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1.
Phytopathology ; : PHYTO08190294R, 2020 Feb 11.
Artigo em Inglês | MEDLINE | ID: mdl-31880984

RESUMO

Populations of Phytophthora infestans, the oomycete causal agent of potato late blight in the United States, are predominantly asexual, and isolates are characterized by clonal lineage or asexual descendants of a single genotype. Current tools for clonal lineage identification are time consuming and require laboratory equipment. We previously found that foliar spectroscopy can be used for high-accuracy pre- and postsymptomatic detection of P. infestans infections caused by clonal lineages US-08 and US-23. In this work, we found subtle but distinct differences in spectral responses of potato foliage infected by these clonal lineages in both growth-chamber time-course experiments (12- to 24-h intervals over 5 days) and naturally infected samples from commercial production fields. In both settings, we measured continuous visible to shortwave infrared reflectance (400 to 2,500 nm) on leaves using a portable spectrometer with contact probe. We consistently discriminated between infections caused by the two clonal lineages across all stages of disease progression using partial least squares (PLS) discriminant analysis, with total accuracies ranging from 88 to 98%. Three-class random forest differentiation between control, US-08, and US-23 yielded total discrimination accuracy ranging from 68 to 76%. Differences were greatest during presymptomatic infection stages and progressed toward uniformity as symptoms advanced. Using PLS-regression trait models, we found that total phenolics, sugar, and leaf mass per area were different between lineages. Shortwave infrared wavelengths (>1,100 nm) were important for clonal lineage differentiation. This work provides a foundation for future use of hyperspectral sensing as a nondestructive tool for pathovar differentiation.

2.
New Phytol ; 224(4): 1557-1568, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31418863

RESUMO

Leaf mass per area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long-standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth's biomes, an efficient, globally generalizable approach to predict LMA is still lacking. We explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 to 393 g m-2 . Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad- and needleleaf species, and upper- and lower-canopy (i.e. sun and shade) growth environments. Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age and canopy position from diverse biomes. Our model captures LMA variability with high accuracy and low error (R2  = 0.89; root mean square error (RMSE) = 15.45 g m-2 ). Our finding highlights the fact that the leaf economics spectrum is mirrored by the leaf optical spectrum, paving the way for this technology to predict the diversity of LMA in ecosystems across global biomes.

3.
Plant Methods ; 15: 6, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30705688

RESUMO

Background: Remote monitoring of plants using hyperspectral imaging has become an important tool for the study of plant growth, development, and physiology. Many applications are oriented towards use in field environments to enable non-destructive analysis of crop responses due to factors such as drought, nutrient deficiency, and disease, e.g., using tram, drone, or airplane mounted instruments. The field setting introduces a wide range of uncontrolled environmental variables that make validation and interpretation of spectral responses challenging, and as such lab- and greenhouse-deployed systems for plant studies and phenotyping are of increasing interest. In this study, we have designed and developed an open-source, hyperspectral reflectance-based imaging system for lab-based plant experiments: the HyperScanner. The reliability and accuracy of HyperScanner were validated using drought and salt stress experiments with Arabidopsis thaliana. Results: A robust, scalable, and reliable system was created. The system was built using open-sourced parts, and all custom parts, operational methods, and data have been made publicly available in order to maintain the open-source aim of HyperScanner. The gathered reflectance images showed changes in narrowband red and infrared reflectance spectra for each of the stress tests that was evident prior to other visual physiological responses and exhibited congruence with measurements using full-range contact spectrometers. Conclusions: HyperScanner offers the potential for reliable and inexpensive laboratory hyperspectral imaging systems. HyperScanner was able to quickly collect accurate reflectance curves on a variety of plant stress experiments. The resulting images showed spectral differences in plants shortly after application of a treatment but before visual manifestation. HyperScanner increases the capacity for spectroscopic and imaging-based analytical tools by providing more access to hyperspectral analyses in the laboratory setting.

4.
Sensors (Basel) ; 19(3)2019 Jan 23.
Artigo em Inglês | MEDLINE | ID: mdl-30678031

RESUMO

There is an increasing interest in using hyperspectral data for phenotyping and crop management while overcoming the challenge of changing atmospheric conditions. The Piccolo dual field-of-view system collects up- and downwelling radiation nearly simultaneously with one spectrometer. Such systems offer great promise for crop monitoring under highly variable atmospheric conditions. Here, the system's utility from a tractor-mounted boom was demonstrated for a case study of estimating soybean plant populations in early vegetative stages. The Piccolo system is described and its performance under changing sky conditions are assessed for two replicates of the same experiment. Plant population assessment was estimated by partial least squares regression (PLSR) resulting in stable estimations by models calibrated and validated under sunny and cloudy or cloudy and sunny conditions, respectively. We conclude that the Piccolo system is effective for data collection under variable atmospheric conditions, and we show its feasibility of operation for precision agriculture research and potential commercial applications.


