Applying fire connectivity and centrality measures to mitigate the cheatgrass-fire cycle in the arid West,USA

Context

Strategic placement of fuel treatments across large landscapes is an important step to mitigate the collective effects of fires interacting over broad spatial and temporal extents. On landscapes where highly invasive cheatgrass (Bromus tectorum) is increasing fire activity, such an approach could help maintain landscape resilience.

Objectives

Our objectives are to 1) model and map fire connectivity on a cheatgrass-invaded landscape, as well as the centrality of large cheatgrass patches, in order to inform a landscape fuel treatment (i.e., a network of greenstrips); and 2) evaluate the modeled greenstrip network based on changes to cheatgrass patch centrality.

Methods

Our analysis covers 485-km² on the Kaibab National Forest in Northern Arizona. We apply a circuit-theoretic model of fire connectivity between all pairs of large cheatgrass patches. Based on these results, we calculate a measure of centrality for each patch to inform fuel treatment placement. We evaluate the modeled greenstrip network by comparing the pre- and post-treatment centrality of each patch.

Results

After modeling fire connectivity across the landscape, we identify 25 of 68 large cheatgrass patches with relatively high centrality. When we simulate greenstrips around these focal patches, model results suggest that they are effective in reducing the centrality for at least 19 of the 25 patches.

Conclusions

Fire connectivity models provide robust network centrality measures, which can help generate multiple, landscape fuel treatment alternatives and facilitate on-the-ground decisions. The extension of these methods is well suited for landscape fuels management in other vegetation communities and ecosystems.

     

Root architecture might account for contrasting establishment success of <Emphasis Type="Italic">Pseudotsuga menziesii</Emphasis> var. <Emphasis Type="Italic">menziesii</Emphasis> and <Emphasis Type="Italic">Pinus sylvestris</Emphasis> in Central Europe under dry conditions

Key message

Pinus sylvestris seedlings quickly expand their roots to deeper soil layers while Pseudotsuga menziesii concentrates its root system in the topsoil, thereby running the risk of desiccation during long dry spells, as indicated by lower survival after simulated summer drought.

Context

Pseudotsuga menziesii (Douglas-fir) is regarded as a promising species to maintain the productivity of Central European lowland forests given the projected increase of long dry spells.

Aims

Will the species be able to regenerate from seed and spread outside plantations in a drier temperate Europe?

Methods

We measured the relative growth rate, biomass allocation, root architecture, and phenotypic plasticity of Pseudotsuga menziesii seedlings sown in a common garden and grown under current precipitation and prolonged drought, respectively. The species’ competitive ability with respect to Pinus sylvestris L., the most drought-tolerant native conifer in Central Europe, was assessed during three growing seasons.

Results

Pinus sylvestris seedlings had higher relative growth rates than did Pseudotsuga menziesii seedlings, first in terms of aboveground biomass and later in terms of shoot height. This resulted in heavier and taller seedlings after three growing seasons under both moist and dry conditions. Shorter vertical roots corresponded with lower survival of Pseudotsuga menziesii seedlings under dry conditions.

Conclusion

Fast root proliferation allows Pinus sylvestris seedlings to reach deeper water pools that are less rapidly depleted during transient drought. By contrast, the shallow root system might put Pseudotsuga menziesii seedlings at the risk of desiccation during prolonged dry spells.

     

Propagating the errors of initial forest variables through stand- and tree-level growth simulators

Developments in the field of remote sensing have led to various cost-efficient forest inventory methods at different levels of detail. Remote-sensing techniques such as airborne laser scanning (ALS) and digital photogrammetry are becoming feasible alternatives for providing data for forest planning. Forest-planning systems are used to determine the future harvests and silvicultural operations. Input data errors affect the forest growth projections and these effects are dependent on the magnitude of the error. Our objective in this study was to determine how the errors typical to different inventory methods affect forest growth projections at individual stand level during a planning period of 30 years. Another objective was to examine how the errors in input data behave when different types of growth simulators are used. The inventory methods we compared in this study were stand-wise field inventory and single-tree ALS. To study the differences between growth models, we compared two forest simulators consisting of either distance-independent tree-level models or stand-level models. The data in this study covered a 2,000-ha forest area in southern Finland, including 240 sample plots with individually measured trees. The analysis was conducted with Monte Carlo simulations. The results show that the tree-level simulator is less sensitive to errors in the input data and that by using single-tree ALS data, more precise growth projections can be obtained than using stand-wise field inventory data.     

Effect of spineless cactus intake (<Emphasis Type="Italic">Opuntia ficus-indica</Emphasis>) on blood glucose levels in lactating sows and its impact on feed intake,body weight loss,and weaning-estrus interval

The effect of spineless cactus intake (Opuntia ficus-indica) on blood glucose (BG) levels in lactating sows and its impact on daily and total feed intake (dFI^?1 and TFI, respectively), body weight loss (BWL), and weaning-estrus interval length (WEI) were evaluated. Thirty-four hybrid (Yorkshire × Landrace × Pietrain) sows in lactation phase were used. Sows were divided into two groups: G1 (n = 17) where they received commercial feed and G2 (n = 17) provided with commercial feed plus an average of 2.0 ± 0.5 kg spineless cactus, based on a sow’s body weight. The variables evaluated were BG, dFI^?1, TFI, BWL, and WEI. Statistical analysis was performed by using a fixed and mixed model methodology, under a repeated measurements experiment. Group effects were found on all analyzed variables (P < 0.05). The BG was lower in G2 (55.2 and 64.5 mg/dL pre- and post-prandial, respectively), compared to that in G1 (70.9 and 80.1 mg/dL pre- and post-prandial, respectively) (P < 0.05). G2 showed better performance than G1 for dFI^?1, BWL, and WEI (P < 0.05) whose averages were 5.5 ± 1.8 kg, 7.4 ± 4.5%, and 5.3 ± 1.2 days, respectively. Averages for these variables in G1 were 4.7 ± 1.5 kg, 16.8 ± 4.6%, and 6.1 ± 1.6 days, respectively. Intake of spineless cactus reduced BG levels in lactating sows, generating greater dFI^?1, lower BWL at the end of lactation, and a lower WEI.     

