Determining landscape extent for succession and disturbance simulation modeling

Dividing regions into manageable landscape units presents special problems in landscape ecology and land management. Ideally, a landscape should be large enough to capture a broad range of vegetation, environmental and disturbance dynamics, but small enough to be useful for focused management objectives. The purpose of this study was to determine the optimal landscape size to summarize ecological processes for two large land areas in the southwestern United States. We used a vegetation and disturbance dynamics model, LANDSUMv4, to simulate a set of nine scenarios involving systematically varied topography, map resolution, and model parameterizations of fire size and fire frequency. Spatial input data were supplied by the LANDscape FIRE Management Planning System (LANDFIRE) prototype project, an effort that will provide comprehensive and scientifically credible mid-scale data to support the National Fire Plan. We analyzed output from 2,000 year simulations to determine the thresholds of landscape condition based on the variability of burned area and dominant vegetation coverage. Results show that optimal landscape extent using burned area variability is approximately 100 km² depending on topography, map resolution, and model parameterization. Variability of dominant vegetation area is generally higher and the optimal landscape sizes are larger in comparison to those features determined from burned area. Using the LANDFIRE project as a case study, we determined landscape size and map resolution for a large mapping project, and showed that optimal landscape size depends upon geographical, ecological, and management context. This paper was written and prepared by U.S. Government employees on official time, and therefore is in the public domain and not subject to copyright. The use of trade or firm names in this paper is for reader information and does not imply endorsement by the U.S. Department of Agriculture of any product or service.     

Effect of road density on abundance of white-footed mice

While several studies have demonstrated that roads can act as barriers to small mammal movement, the relationship between road density and small mammal abundance has not yet been investigated. In southeastern Ontario, Peromyscus leucopus (white-footed mice) suffer high over-winter mortality rates, resulting in small springtime populations and frequent local extinctions. Peromyscus leucopus movement is known to be inhibited by roads, which should result in lower rates of immigration into and recolonization of habitats in landscapes with high road density. We tested two predictions: (1) Forest sites situated in landscapes with high road densities have a higher chance of P. leucopus being absent during the early spring than forest sites situated in landscapes with low road densities and (2) P. leucopus populations during the summer are smaller in forest sites situated in landscapes with high road densities than in landscapes with low road densities. We sampled P. leucopus in focal patches within nineteen landscapes (7 rural, low-road-density landscapes; 7 rural, high-road-density landscapes; 5 urban landscapes). There was no significant relationship between road density and the presence/absence of P. leucopus during the early spring. We found a significant positive effect of road density on P. leucopus relative abundance during the summer, even when we excluded the urban landscapes and based the analysis on only the 14 rural landscapes. Our results suggest that any negative effect of roads on P. leucopus populations, created by their inhibition to moving across roads, is far outweighed by some positive effect of roads on P. leucopus abundance. We suggest that the two most likely explanations are that roads are positively correlated with an important as-yet-undetermined component of habitat quality, or that roads positively affect P. leucopus by negatively affecting their predators.     

Testing assumptions of cost surface analysis—a tool for invasive species management

Applied ecology could benefit from new tools that identify potential movement pathways of invasive species, particularly where data are sparse. Cost surface analysis (CSA) estimates the permeability (friction) across a landscape and can be applied to dispersal modelling. Increasingly used in a diversity of applications, several fundamental assumptions that might influence the outputs of CSA (cost surfaces and least-cost pathways) have yet to be systematically examined. Thus, we explore two issues: the presumed relationship between habitat preferences and dispersal behaviour as well as the degree of landscape fragmentation through which an organism moves by modelling a total of 18 sensitivity and dispersal scenarios. We explored the effect of fragmentation by altering the friction values (generally assigned using expert opinion) associated with patch and linear features. We compared these sensitivity scenarios in two sites that differed in fragmentation. We also used eastern grey squirrels (Sciurus carolinensis) as an example invading species and compared diffusion models and two contrasting cost surface dispersal scenarios. The diffusion model underestimated spread because squirrels did not move randomly through the landscape. Despite contrasting assumptions regarding dispersal behaviour, the two cost surfaces were strikingly similar while the least-cost paths differed. Furthermore, while the cost surfaces were insensitive to changes in friction values for linear features, they were sensitive to assumptions made for patch features. Our results suggest that movement in fragmented landscapes may be more sensitive to assumptions regarding friction values than contiguous landscapes. Thus, the reliability of CSA may depend not only on the range of friction values used for patches but also the degree of contiguity in the landscape.     

