Nitrate, phosphate, and ammonium loads at subsurface drains: agroecosystems and nitrogen management

Artificial subsurface drainage in cropland creates pathways for nutrient movement into surface water; quantification of the relative impacts of common and theoretically improved management systems on these nutrient losses remains incomplete. This study was conducted to assess diverse management effects on long-term patterns (1998-2006) of NO, NH, and PO loads (). We monitored water flow and nutrient concentrations at subsurface drains in lysimeter plots planted to continuous corn ( L.) (CC), both phases of corn-soybean [ (L.) Merr.] rotations (corn, CS; soybean, SC), and restored prairie grass (PG). Corn plots were fertilized with preplant or sidedress urea-NHNO (UAN) or liquid swine manure injected in the fall (FM) or spring (SM). Restored PG reduced NO eightfold compared with fields receiving UAN (2.5 vs. 19.9 kg N ha yr; < 0.001), yet varying UAN application rates and timings did not affect NO across all CCUANs and CSUANs. The NO from CCFM (33.3 kg N ha yr) were substantially higher than for all other cropped fields including CCSM (average 19.8 kg N ha yr, < 0.001). With respect to NH and PO, only manured soils recorded high but episodic losses in certain years. Compared with the average of all other treatments, CCSM increased NH in the spring of 1999 (217 vs. 680 g N ha yr), while CCFM raised PO in the winter of 2005 (23 vs. 441 g P ha yr). Our results demonstrate that fall manuring increased nutrient losses in subsurface-drained cropland, and hence this practice should be redesigned for improvement or discouraged.     

Feature Extraction Techniques for Measuring Piñon and Juniper Tree Cover and Density,and Comparison with Field-Based Management Surveys

Western North America is experiencing a dramatic expansion of piñon (Pinus spp.) and juniper (Juniperus spp.) (P-J) trees into shrub-steppe communities. Feature extracted data acquired from remotely sensed imagery can help managers rapidly and accurately assess this land cover change in order to manage rangeland ecosystems at a landscape-scale. The objectives of this study were to: (1) develop an effective and efficient method for accurately quantifying P-J tree canopy cover and density directly from high resolution photographs and (2) compare feature-extracted data to typical in-situ datasets used by land managers. Tree cover was extracted from aerial-photography using Feature Analyst^®. Tree density was calculated as the sum of the total number of individual polygons (trees) within the tree cover output file after isolation using a negative buffer post-processing technique. Feature-extracted data were compared to ground reference measurements from Utah’s Division of Wildlife Resources Range Trend Project (DWR-RTP). We found that the proposed feature-extraction techniques used for measuring cover and density were highly correlated to ground reference and DWR-RTP datasets. Feature-extracted measurements of cover generally showed a near 1:1 relationship to these data, while tree density was underestimated; however, after calibration for juvenile trees, a near 1:1 relationship was realized. Feature-extraction techniques used in this study provide an efficient method for assessing important rangeland indicators, including: density, cover, and extent of P-J tree encroachment. Correlations found between field and feature-extracted data provide evidence to support extrapolation between the two approaches when assessing woodland encroachment.     

A brief guide to model selection,multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion

Akaike’s information criterion (AIC) is increasingly being used in analyses in the field of ecology. This measure allows one to compare and rank multiple competing models and to estimate which of them best approximates the “true” process underlying the biological phenomenon under study. Behavioural ecologists have been slow to adopt this statistical tool, perhaps because of unfounded fears regarding the complexity of the technique. Here, we provide, using recent examples from the behavioural ecology literature, a simple introductory guide to AIC: what it is, how and when to apply it and what it achieves. We discuss multimodel inference using AIC—a procedure which should be used where no one model is strongly supported. Finally, we highlight a few of the pitfalls and problems that can be encountered by novice practitioners.     

Growth rate and retention of learned predator cues by juvenile rainbow trout: faster-growing fish forget sooner

Under conditions of spatial and/or temporal variability in predation risk, prey organisms often rely on acquired predator recognition to balance the trade-offs between energy intake and risk avoidance. The question of ‘for how long’ should prey retain this learned information is poorly understood. Here, we test the hypothesis that the growth rate experienced by prey should influence the length of the ‘memory window’. In a series of laboratory experiments, we manipulated growth rate of juvenile rainbow trout and conditioned them to recognize a novel predator cue. We subsequently tested for learned recognition either 24 h or 8 days post-conditioning. Our results suggest that trout with high versus low growth rates did not differ in their response to learned predator cues when tested 24 h post-conditioning. However, trout on a high growth rate exhibited no response to the predator cues after 8 days (i.e. did not retain the recognition of the predator odour), whereas trout on a lower growth rate retained a strong recognition of the predator. Trout that differed in their growth rate only after conditioning did not differ in their patterns of retention, demonstrating growth rate after learning does not influence retention. Trout of different initial sizes fed a similar diet (percent body mass per day) showed no difference in retention of the predator cue. Together, these data suggest that growth rate at the time of conditioning determines the ‘memory window’ of trout. The implications for threat-sensitive predator avoidance models are described.     

