Disentangling linkages between satellite-derived indicators of forest structure and productivity for ecosystem monitoring.

The essential biodiversity variables (EBV) framework has been proposed as a monitoring system of standardized, comparable variables that represents a minimum set of biological information to monitor biodiversity change at large spatial extents. Six classes of EBVs (genetic composition, species populations, species traits, community composition, ecosystem structure and ecosystem function) are defined, a number of which are ideally suited to observation and monitoring by remote sensing systems. We used moderate-resolution remotely sensed indicators representing two ecosystem-level EBV classes (ecosystem structure and function) to assess their complementarity and redundancy across a range of ecosystems encompassing significant environmental gradients. Redundancy analyses found that remote sensing indicators of forest structure were not strongly related to indicators of ecosystem productivity (represented by the Dynamic Habitat Indices; DHIs), with the structural information only explaining 15.7% of the variation in the DHIs. Complex metrics of forest structure, such as aboveground biomass, did not contribute additional information over simpler height-based attributes that can be directly estimated with light detection and ranging (LIDAR) observations. With respect to ecosystem conditions, we found that forest types and ecosystems dominated by coniferous trees had less redundancy between the remote sensing indicators when compared to broadleaf or mixed forest types. Likewise, higher productivity environments exhibited the least redundancy between indicators, in contrast to more environmentally stressed regions. We suggest that biodiversity researchers continue to exploit multiple dimensions of remote sensing data given the complementary information they provide on structure and function focused EBVs, which makes them jointly suitable for monitoring forest ecosystems.

Keeping an ear out: size relationship of the tympanic bullae and pinnae in bandicoots and bilbies (Marsupialia: Peramelemorphia).

Bandicoots and bilbies (Order Peramelemorphia) occupy a broad range of habitats across Australia and New Guinea, from open, arid deserts to dense forests. This once diverse group has been particularly vulnerable to habitat loss and introduced eutherian predators, and numerous species extinctions and range retractions have occurred. Understanding reasons for this loss requires greater understanding of their biology. Morphology of the pinnae and tympanic bullae varies markedly among species. As hearing is important for both predator avoidance and prey location, the variability in ear morphology could reflect specialization and adaptation to specific environments, and therefore be of conservation relevance. We measured 798 museum specimens representing 29 species of Peramelemorphia. Controlling for phylogenetic relatedness and head length, pinna surface area was weakly negatively correlated with average precipitation (rainfall being our surrogate measure of vegetation productivity/complexity), and there were no environmental correlates with effective diameter (pinna width). Controlling for phylogenetic relatedness and skull length, tympanic bulla volume was negatively correlated with precipitation. Species that inhabited drier habitats, which would be open and allow sound to carry further with less obstruction, had relatively larger pinnae and tympanic bullae. In contrast, species from higher rainfall habitats, where sounds would be attenuated and diffused by dense vegetation, had the smallest pinnae and bullae, suggesting that low-frequency hearing is not as important in these habitats. Associations with temperature did not reach statistical significance. These findings highlight linkages between hearing traits and habitat that can inform conservation and management strategies for threatened species.

Plant functional traits differ in adaptability and are predicted to be differentially affected by climate change.

Climate change is testing the resilience of forests worldwide pushing physiological tolerance to climatic extremes. Plant functional traits have been shown to be adapted to climate and have evolved patterns of trait correlations (similar patterns of distribution) and coordinations (mechanistic trade-off). We predicted that traits would differentiate between populations associated with climatic gradients, suggestive of adaptive variation, and correlated traits would adapt to future climate scenarios in similar ways.We measured genetically determined trait variation and described patterns of correlation for seven traits: photochemical reflectance index (PRI), normalized difference vegetation index (NDVI), leaf size (LS), specific leaf area (SLA), δ13C (integrated water-use efficiency, WUE), nitrogen concentration (NCONC), and wood density (WD). All measures were conducted in an experimental plantation on 960 trees sourced from 12 populations of a key forest canopy species in southwestern Australia.Significant differences were found between populations for all traits. Narrow-sense heritability was significant for five traits (0.15-0.21), indicating that natural selection can drive differentiation; however, SLA (0.08) and PRI (0.11) were not significantly heritable. Generalized additive models predicted trait values across the landscape for current and future climatic conditions (>90% variance). The percent change differed markedly among traits between current and future predictions (differing as little as 1.5% (δ13C) or as much as 30% (PRI)). Some trait correlations were predicted to break down in the future (SLA:NCONC, δ13C:PRI, and NCONC:WD).Synthesis: Our results suggest that traits have contrasting genotypic patterns and will be subjected to different climate selection pressures, which may lower the working optimum for functional traits. Further, traits are independently associated with different climate factors, indicating that some trait correlations may be disrupted in the future. Genetic constraints and trait correlations may limit the ability for functional traits to adapt to climate change.