Assuntos
Soja/metabolismo , Análise dos Mínimos Quadrados , Soja/genética
5.
Ecol Appl ; 29(2): e01849, 2019 03.
Artigo em Inglês | MEDLINE | ID: mdl-30656779

RESUMO

Measurement or observation error is common in ecological data: as citizen scientists and automated algorithms play larger roles processing growing volumes of data to address problems at large scales, concerns about data quality and strategies for improving it have received greater focus. However, practical guidance pertaining to fundamental data quality questions for data users or managers-how accurate do data need to be and what is the best or most efficient way to improve it?-remains limited. We present a generalizable framework for evaluating data quality and identifying remediation practices, and demonstrate the framework using trail camera images classified using crowdsourcing to determine acceptable rates of misclassification and identify optimal remediation strategies for analysis using occupancy models. We used expert validation to estimate baseline classification accuracy and simulation to determine the sensitivity of two occupancy estimators (standard and false-positive extensions) to different empirical misclassification rates. We used regression techniques to identify important predictors of misclassification and prioritize remediation strategies. More than 93% of images were accurately classified, but simulation results suggested that most species were not identified accurately enough to permit distribution estimation at our predefined threshold for accuracy (<5% absolute bias). A model developed to screen incorrect classifications predicted misclassified images with >97% accuracy: enough to meet our accuracy threshold. Occupancy models that accounted for false-positive error provided even more accurate inference even at high rates of misclassification (30%). As simulation suggested occupancy models were less sensitive to additional false-negative error, screening models or fitting occupancy models accounting for false-positive error emerged as efficient data remediation solutions. Combining simulation-based sensitivity analysis with empirical estimation of baseline error and its variability allows users and managers of potentially error-prone data to identify and fix problematic data more efficiently. It may be particularly helpful for "big data" efforts dependent upon citizen scientists or automated classification algorithms with many downstream users, but given the ubiquity of observation or measurement error, even conventional studies may benefit from focusing more attention upon data quality.


Assuntos
Confiabilidade dos Dados , Ecologia , Algoritmos
6.
Ecol Lett ; 22(3): 506-517, 2019 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-30609108

RESUMO

Earth system models (ESMs) use photosynthetic capacity, indexed by the maximum Rubisco carboxylation rate (Vcmax ), to simulate carbon assimilation and typically rely on empirical estimates, including an assumed dependence on leaf nitrogen determined from soil fertility. In contrast, new theory, based on biochemical coordination and co-optimization of carboxylation and water costs for photosynthesis, suggests that optimal Vcmax can be predicted from climate alone, irrespective of soil fertility. Here, we develop this theory and find it captures 64% of observed variability in a global, field-measured Vcmax dataset for C3 plants. Soil fertility indices explained substantially less variation (32%). These results indicate that environmentally regulated biophysical constraints and light availability are the first-order drivers of global photosynthetic capacity. Through acclimation and adaptation, plants efficiently utilize resources at the leaf level, thus maximizing potential resource use for growth and reproduction. Our theory offers a robust strategy for dynamically predicting photosynthetic capacity in ESMs.


Assuntos
Aclimatação , Dióxido de Carbono , Fotossíntese , Adaptação Fisiológica , Nitrogênio , Folhas de Planta , Ribulose-Bifosfato Carboxilase
7.
Plant Cell Environ ; 42(2): 633-646, 2019 02.
Artigo em Inglês | MEDLINE | ID: mdl-30474119

RESUMO

Conifers possess chemical and anatomical defences against tree-killing bark beetles that feed in their phloem. Resins accumulating at attack sites can delay and entomb beetles while toxins reach lethal levels. Trees with high concentrations of metabolites active against bark beetle-microbial complexes, and more extensive resin ducts, achieve greater survival. It is unknown if and how conifers integrate chemical and anatomical components of defence or how these capabilities vary with historical exposure. We compared linkages between phloem chemistry and tree ring anatomy of two mountain pine beetle hosts. Lodgepole pine, a mid-elevation species, has had extensive, continual contact with this herbivore, whereas high-elevation whitebark pines have historically had intermittent exposure that is increasing with warming climate. Lodgepole pine had more and larger resin ducts. In both species, anatomical defences were positively related to tree growth and nutrients. Within-tree constitutive and induced concentrations of compounds bioactive against bark beetles and symbionts were largely unrelated to resin duct abundance and size. Fewer anatomical defences in the semi-naïve compared with the continually exposed host concurs with directional differences in chemical defences. Partially uncoupling chemical and morphological antiherbivore traits may enable trees to confront beetles with more diverse defence permutations that interact to resist attack.