Empirical relationships between land use/cover and estuarine condition in the Northeastern United States

Land–water interactions were examined in three regions in the Virginian Biogeographic Province; the southern shore of Cape Cod, Massachusetts; the Hudson/Raritan region of New York; and the eastern shore of the Delmarva (Delaware/Maryland/Virginia) Peninsula. Cumulative distribution functions were used to evaluate similarity in environmental condition among estuaries. Spatial-setting variables (location in a river, coastal lagoon, or in open waters) were associated with variation for some measures of estuarine condition. Patterns of coastal urban and agriculture gradients were measured and their relationship with indicators of estuarine condition was modeled statistically. When estuaries were pooled, the highest variation explained by spatial-setting variables was found for dissolved oxygen (DO, R ² = 0.44) and salinity (R ² = 0.58), with DO decreasing in river locations and salinity decreasing with rainfall and sampling locations near rivers. The explanatory power for the other indicator variables was low and varied from 6% to 27%. Rainfall explained some of the variation (R ² = 0.23) in total suspended solids. Moderate (0.4 < | r | < 0.7) to strong (| r | ≥ 0.7) linear associations were found between total urban area and measures of estuarine condition. Within regions, total urban area was positively associated with Silver (r = 0.59), Cadmium (r = 0.65), and Mercury (r = 0.47) in Cape Cod, and inversely related to DO (r = −0.65) in the Hudson/Raritan region. No associations were found in the Delmarva Peninsula study area. Total area of agriculture showed a moderate association with Arsenic in Cape Cod, but no other associations were found in the other two regions. Our analyses show a measurable impact of urban land use on coastal ecosystem condition over large areas of the northeastern United States. This pattern was most evident when many different landscapes were considered simultaneously. The relationship between urban development and estuarine condition were weaker within the individual regions studied. The use of land use/cover models for predicting estuarine condition is a challenging task that warrants enhancements in the type, quantity, and quality of data to improve our ability to discern relationships between anthropogenic activities on land and the condition of coastal environments.     

The relationship between serum IGF-1, IGFBP-3 levels,bone mineral density and bone metabolic markers in postmenopausal patients with osteoporosis

AIM:To study the relationship between serum insulin-like growth factor- 1 (IGF-1), insulin-like growth factor binding protein-3 (IGFBP-3) levels, bone mineral density(BMD) and bone metabolic markers in postmenopausal women. METHODS:Serum IGF-1, IGFBP-3, osteocalcin(BGP), isomeric C-telopeptide of type I collagen (β-CTX), estradiol(E₂), calcitonin(CT), parathormone(PTH), calcium (Ca), and phosphorus(P) were measured in 90 postmenopausal osteoporosis patients and 70 healthy postmenopausal women. BMD of lumbar vertebra and left femoral neck were determined by dual energy X-ray absorptiometry.RESULTS:BMD of lumbar vertebra and left femoral neck decreased significantly (P<0.01), serum IGF-1, IGFBP-3, E₂, CT and BGP decreased significantly (P<0.01), serum β-CTX and PTH increased significantly in postmenopausal osteoporosis group (P<0.01). There were no significantly differences in serum Ca, P between two groups (P>0.05). BMD of lumbar vertebra and left femoral neck were positively correlated with serum IGF-1, IGFBP-3, E₂, CT and BGP, but negatively correlated with β CTX and PTH. There were no correlation with serum Ca, P and BMD.CONCLUSION:Serum IGF-1, IGFBP-3, E₂, CT, BGP, β-CTX and PTH level were correlated with BMD of lumbar vertebra and left femoral neck. These markers can be one of the valuable evidences for screening osteoporosis in postmenopausal women.