Increasing phenological asynchrony between spring green-up and arrival of migratory birds.

Consistent with a warming climate, birds are shifting the timing of their migrations, but it remains unclear to what extent these shifts have kept pace with the changing environment. Because bird migration is primarily cued by annually consistent physiological responses to photoperiod, but conditions at their breeding grounds depend on annually variable climate, bird arrival and climate-driven spring events would diverge. We combined satellite and citizen science data to estimate rates of change in phenological interval between spring green-up and migratory arrival for 48 breeding passerine species across North America. Both arrival and green-up changed over time, usually in the same direction (earlier or later). Although birds adjusted their arrival dates, 9 of 48 species did not keep pace with rapidly changing green-up and across all species the interval between arrival and green-up increased by over half a day per year. As green-up became earlier in the east, arrival of eastern breeding species increasingly lagged behind green-up, whereas in the west-where green-up typically became later-birds arrived increasingly earlier relative to green-up. Our results highlight that phenologies of species and trophic levels can shift at different rates, potentially leading to phenological mismatches with negative fitness consequences.

Contemporary Remotely Sensed Data Products Refine Invasive Plants Risk Mapping in Data Poor Regions.

Invasive weeds are a serious problem worldwide, threatening biodiversity and damaging economies. Modeling potential distributions of invasive weeds can prioritize locations for monitoring and control efforts, increasing management efficiency. Forecasts of invasion risk at regional to continental scales are enabled by readily available downscaled climate surfaces together with an increasing number of digitized and georeferenced species occurrence records and species distribution modeling techniques. However, predictions at a finer scale and in landscapes with less topographic variation may require predictors that capture biotic processes and local abiotic conditions. Contemporary remote sensing (RS) data can enhance predictions by providing a range of spatial environmental data products at fine scale beyond climatic variables only. In this study, we used the Global Biodiversity Information Facility (GBIF) and empirical maximum entropy (MaxEnt) models to model the potential distributions of 14 invasive plant species across Southeast Asia (SEA), selected from regional and Vietnam's lists of priority weeds. Spatial environmental variables used to map invasion risk included bioclimatic layers and recent representations of global land cover, vegetation productivity (GPP), and soil properties developed from Earth observation data. Results showed that combining climate and RS data reduced predicted areas of suitable habitat compared with models using climate or RS data only, with no loss in model accuracy. However, contributions of RS variables were relatively limited, in part due to uncertainties in the land cover data. We strongly encourage greater adoption of quantitative remotely sensed estimates of ecosystem structure and function for habitat suitability modeling. Through comprehensive maps of overall predicted area and diversity of invasive species, we found that among lifeforms (herb, shrub, and vine), shrub species have higher potential invasion risk in SEA. Native invasive species, which are often overlooked in weed risk assessment, may be as serious a problem as non-native invasive species. Awareness of invasive weeds and their environmental impacts is still nascent in SEA and information is scarce. Freely available global spatial datasets, not least those provided by Earth observation programs, and the results of studies such as this one provide critical information that enables strategic management of environmental threats such as invasive species.

Spatial Configuration of Drought Disturbance and Forest Gap Creation across Environmental Gradients.

Climate change is increasing the risk of drought to forested ecosystems. Although drought impacts are often anecdotally noted to occur in discrete patches of high canopy mortality, the landscape effects of drought disturbances have received virtually no study. This study characterized the landscape configuration of drought impact patches and investigated the relationships between patch characteristics, as indicators of drought impact intensity, and environmental gradients related to water availability to determine factors influencing drought vulnerability. Drought impact patches were delineated from aerial surveys following an extreme drought in 2011 in southwestern Australia, which led to patchy canopy dieback of the Northern Jarrah Forest, a Mediterranean forest ecosystem. On average, forest gaps produced by drought-induced dieback were moderate in size (6.6 ± 9.7 ha, max = 85.7 ha), compact in shape, and relatively isolated from each other at the scale of several kilometers. However, there was considerable spatial variation in the size, shape, and clustering of forest gaps. Drought impact patches were larger and more densely clustered in xeric areas, with significant relationships observed with topographic wetness index, meteorological variables, and stand height. Drought impact patch clustering was more strongly associated with the environmental factors assessed (R2 = 0.32) than was patch size (R2 = 0.21); variation in patch shape remained largely unexplained (R2 = 0.02). There is evidence that the xeric areas with more intense drought impacts are 'chronic disturbance patches' susceptible to recurrent drought disturbance. The spatial configuration of drought disturbances is likely to influence ecological processes including forest recovery and interacting disturbances such as fire. Regime shifts to an alternate, non-forested ecosystem may occur preferentially in areas with large or clustered drought impact patches. Improved understanding of drought impacts and their patterning in space and time will expand our knowledge of forest ecosystems and landscape processes, informing management of these dynamic systems in an uncertain future.

