Praticamente todos os ecossistemas naturais têm sido afetados pelas atividades humanas. A construção de usinas hidrelétricas produz grandes impactos ambientais causando problemas notáveis como a fragmentação e perdas de habitats. No Brasil aproximadamente 70% da matriz energética é formada por hidrelétricas e há previsão de expansão nos próximos anos. A degradação ambiental é apontada como a principal ameaça à conservação da biodiversidade e a maior causa do declínio das populações de espécies ameaçadas, especialmente de grandes mamíferos e, particularmente, de grandes carnívoros. Apesar da ampla distribuição no continente, as populações de onça-pintada (Panthera onca) têm sido reduzidas ou extintas pela pressão antrópica, tendo sua área de distribuição reduzida aproximadamente à metade. No Brasil é considerada uma espécie Vulnerável e está Criticamente Ameaçada de extinção na Mata Atlântica. Na Ecorregião Florestas do Alto Paraná está distribuída na forma de metapopulação, possivelmente em processo de isolamento de suas subpopulações. No Alto Rio Paraná a Usina Hidrelétrica de Porto Primavera (UHEPP) alagou em 1998 uma área de cerca de 2.250 km². A dissertação aborda este impacto ambiental e tem por objetivo avaliar como o enchimento do reservatório da UHEPP afetou a população de onças-pintadas na região, em relação à distribuição espacial e adequabilidade de habitat. Foram monitorados 11 indivíduos por radiotelemetria em dois períodos (1992 a 1995 e 1998 a 2002), investigando-se o tamanho de área de uso e movimentação. Com estes dados mais a cobertura de solo foram avaliadas adequabilidade ambiental e seleção de habitat. A distribuição espacial e a adequabilidade do habitat foram avaliadas em dois cenários, anterior e posterior ao enchimento, relacionando as mudanças ocorridas com o impacto na área sob o efeito direto do enchimento. Um terceiro cenário foi também avaliado, englobando toda a área de várzea remanescente no Alto Rio Paraná e incluindo as Unidades de Conservação (UCs), quanto à adequabilidade e seleção de habitat. Para este cenário foram utilizadas localizações de outros 10 indivíduos monitorados no Parque Estadual das Várzeas do Rio Ivinhema, Mato Grosso do Sul e no Parque Estadual Morro do Diabo, São Paulo, entre 1998 e 2005. Após o enchimento houve um aumento significativo nas dimensões das áreas de uso das onças-pintadas (fêmeas: anterior, 78 km² [n=8]; posterior, 197 km² [n=5]; macho [n=1]: anterior, 111 km²; posterior, 149 km²) bem como em seus deslocamentos máximos, pois passaram a ocupar áreas mais degradadas de fazendas, com poucos refúgios e presas naturais. Porém não foram observadas diferenças nos deslocamentos médios e sobreposição de áreas, assim como as áreas de uso não diferiram sazonalmente. A modelagem de habitat demonstrou que o enchimento atingiu principalmente as áreas mais favoráveis para a espécie suprimindo cerca de metade dessas. As onças selecionaram várzeas e florestas enquanto que a paisagem altamente modificada pelo homem foi evitada. Com as áreas mais degradadas ocupadas e o conflito com o homem, grande parte dos animais foram mortos em retaliação à predação de animais domésticos, levando praticamente a extinção local da espécie na área sob o efeito direto do enchimento. A área remanescente do Alto Rio Paraná comporta ainda cerca de 50 onças-pintadas adultas, sendo um terço em UCs. Portanto áreas adjacentes às UCs, na sua maioria com várzeas, devem ser preservadas e áreas florestais devem ser restauradas para a conservação da espécie em longo prazo na região. As informações geradas nesta dissertação podem auxiliar nas ações de conservação e manejo da onça-pintada no Alto Rio Paraná e mostra que a modelagem de habitat pode ser uma importante ferramenta para avaliação de impactos ambientais. / Virtually all natural ecosystems have been affected by human activities. The construction of hydroelectric power plants is the cause of major environmental impacts, such as habitat destruction and fragmentation. Brazil's energy matrix is mostly based on hydroelectricity, which comprises approximately 70% of all produced energy in the country, and the national government plans to expand it in the coming years. Habitat destruction is considered the main threat to biodiversity conservation and the major cause of the decline of endangered species, especially large mammals and large carnivores. Despite their wide distribution in the Americas, jaguar (Panthera onca) populations have been reduced or extinguished by human pressure, and the species current distribution represents half of its past distribution. In Brazil, the jaguar is considered a vulnerable species and it is critically endangered in the Atlantic Rain Forest. In the Upper Paraná Forest Ecoregion the species is distributed as a metapopulation, possibly in an isolation process of its subpopulations. In the Upper Paraná River (UPR), the reservoir filling of the Porto Primavera Hydroelectric plant (PPHE) began in 1998, and flooded an area of approximately 2250 km ². I evaluated the effects of the PPHE reservoir filling on the local jaguar population, its effects on the species spatial distribution and habitat suitability. I monitored a total of 11 jaguars using radio telemetry in two periods (1992-1995 and 1998-2002). I investigated jaguar home range size and movements, and evaluated habitat selection and suitability combing spatial and land cover data (Geographic Information System). These two metrics were measured before and after reservoir filling, linking the impact with changes in the species spatial patterns between these two phases. I evaluated three scenarios: the area affected directly by the PPHE filling, before and after, and all the remaining wetlands in the UPR, including protected areas. Jaguars’ home ranges increased significantly after reservoir filling (females: 78 km ² before [n = 8], 197 km ² after [n = 5]; male [n = 1]: 111 km ² before, 149 km ² after). Maximum movement of the four jaguars (3 females and 1 male) also increased from one phase to the other (10.75 km before; 25.05 km after). The increase in movement patterns results from jaguars using new and more degraded areas in farms, where there are few refuges and natural preys. Home ranges did not differ seasonally on the first period; neither did the mean movements and overlapping areas. The PPHE filling mainly affected jaguars by suppressing approximately half of their suitable area. Jaguars selected wetlands and forests and avoided landscapes heavily modified by Man. Most jaguars were killed in retaliation to cattle predation as they commenced to occupy the most degraded areas, nearly driving the population to extinction. The remaining area of the UPR still holds approximately 50 adult jaguars and one third of them are in protected areas. Therefore, the long-term species conservation depends on the preservation of the adjacent wetlands and on the restoration of forest patches. My results can subsidize jaguar conservation and management plans in the UPR and show that habitat suitability modeling can be a useful tool for assessing environmental impacts.