Assuntos
Herbivoria , Pinus/fisiologia , Resinas Vegetais/metabolismo , Árvores/fisiologia , Gorgulhos , Animais , Floema/metabolismo , Pinus/metabolismo , Casca de Planta , Árvores/metabolismo
8.
Glob Chang Biol ; 24(11): 5500-5517, 2018 11.
Artigo em Inglês | MEDLINE | ID: mdl-30003643

RESUMO

American chestnut (Castanea dentata) was once an important component forests in the central Appalachians (USA), but it was functionally extirpated nearly a century ago. Attempts are underway to reintroduce blight-resistant chestnut to its former range, but it is uncertain how current forest composition, climate, and atmospheric changes and disturbance regimes will interact to determine future forest dynamics and ecosystem services. The combination of novel environmental conditions (e.g. climate change), a reintroduced tree species and new disturbance regimes (e.g. exotic insect pests, fire suppression) have no analog in the past that can be used to parameterize phenomenological models. We therefore used a mechanistic approach within the LANDIS-II forest landscape model that relies on physiological first principles to project forest dynamics as the outcome of competition of tree cohorts for light and water as a function of temperature, precipitation, CO2 concentration, and life history traits. We conducted a factorial landscape simulation experiment to evaluate specific hypotheses about future forest dynamics in two study sites in the center of the former range of chestnut. Our results supported the hypotheses that climate change would favor chestnut because of its optimal temperature range and relative drought resistance, and that chestnut would be less competitive in the more mesic Appalachian Plateau province because competitors will be less stressed. The hypothesis that chestnut will increase carbon stocks was supported, although the increase was modest. Our results confirm that aggressive restoration is needed regardless of climate and soils, and that increased aggressiveness of chestnut restoration increased biomass accumulation. The hypothesis that chestnut restoration will increase both compositional and structural richness was not supported because chestnut displaced some species and age cohorts. Although chestnut restoration did not markedly enhance carbon stocks, our findings provide hope that this formerly important species can be successfully reintroduced and associated ecosystem services recovered.


Assuntos
Sequestro de Carbono , Mudança Climática , Conservação dos Recursos Naturais/métodos , Fagaceae/fisiologia , Árvores/fisiologia , Fagaceae/crescimento & desenvolvimento , Maryland , Árvores/crescimento & desenvolvimento
9.
Nat Ecol Evol ; 2(6): 976-982, 2018 06.
Artigo em Inglês | MEDLINE | ID: mdl-29760440

RESUMO

Biodiversity promotes ecosystem function as a consequence of functional differences among organisms that enable resource partitioning and facilitation. As the need for biodiversity assessments increases in the face of accelerated global change, novel approaches that are rapid, repeatable and scalable are critical, especially in ecosystems for which information about species identity and the number of species is difficult to acquire. Here, we present 'spectral diversity'-a spectroscopic index of the variability of electromagnetic radiation reflected from plants measured in the visible, near-infrared and short-wave infrared regions (400-2,400 nm). Using data collected from the Cedar Creek biodiversity experiment (Minnesota, USA), we provide evidence that the dissimilarity of species' leaf spectra increases with functional dissimilarity and evolutionary divergence time. Spectral diversity at the leaf level explains 51% of total variation in productivity-a proportion comparable to taxonomic (47%), functional (51%) or phylogenetic diversity (48%)-and performs similarly when calculated from high-resolution canopy image spectra. Spectral diversity is an emerging dimension of plant biodiversity that integrates trait variation within and across species even in the absence of taxonomic, functional, phylogenetic or abundance information, and has the potential to transform biodiversity assessment because of its scalability to remote sensing.


