Resilience and Alternative Stable States After Desert Wildfires

Improving models of community change is a fundamental goal in ecology and has renewed importance during global change and increasing human disturbance of the biosphere. Using the Mojave Desert (southwestern United States) as a model system, invaded by nonnative plants and subject to wildfire disturbances, we examined models of resilience, alternative stable states, and convergent-divergent trajectories for 36 yr of plant community change after 31 wildfires in communities dominated by the native shrubs Larrea tridentata or Coleogyne ramosissima. Perennial species richness on average was fully resilient within 23 yr after disturbance in both community types. Perennial cover was fully resilient within 25 yr in the Larrea community, but recovery was projected to require 52 yr in the Coleogyne community. Species composition shifts were persistent, and in the Coleogyne community, the projected compositional recovery time of 550 yr and increasing resembled a deflected trajectory toward potential alternative states. Disturbed sites contained a perennial species composition of predominately short-statured forbs, subshrubs, and grasses, contrasting with the larger-statured shrub and tree structure of undisturbed sites. Auxiliary data sets characterizing species recruitment, annual plants including nonnative grasses, biocrust communities, and soils showed persistent differences between disturbed and undisturbed sites consistent with positive feedbacks potentially contributing to alternative stable states. Resprouting produced limited resilience for the large shrubs L. tridentata and Yucca spp. important to population persistence but did not forestall long-term reduced abundance of the species. The nonnative annual grass Bromus rubens increased on disturbed sites over time, suggesting persistently abundant nonnative plant fuels and reburn potential. Biocrust cover on disturbed sites was half and species richness a third of amounts on undisturbed sites. Soil nitrogen was 30% greater on disturbed sites and no significant trend was evident for it to decline on even the oldest burns. Disturbed desert plant communities simultaneously supported all three models of resilience, alternative stable states, and convergent-divergent trajectories among community measures (e.g., species richness, composition), timeframes since disturbance, and spatial resolutions. Accommodating expression within ecosystems of multiple models, including those opposing each other, may help broaden theoretical models of ecosystem change

Controlling Argan Seed Quality by NIR

The suitability of using visible/near infrared spectroscopy (Vis/NIR), as a rapid and non-destructive technique for monitoring the quality of argan seeds (Argania spinosa Skeels) was studied. The analyzed parameters were the fatty acid composition of argan seed oil, seed moisture content, seed oil content and oil stability index (OSI). The ratio between major unsaturated and saturated fatty acids (U/S) during the oxidation assay at constant temperature was studied. Values from infrared drying were used as a laboratory reference for the moisture. Argan seed oil content was determined by Soxhlet extraction. A fatty acid analysis was carried out by gas chromatography and the OSI was determined by the Rancimat test. Predictive models of argan seed moisture, ratio U/S and OSI showed good accuracy. Therefore, Vis/NIR measurements can be used for controlling several argan seed quality parameters. This procedure might be of interest to the argan oil industry, which is currently in the process of modernization and expansion.The authors are grateful to the Spanish Agency for the International Cooperation (AECID-MAE) for financing the Project A/019935/08. They also express their gratitude to the industrial plant Argan Oil Company Ltd. Morocco for the argan seed supply.Peer Reviewe

A spatial econometric approach to designing and rating scalable index insurance in the presence of missing data

Index-Based Livestock Insurance has emerged as a promising market-based solution for insuring livestock against drought-related mortality. The objective of this work is to develop an explicit spatial econometric framework to estimate insurable indexes that can be integrated within a general insurance pricing framework. We explore the problem of estimating spatial panel models when there are missing dependent variable observations and cross-sectional dependence, and implement an estimable procedure which employs an iterative method. We also develop an out-of-sample efficient cross-validation mixing method to optimise the degree of index aggregation in the context of spatial index models