mapa de probabilidades de ocorrência da espécie, através do qual foram categorizados diferentes troços de rios, de acordo com os seus diferentes níveis de prioridade de conservação para a espécie; Abstract: Distribution and habitat preferences of stickleback (Gasterosteus aculeatus L.) in Portugal: implications for their management and conservation. The threespine stickleback (G. aculeatus L.) is a small freshwater fish that has been listed as Endangered (EN) in Portugal on the national Red List of Threatened Vertebrates. However, due to the lack of knowledge about its distribution and habitat preferences, few measures have been proposed aiming at the conservation of populations of this species. This study allowed to model the potential distribution of the species, using an ensemble forecasting method. It was found that stickleback tends to occur in aquatic habitats where the sandy substrate is dominant and the levels of flow and precipitation during the driest month are higher, avoiding areas of steep slope and with high levels of average annual precipitation. Based on our results, a map of the species probability of occurrence was generated, and based on this, some river sections were categorized, according to distinct priority levels for the species conservation.

This thesis presents a new template for multidisciplinary habitat mapping that combines the analyses of seafloor geomorphology, oceanographic proxies and modelling of associated biologic features. High resolution swath bathymetry of the Faial and western Pico shelves is used to present the first state-of-the-art geomorphologic assessment of submerged island shelves in the Azores. Solid seafloor structures are described in previously unreported detail together with associated volcanic, tectonic and erosion processes. The large sedimentary expanses identified in the area are also investigated and the large bedforms identified are discussed in view of new data on the local hydrodynamic conditions. Coarse-sediment zones of types hitherto unreported for volcanic island shelves are described using swath data and in situ imagery together with sub-bottom profiles and grainsize information. The hydrodynamic and geological processes producing these features are discussed. New oceanographic information extracted from satellite imagery is presented including yearly and seasonal sea surface temperature and chlorophyll-a concentration fields. These are used as proxies to understand the spatio-temporal variability of water temperature and primary productivity in the immediate island vicinity. The patterns observed are discussed, including onshore-offshore gradients and the prevalence of colder/more productive waters in the Faial-Pico passage and shelf areas in general. Furthermore, oceanographic proxies for swell exposure and tidal currents are derived from GIS analyses and shallow-water hydrographic modelling. Finally, environmental variables that potentially regulate the distribution of benthic organisms (seafloor nature, depth, slope, sea surface temperature, chlorophyll-a concentration, swell exposure and maximum tidal currents) are brought together and used to develop innovative statistical models of the distribution of six macroalgae taxa dominant in the infralittoral (articulated Corallinaceae, Codium elisabethae, Dictyota spp., Halopteris filicina, Padina pavonica and Zonaria tournefortii). Predictive distributions of these macroalgae are spatialized around Faial island using ordered logistic regression equations and raster fields of the explanatory variables found to be statistically significant. This new approach represents a potentially highly significant step forward in modelling benthic communities not only in the Azores but also in other oceanic island shelves where the management of benthic species and biotopes is critical to preserve ecosystem health.

Cette thèse propose des solutions de commande basées sur la planification optimale de trajectoires pour des systèmes dynamiques multi-agents fonctionnant dans un environnement variable (avec obstacles statiques ou mobiles et des perturbations variables dans le temps).Cette planification de trajectoires repose sur l'utilisation combinée de la théorie des ensembles (en particulier des ensembles convexes bornés), de la commande prédictive non-linéaire (NMPC), du calcul de champs de potentiel et des méthodes basées sur des graphes. Elle se base sur la construction de champs de potentiel répulsifs associés à des fonctions de barrière marche-arrêt (on-off barrier functions) qui décrivent et activent ou désactivent les trajectoires libres (sans collision) calculées au préalable par une commande de type NMPC distribuée. Ces constructions sont ensuite utilisées pour maintenir la connectivité dans le groupe d'agents, tout en assurant le suivi du chemin pré-généré. En outre, un observateur pour l'estimation de perturbations non linéaires est intégré dans le schéma de commande afin de les rejeter.Les résultats théoriques obtenus sont validés en simulation, par des comparaisons avec des approches utilisant la programmation mixte en nombres entiers, à l'aide de données numériques réelles provenant d'une plateforme de navigation sécurisée pour les véhicules de surface non habités dans le fjord de Trondheim (Norvège)<br>This thesis proposes optimization-based control solutions for the motion planning of multi-agent dynamical systems operating in a variable environment (with static/mobile obstacles and time-varying environmental disturbances).Collision-free paths are planned for the agents through the combined use of set theory (particularly, bounded convex sets), non(-linear) Model Predictive Control (MPC), Potential Field (PF) and graph-based methods. The contributions build on the proposal of repulsive potential field constructions together with on-off barrier functions which describe and, respectively, activate/deactivate the collision-free conditions introduced in a distributed NMPC framework. These constructions are further used for connectivity maintenance conditions among the group of agents while ensuring the tracking of the a priori generated path. Furthermore, a nonlinear disturbance observer is integrated within the control scheme for environmental disturbance rejection.Finally, the results are validated in simulation through comparisons with mixed-integer approaches and over a benchmark for the safe navigation of Unmanned Surface Vehicles (USVs) in the Trondheim fjord, Norway, using real numerical data