Estimation of genetic parameters for growth traits of Japanese flounder <Emphasis Type="Italic">Paralichthys olivaceus</Emphasis> using an animal model

To estimate genetic parameters of growth traits in Japanese flounder Paralichthys olivaceus, full-sib and half-sib families were produced in three consecutive years at the Beidaihe Central Experiment Station in China. Each year 8–28 families were produced. The body weight, body length and body depth at 180, 240, and 360 days of age were measured for 5,224 individuals. Four animal models were used to examine the phenotypic variation of growth traits and were compared using the likelihood ratio test. The results showed that estimates for additive genetic effect heritabilities varied greatly depending on the model, trait and age. The maternal effect had a significant impact on phenotypic variation only for body depth at 180 days of age, which explained 49% of the phenotypic variance. The ratio of full-sib effect to phenotypic variation ranged from 0.09 to 0.22. Growth traits all exhibited low heritability (0.13–0.39), indicating that there is the potential for family selection breeding for these traits in Japanese flounder. Using the full model with the fixed, full-sib family, additive and maternal genetic effects, genetic correlations among the three traits for fish of the same age were estimated to be more than 0.80. Generally, the genetic correlations gradually increased as age increased.     

Relationship between the incidence of latent infections caused by <Emphasis Type="Italic">Monilinia</Emphasis> spp<Emphasis Type="Italic">.</Emphasis> and the incidence of brown rot of peach fruit: factors affecting latent infection

Five field experiments were performed in commercial orchards located in Lleida (Spain) over three growing seasons, 2000–2002, in order to estimate the relationship between the incidence of latent infection caused by Monilinia spp. in peaches and the incidence of post-harvest brown rot. No latent infection was recorded at popcorn and the maximum incidence occurred pre-harvest; in some orchards a second peak was detected during the pit hardening period. Monilinia laxa is the most prevalent species isolated from peaches with brown rot. There was a positive correlation between the incidence of latent infection and that of post-harvest brown rot. The average incidence of latent infection during the crop season explained 55% of the total variation in the incidence of post-harvest brown rot. The effect of temperature (T) and duration of wetness (W) on the incidence of latent infection in peach and nectarine orchards was analysed using multiple regression. The regression analysis indicated that T and W jointly explained 83% of the total variation in the incidence of latent infection. The model predicts no latent infections when T < 8°C, and >22 h wetness are required when T = 8°C but only 5 h at 25°C are necessary for latent infection to occur. The incidence of brown rot and latent infection of peaches caused by M. laxa under controlled experimental conditions were also affected by T and W, as well as by fruit maturity and inoculum concentration. Latent infections were produced in fruit when T was not suitable for the development of brown rot symptoms. In these experiments more than 4–5 h of daily wetness were required after embryo growth in fruit sprayed to run-off with an inoculum concentration higher than 10⁴ conidia ml⁻¹ of M. laxa for brown rot and latent infections to develop. The fitted model obtained from the field data was able to predict the observed data obtained under controlled environmental conditions.     

An integrated approach of remote sensing,GIS and swarm intelligence for zoning protected ecological areas

Interest in protecting ecological areas is increasing because of land uses conflicts and environmental pressures. The optimal zoning of protected ecological areas belongs to a NP-hard problem because it is subject to both box and spatial constraints. A challenge in solving area optimization problems emerges with the increasing size of a study region. In this article, an integrated approach of remote sensing, GIS and modified ant colony optimization (ACO) is proposed for application in zoning protected ecological areas. Significant modifications have been made in the conventional ACO so that it can be further extended to solve zoning problems in large regions. An improved selection strategy is designed to accelerate the progress of sites selection for artificial ants. Another important modification in ACO is to incorporate the neighborhood diffusion strategy into pheromone updating. The optimal objective is to generate protected areas that maximize both ecological suitability and spatial compactness. The modified ACO model has been successfully applied to a case study involving an area of 25,483 cells in Dongguan, Guangdong, China. The experiments have demonstrated that the proposed model is an efficient and effective optimization technique for generating optimal protection. The modified ACO model only requires approximately 119 s for determining near-optimal solutions. Furthermore, the proposed method performs better than other methods, including simulated annealing, genetic algorithm, iterative relaxation, basic ACO, and density slicing.     

A riverscape perspective on habitat associations among riverine bird assemblages in the Lake Champlain Basin,USA

The riverscape perspective recognizes the heterogeneous habitat types within the stream corridor as a single, integrated ecological unit operating across spatial scales. Although there is ample evidence that the riverscape notion is appropriate in understanding the physical phenomena of stream corridors, significantly less attention has focused on its ecological ramifications. To this end, we surveyed riverscape habitat variables and bird community characteristics in the Champlain Valley of Vermont, USA. From the data collected, we used information theoretic methodology (AIC_c) to model relationships between bird community attributes and key habitat variables across the riverscape. Our models with the greatest support suggest that riverine bird communities respond to a suite of characteristics; representing a variety of riverscape habitats at the in-stream, floodplain, and riparian levels. Channel slope, drainage area, percent conifers, and in-stream habitat condition were among the most influential variables. We found that piscivores are potentially important indicators of riverscape condition, responding to a host of variables across the riverscape. Our results endorse a holistic approach to assessing and managing the mosaic of patches in the riverscape and suggest that a riverscape approach has significant conservation potential.     

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.