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Fig. 4 in Physical habitat simulation for small-sized characid fish species from tropical rivers in Brazil

Fig. 4. The velocities and depth comparison between the field measurement and the numerical model for points in 50% cros- s-section width of the Velhas reach (a-b) and in 50% width of the Curimataí reach (c-d).

Figure 2 in Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics

Figure 2. Maxent predictions of suitable zebra mussel (Dreissena polymorpha) habitat in Texas. Shading indicates the logistic output of the Maxent model. Polygons represent state and national borders as well as major river basins within Texas.

Figure 1 in Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics

Figure 1. Physicochemical data survey lakes. Sites categorized by TPWD (at the time of this study in 2016) as "infested" (the water body has an established, reproducing population) or "positive" (zebra mussels or their larvae have been detected on more than one occasion despite lack of evidence of a fully established, reproducing population) are indicated by red triangles and included: Lakes Austin, Belton, Bridgeport, Dean Gilbert, Lavon, Lewisville, Ray Roberts, Stillhouse Hollow, Texoma, Travis, and Waco. Sites categorized by TPWD as zebra mussel "negative" are indicated by green circles and included: Lakes Aquilla, Buchanan, Georgetown, Granbury, Granger, Hubbard Creek, Inks, Lady Bird, LBJ, Limestone, Marble Falls, Palo Pinto, Pflugerville, Possum Kingdom, Proctor, and Whitney.

Figure 4 in Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics

Figure 4. Biplot of components 1 and 2 (top) and 1 and 3 (bottom) from Principal Component Analysis of water quality variables in 27 study lakes. Variables that predominated in each component (|factor loading| ≥ 0.50) are shown on the appropriate axes. Individual lake data are represented by symbols, with open circles representing lakes without previously reported incidences of zebra mussels (absent, 16 lakes), and solid circles those known to harbor the invasive species (present, 11 lakes) at the time of sampling (October 2016). No separation between the two lake groups is evident in either of the biplots. Ca, calcium, N, nitrogen; P, phosphorous.

Figure 3 in Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics

Figure 3. Maxent predictions of suitable quagga mussel (Dreissena bugensis) habitat in Texas. Shading indicates the logistic output of the Maxent model. Polygons represent state and national borders as well as major river basins within Texas.

opencc-by-4.0Jun 2020View details →

FIGURE 3 in Physical habitat as predictor of fish trophic structure in Brazilian Atlantic rainforest streams

FIGURE 3 | Biplot of the first two canonical functions showing how substrate composition (SC), meso-habitat variability (MH), and bank stability (BS) correlate with richness of carnivores (SCar), invertivores (SInv), omnivores (SOmn), and herbivorous-detritivores (SHD).

opencc-by-4.0Jun 2020View details →

FIGURE 1 in Physical habitat as predictor of fish trophic structure in Brazilian Atlantic rainforest streams

FIGURE 1 | Sampling sites located in the Paranapanema river basin, São Paulo state, Brazil. The Paranapanema river is highlighted in blue.

Fig. 5 in Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil

Fig. 5. Average habitat suitability index –HSI– in the upper and lower reach of the riacho São Pedro, for the three fish species.

Fig. 6 in Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil

Fig. 6. Weighted usable area (WUA) in the upper and lower reach of the riacho São Pedro, for the three fish species.

Fig. 4 in Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil

Fig. 4. Habitat suitability curves for depth (m), mean water column velocity (m/s) and type of substrate for the three fish species in the riacho São Pedro. Substrate types are; 1- large boulder, 2- boulder, 3- cobble, 4- gravel, 5- fine gravel, 6- sand and 7- vegetation.

Fig. 3 in Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil

Fig. 3. Frequencies of habitat availability by classes of depth (m), mean velocity (m/s) and type of substrate (1- large boulder; 2- boulder; 3- cobble; 4- gravel; 5- fine gravel; 6- sand and 7- vegetation), obtained by direct survey in the riacho São Pedro.

Fig. 1 in Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil

Fig. 1. Map of the rio Guandu basin, with indication of the studied reach in riacho São Pedro. Solid dash = small substation of CEDAE (Water and Sewer Treatment Works of Rio de Janeiro State). WTP: Water Treatment Plant.