Invasive alien species (IAS) pose a serious threat to ecosystems and societies worldwide. Local ecological knowledge (LEK) is increasingly valued as a means of understanding environmental issues; however, its application in the context of IAS research has been limited. The overall objective of this study was to document the LEK of farmers and Weed Supervisors to gain insight into a recent IAS, Odontites serotina. I conducted semi-structured interviews with farmers and Weed Supervisors with O. serotina management experience. Results indicated that the socio-economic impacts for farmers were severe in affected rural communities. However, participants had developed promising control techniques, including the application of compost mulch. I used this LEK as well as data on species occurrence, environmental variables, and measures of propagule pressure to forecast the potential distribution of O. serotina across Manitoba. The risk map generated will be useful for guiding future monitoring and public outreach efforts.

Dissertação de Mestrado, Estudos Integrados dos Oceanos, 25 de Março de 2013, Universidade dos Açores.<br>Species distribution modelling of the marine environment has been extensively used to assess species–environment relationships to predict fish spatial distributions accurately. In this study we explored the application of two distinct modelling techniques, maximum entropy model (MaxEnt) and generalized linear models (GLMs) for predicting the potential distribution in the Azores economic exclusive zone (EEZ) of four economically important demersal fish species: blackbelly rosefish, Helicolenus dactylopterus dactylopterus, forkbeard, Phycis phycis, wreckfish, Polyprion americanus and offshore rockfish, Pontinus kuhlii. Models were constructed based on 13 years of fish presence/absence data derived from bottom longline surveys performed in the study area combined with high resolution (300 m) topographic and biogeochemical habitat seafloor variables. The most important predictors were depth and slope followed by sediment type, oxygen saturation and salinity, with relative contributions being similar among species. GLMs provided ‘outstanding’ model predictions (AUC>0.9) for two of the four fish species while MaxEnt provided ‘excellent’ model predictions (AUC=0.8–0.9) for three of four species. The level of agreement between observed and predicted presence/absence sites for both modelling techniques was ‘moderate’ (K=0.4–0.6) for three of the four species with P. americanus models presenting the lowest level of agreement (K<0.1). For the scope of this study, both modelling approaches presented here were determined to produce viable presence/absence maps which represent a snap–shot of the potential distributions of the investigated species. This information provides a better description of demersal fish spatial ecology and can be of a great deal of interest for future fisheries management and conservation planning.

For the west coast of North America, from northern California to southern Washington, a habitat suitability prediction framework was developed to support wave energy device siting. Concern that wave energy devices may impact the seafloor and benthos has renewed research interest in the distribution of marine benthic invertebrates and factors influencing their distribution. A Bayesian belief network approach was employed for learning species-habitat associations for Rhabdus rectius, a tusk-shaped marine infaunal Mollusk. Environmental variables describing surficial geology and water depth were found to be most influential to the distribution of R. rectius. Water property variables, such as temperature and salinity, were less influential as distribution predictors. Species-habitat associations were used to predict habitat suitability probabilities for R. rectius, which were then mapped over an area of interest along the south-central Oregon coast. Habitat suitability prediction models tested well against data withheld for crossvalidation supporting our conclusion that Bayesian learning extracts useful information available in very small, incomplete data sets and identifies which variables drive habitat suitability for R. rectius. Additionally, Bayesian belief networks are easily updated with new information, quantitative or qualitative, which provides a flexible mechanism for multiple scenario analyses. The prediction framework presented here is a practical tool informing marine spatial planning assessment through visualization of habitat suitability.<br>Graduation date: 2012

Thesis (M.S.)--University of Georgia, 2007.<br>Directed by Michael J. Conroy. Includes articles submitted to Journal of wildlife and The journal of wildlife management. Includes bibliographical references.