Abstract Maintaining biodiversity in boreal forest landscapes in conjunction with forestry is a challenging task. This requires ecological understanding that is based on empirical research. In this thesis, I examined spatial and temporal occupancy patterns as well as predictability of the occurrence of the Siberian flying squirrel (Pteromys volans L.) in Finland. I used thematic maps which matched habitat requirements of the flying squirrel in forested landscapes and data on species presence and absence, which were gathered in suitable forest habitats. The results of this thesis provide applications for landscape management. First, the preferred habitat characteristics of the flying squirrel were linked to available forest data. In addition, some predictive habitat models could be used to estimate the distribution of the flying squirrel within a region. Second, based on a five year study the forests were classified as continuously occupied, continuously unoccupied and variable-occupancy patches. The dynamic occupancy pattern emphasizes the need for repeated surveys to also locate the seldom-used suitable habitats in a landscape. Third, a comparison of simulated future scenarios in long-term forest planning suggested that flying squirrel habitat might be maintained without considerable loss of timber in a landscape. Thus, a combination of ecological and economic goals in forestry planning is an encouraging alternative. Fourth, there were more polypore species in forests occupied by the flying squirrel. This suggests that conservation of the flying squirrel habitats would protect other naturally co-occurring species, and thus the flying squirrel could be assigned as an umbrella species in mature spruce-dominated forests. Based on these findings, I suggest that the flying squirrel could be used as one of the target species for forest management in boreal forest landscapes. Further research challenges are related to the examination of habitat thresholds and to the projection of future scenarios where ecological, economic and social aspects are combined to assist in complex decision making processes.

No description available.

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

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. / 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).

The goals of this study were to use Geographic Information Systems (GIS) and remote sensing technologies to gain a better understanding of habitat requirements of a population of ocelots in south Texas, and then apply this knowledge to form a predictive model to locate areas of suitable habitat in Willacy and Cameron counties, Texas. Satellite imagery from August 1991 and August 2000 were classified into four land cover types: closed canopy, open canopy, water, and urban/barren. These classified images were converted into digital thematic maps for use in resource utilization studies and modeling. Location estimates (762 from 1991 and 406 from 2000) were entered into a GIS in order to extract information about home range and resource selection. Each animal's home range was calculated using both Minimum Convex Polygon (MCP) and Kernel home range estimators (95% and 50%). Habitat parameters of interest were: soil, land cover, human density, road density, and distance to closest road, city and water body. Ocelots were found to prefer closed canopy and avoid open canopy land cover types. Ocelots preferred soils known to support thorn scrub, an indication of the importance of this habitat. Landscape metrics associated with habitat used by ocelots were determined through the use of Patch Analyst, an extension for ArcView 3.2. Contrary to expectations, ocelots utilized areas with greater fragmentation than random areas available for use. However, this use of highly fragmented areas was an indication of the degree of fragmentation of suitable habitat in the area. Further investigation of patch size selection indicated that ocelots used large sized patches disproportionately to availability, indicating a preference for larger patches. A model was created using the resource selection and habitat preference GIS database from 1991. This model was used to identify areas of “optimal”, ”sub-optimal”, and “unsuitable” habitat for ocelots in 2000. This resultant map was compared to known locations of ocelots in 2000. Ocelots were found to prefer optimal habitat and avoid unsuitable habitat, an indication that the model created was valid.

No description available.