Fig. 2 in Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil

Fig. 2. Isometric view of the cross-sections in the two target reaches of the riacho São Pedro. Grey color indicates water; solid line indicates the contour of the cross-section (in wet areas and banks).

opencc-by-4.0Feb 2023View details →

A habitat connectivity reality check for fish physical habitat model results and decision making for river restoration

<ol> <li>Fish physical habitat models are a tool for guiding restoration efforts in lotic ecosystems but often they overestimate restoration outcomes because currently they do not incorporate habitat connectivity. This persistent issue can, in extreme cases, result in little or no improvement to fish populations after the restoration, wasting valuable conservation resources.</li> <li>We present a case study where practitioners applied a fish habitat model for multiple life history stages of gravel spawning fishes to a 52 kilometer stretch of the Iller River but did so at a microscale implementation (every 200 meters). This approach provided an opportunity to assess the connectivity of gravel spawning fishes to find suitable habitats for all life history stages and seasonal movements.</li> <li>We used the assessed habitat estimates (availability of distinct habitat types within the 200 m reaches) to calculate the minimum distance a fish would need to go as it hypothetically “grew up” from egg to full spawning adult. We call this technique a reality check as it results in a decisive understanding of which areas were ultimately necessary to fulfill the life cycle of gravel spawning fishes, which standard assessments do not show.</li> <li>Our results show that complete connectivity still require long movement distances for vulnerable life stages to find suitable habitat. This contradicts standard practice, as restoration schemes and decision making often assume that connectivity inherently leads to more fish production without added habitat restoration.</li> <li>We recommend practitioners should perform this habitat connectivity approach when assessments implement fish habitat suitability models at similar scales. As a result, decision makers can evaluate proposed restoration sites and measures more realistically.</li> </ol>

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BES bird survey habitat features. This is a collection of the habitat features for the BES bird sampling points. Features include number of houses, proximity to trees, shrubs, grass, annuals, and other vegetation and physical features.

This dataset is associated with BES Bird Monitoring Bird Monitoring Project: ================= The BES Bird Monitoring Project is a breeding bird survey designed to find out what birds are found in the breeding season in Baltimore and where. Our monitoring efforts will show associations among block group socioeconomic variables, land cover, land use, and habitat features with breeding bird abundance, to provide information for land managers on possible consequences of land use changes on bird communities. A distinguishing feature of the bird monitoring at BES LTER, relative to other urban bird work, is the capacity for long-term monitoring of features at multiple scales through links to other parts of the project. Different processes influence habitat for birds at different scales, e.g. ongoing household level human decision-making at lot scale vs. block or neighborhood scale abandonment/re-development. Our project seeks to understand how these processes impact bird occurrence, abundance, and composition differ at the lot, block and neighborhood scale. The data consists of four major elements, Sites, Surveys, Taxalist, and Birds. Sites records the sites and their characteristics. Surveys describe the actual outings or sampling sessions. They describe the weather, the temperature, the sites visited. Taxalist provides the integration of speciaies abbreviations and common names, and Birds describes the actual sightings, linking to the other three tables. Attribute information: Here are the fields Surveys: site_id FK->Sites[site_id] survey_id survey_date time_start time_end observer wind_speed wind_dir air_temp temp_units cloud_cover notes Sites: site_id park_code park_district park_name point_code point_location park_acreage Taxalist: species_id common_name Birds: survey_id FK->surveys[survey_id] site_id FK->surveys[site_id] species_id FK->taxalist[species_id] distance bird_count notes seen heard direction time_class

openCustomSep 2013View details →

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Data from: Rock climbing activity and physical habitat attributes impact avian community diversity in cliff environments

Open the record for dataset details and reuse information.

publicJan 2019View details →

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Fig. 6 in Physical habitat simulation for small-sized characid fish species from tropical rivers in Brazil

Fig. 6. Weight usable area (WUA) in percentage for four species at the Velhas reach in the dry scenario (a) and in the rainy scenario (b). Highest WUA is indicated in red and lowest WUA is indicated in dark blue. The reach length is 1 km. The red line indicated the area along the reach where the habitat use was observed.

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Fig. 2 in Physical habitat simulation for small-sized characid fish species from tropical rivers in Brazil

Fig. 2. Suitability curves for velocity (m/s) (a), depth (b) and channel index (c) for the four studied species at the Ve- lhas river basin.

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Fig. 5 in Physical habitat simulation for small-sized characid fish species from tropical rivers in Brazil

Fig. 5. Hydrodynamic (depth and water velocity) for the Velhas reach in the dry (a) and rainy (b) scenario; and for the Curimataí reach in the dry (c) the rainy (d) scenario. The reach length is 1 km.