Advancing effects analysis for integrated, large-scale wildfire risk assessment

In this article, we describe the design and development of a quantitative, geospatial risk assessment tool intended to facilitate monitoring trends in wildfire risk over time and to provide information useful in prioritizing fuels treatments and mitigation measures. The research effort is designed to develop, from a strategic view, a first approximation of how both fire likelihood and intensity influence risk to social, economic, and ecological values at regional and national scales. Three main components are required to generate wildfire risk outputs: (1) burn probability maps generated from wildfire simulations, (2) spatially identified highly valued resources (HVRs), and (3) response functions that describe the effects of fire (beneficial or detrimental) on the HVR. Analyzing fire effects has to date presented a major challenge to integrated risk assessments, due to a limited understanding of the type and magnitude of changes wrought by wildfire to ecological and other nonmarket values. This work advances wildfire effects analysis, recognizing knowledge uncertainty and appropriately managing it through the use of an expert systems approach. Specifically, this work entailed consultation with 10 fire and fuels program management officials from federal agencies with fire management responsibilities in order to define quantitative resource response relationships as a function of fire intensity. Here, we demonstrate a proof-of-concept application of the wildland fire risk assessment tool, using the state of Oregon as a case study.     

A global ecological signal of extinction risk in terrestrial vertebrates

To determine the distribution and causes of extinction threat across functional groups of terrestrial vertebrates, we assembled an ecological trait data set for 18,016 species of terrestrial vertebrates and utilized phylogenetic comparative methods to test which categories of habitat association, mode of locomotion, and feeding mode best predicted extinction risk. We also examined the individual categories of the International Union for Conservation of Nature Red List extinction drivers (e.g., agriculture and logging) threatening each species and determined the greatest threats for each of the four terrestrial vertebrate groups. We then quantified the sum of extinction drivers threatening each species to provide a multistressor perspective on threat. Cave dwelling amphibians (p < 0.01), arboreal quadrupedal mammals (all of which are primates) (p < 0.01), aerial and scavenging birds (p < 0.01), and pedal (i.e., walking) squamates (p < 0.01) were all disproportionately threatened with extinction in comparison with the other assessed ecological traits. Across all threatened vertebrate species in the study, the most common risk factors were agriculture, threatening 4491 species, followed by logging, threatening 3187 species, and then invasive species and disease, threatening 2053 species. Species at higher risk of extinction were simultaneously at risk from a greater number of threat types. If left unabated, the disproportionate loss of species with certain functional traits and increasing anthropogenic pressures are likely to disrupt ecosystem functions globally. A shift in focus from species- to trait-centric conservation practices will allow for protection of at-risk functional diversity from regional to global scales.     

A Framework for Geographically Sensitive and Efficient Recycling Networks

This paper has three primary objectives. First, it seeks to demonstrate that recycling is an important component of sustainable human systems, particularly in the case of electronics, where environmental impacts of disposal are potentially severe. Second, it presents a methodology that could be used to estimate the volumes of electronics or other consumer durable goods that are available for recycling. Third and last, it illustrates, through a case study of Atlanta focused on computers, that metropolitan areas may fruitfully be viewed as opportune centres from which to mine, recycle and reuse cast-off electronic goods. From an environmental and economic development policy perspective, doing so presents an important opportunity to provide new economic opportunities in the most distressed portions of metropolitan areas which have been disproportionately impacted by previous environmentally destructive industrialization practices.     

Effects of life history and reproduction on recruitment time lags in reintroductions of rare plants

Reintroductions are important components of conservation and recovery programs for rare plant species, but their long-term success rates are poorly understood. Previous reviews of plant reintroductions focused on short-term (e.g., ≤3 years) survival and flowering of founder individuals rather than on benchmarks of intergenerational persistence, such as seedling recruitment. However, short-term metrics may obscure outcomes because the unique demographic properties of reintroductions, including small size and unstable stage structure, could create lags in population growth. We used time-to-event analysis on a database of unusually well-monitored and long-term (4–28 years) reintroductions of 27 rare plant species to test whether life-history traits and population characteristics of reintroductions create time-lagged responses in seedling recruitment (i.e., recruitment time lags [RTLs]), an important benchmark of success and indicator of persistence in reintroduced populations. Recruitment time lags were highly variable among reintroductions, ranging from <1 to 17 years after installation. Recruitment patterns matched predictions from life-history theory with short-lived species (fast species) exhibiting consistently shorter and less variable RTLs than long-lived species (slow species). Long RTLs occurred in long-lived herbs, especially in grasslands, whereas short RTLs occurred in short-lived subtropical woody plants and annual herbs. Across plant life histories, as reproductive adult abundance increased, RTLs decreased. Highly variable RTLs were observed in species with multiple reintroduction events, suggesting local processes are just as important as life-history strategy in determining reintroduction outcomes. Time lags in restoration outcomes highlight the need to scale success benchmarks in reintroduction monitoring programs with plant life-history strategies and the unique demographic properties of restored populations. Drawing conclusions on the long-term success of plant reintroduction programs is premature given that demographic processes in species with slow life-histories take decades to unfold.