Ecosystem classifications based on summer and winter conditions.

Ecosystem classifications map an area into relatively homogenous units for environmental research, monitoring, and management. However, their effectiveness is rarely tested. Here, three classifications are (1) defined and characterized for Canada along summertime productivity (moderate-resolution imaging spectrometer fraction of absorbed photosynthetically active radiation) and wintertime snow conditions (special sensor microwave/imager snow water equivalent), independently and in combination, and (2) comparatively evaluated to determine the ability of each classification to represent the spatial and environmental patterns of alternative schemes, including the Canadian ecozone framework. All classifications depicted similar patterns across Canada, but detailed class distributions differed. Class spatial characteristics varied with environmental conditions within classifications, but were comparable between classifications. There was moderate correspondence between classifications. The strongest association was between productivity classes and ecozones. The classification along both productivity and snow balanced these two sets of variables, yielding intermediate levels of association in all pairwise comparisons. Despite relatively low spatial agreement between classifications, they successfully captured patterns of the environmental conditions underlying alternate schemes (e.g., snow classes explained variation in productivity and vice versa). The performance of ecosystem classifications and the relevance of their input variables depend on the environmental patterns and processes used for applications and evaluation. Productivity or snow regimes, as constructed here, may be desirable when summarizing patterns controlled by summer- or wintertime conditions, respectively, or of climate change responses. General purpose ecosystem classifications should include both sets of drivers. Classifications should be carefully, quantitatively, and comparatively evaluated relative to a particular application prior to their implementation as monitoring and assessment frameworks.

The effects of temporally variable dispersal and landscape structure on invasive species spread.

Many invasive species are too widespread to realistically eradicate. For such species, a viable management strategy is to slow the rate of spread. However, to be effective, this will require detailed spread data and an understanding of the influence of environmental conditions and landscape structure on invasion rates. We used a time series of remotely sensed distribution maps and a spatial simulation model to study spread of the invasive Lepidium latifolium (perennial pepperweed) in California's Sacramento-San Joaquin River Delta. L. latifolium is a noxious weed and exhibited rapid, explosive spread. Annual infested area and empirical dispersal kernels were derived from the remotely sensed distributions in order to assess the influence of weather conditions on spread and to parameterize the simulation model. Spread rates and dispersal distances were highest for nascent infestations and in years with wet springs. Simulations revealed that spread rates were more strongly influenced by the length of long-distance dispersal than by temporal variation in its likelihood. It is thus important to capture long-distance dispersal and the conditions that facilitate spread when collecting data to parameterize spread models. Additionally, management actions performed in high-spread years, targeting long-distance recruits, can effectively contain infestations. Corridors were relatively unimportant to spread rates; their effectiveness at enhancing rate of spread was limited by the species' dispersal ability and the time needed to travel through the corridor. In contrast, habitat abundance and shape surrounding the introduction site strongly influenced invasion dynamics. Satellite patches invading large areas of invasible habitat present especially high risk.

RNase E levels in Escherichia coli are controlled by a complex regulatory system that involves transcription of the rne gene from three promoters.

The rne gene of Escherichia coli encodes RNase E, an essential endoribonuclease that is involved in both mRNA decay and rRNA processing. Here we present evidence that the gene is transcribed from three promoters: p1, p2 and p3. The p2 and p3 promoters map 34 and 145 nt upstream from the previously characterized rne promoter, p1, generating unusually long 5' UTRs of 395 and 506 nt respectively. Based on promoter-lacZ transcriptional fusions, p1 is a more efficient promoter than either p2 or p3. Low copy number or single copy number vectors carrying rne transcribed from either p1, p2 or p3 alone complement the rne 1018::bla deletion mutation at 30 degrees C, 37 degrees C and 44 degrees C. However, normal autoregulation requires the presence of all three promoters. A comparison among intracellular levels of RNase E, the half-lives of the rpsO, rpsT and rne mRNAs, and growth rates, indicates that the cell contains a considerable excess of RNase E protein. In addition, when the rne transcript is stabilized at low RNase E levels, it is not efficiently translated.