Assuntos
Biodiversidade , Filogenia , Folhas de Planta/fisiologia , Fenômenos Fisiológicos Vegetais , Ecossistema , Minnesota , Análise Espectral
10.
Ecol Appl ; 28(5): 1313-1324, 2018 07.
Artigo em Inglês | MEDLINE | ID: mdl-29694698

RESUMO

A central challenge to understanding how climate anomalies, such as drought and heatwaves, impact the terrestrial carbon cycle, is quantification and scaling of spatial and temporal variation in ecosystem gross primary productivity (GPP). Existing empirical and model-based satellite broadband spectra-based products have been shown to miss critical variation in GPP. Here, we evaluate the potential of high spectral resolution (10 nm) shortwave (400-2,500 nm) imagery to better detect spatial and temporal variations in GPP across a range of ecosystems, including forests, grassland-savannas, wetlands, and shrublands in a water-stressed region. Estimates of GPP from eddy covariance observations were compared against airborne hyperspectral imagery, collected across California during the 2013-2014 HyspIRI airborne preparatory campaign. Observations from 19 flux towers across 23 flight campaigns (102 total image-flux tower pairs) showed GPP to be strongly correlated to a suite of spectral wavelengths and band ratios associated with foliar physiology and chemistry. A partial least squares regression (PLSR) modeling approach was then used to predict GPP with higher validation accuracy (adjusted R2  = 0.71) and low bias (0.04) compared to existing broadband approaches (e.g., adjusted R2  = 0.68 and bias = -5.71 with the Sims et al. model). Significant wavelengths contributing to the PLSR include those previously shown to coincide with Rubisco (wavelengths 1,680, 1,740, and 2,290 nm) and Vcmax (wavelengths 1,680, 1,722, 1,732, 1,760, and 2,300 nm). These results provide strong evidence that advances in satellite spectral resolution offer significant promise for improved satellite-based monitoring of GPP variability across a diverse range of terrestrial ecosystems.


Assuntos
Secas , Ecossistema , Tecnologia de Sensoriamento Remoto/métodos , Análise Espectral/métodos , California , Florestas , Pradaria , Áreas Alagadas
11.
Environ Sci Pollut Res Int ; 25(9): 8249-8267, 2018 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-28699011

RESUMO

The Mediterranean basin can be considered a hot spot not only in terms of climate change (CC) but also for air quality. Assessing the impact of CC and air pollution on ecosystem functions is a challenging task, and adequate monitoring techniques are needed. This paper summarizes the present knowledge on the use of reflectance spectroscopy for the evaluation of the effects of air pollution on plants. First, the history of this technique is outlined. Next, we describe the vegetation reflectance spectrum, how it can be scaled from leaf to landscape levels, what information it contains, and how it can be exploited to understand plant and ecosystem functions. Finally, we review the literature concerning this topic, with special attention to Mediterranean air pollutants, showing the increasing interest in this technique. The ability of spectroscopy to detect the influence of air pollution on plant function of all major and minor Mediterranean pollutants has been evaluated, and ozone and its interaction with other gases (carbon dioxide, nitrogen oxides, and sulfur dioxide) have been the most studied. In the recent years, novel air pollutants, such as particulate matter, nitrogen deposition, and heavy metals, have drawn attention. Although various vegetation types have been studied, few of these species are representative of the Mediterranean environment. Thus, major emphasis should be placed on using vegetation spectroscopy for better understanding and monitoring the impact of air pollution on Mediterranean plants in the CC era.


Assuntos
Poluição do Ar/análise , Metais Pesados/química , Óxidos de Nitrogênio/análise , Nitrogênio/química , Ozônio/análise , Material Particulado/análise , Dióxido de Enxofre/análise , Mudança Climática , Ecossistema , Óxidos de Nitrogênio/química , Ozônio/química , Material Particulado/química , Fenômenos Fisiológicos Vegetais , Plantas , Análise Espectral , Dióxido de Enxofre/química
12.
Ecol Appl ; 28(2): 541-556, 2018 03.
Artigo em Inglês | MEDLINE | ID: mdl-29266500

RESUMO

Remote sensing has been used to detect plant biodiversity in a range of ecosystems based on the varying spectral properties of different species or functional groups. However, the most appropriate spatial resolution necessary to detect diversity remains unclear. At coarse resolution, differences among spectral patterns may be too weak to detect. In contrast, at fine resolution, redundant information may be introduced. To explore the effect of spatial resolution, we studied the scale dependence of spectral diversity in a prairie ecosystem experiment at Cedar Creek Ecosystem Science Reserve, Minnesota, USA. Our study involved a scaling exercise comparing synthetic pixels resampled from high-resolution images within manipulated diversity treatments. Hyperspectral data were collected using several instruments on both ground and airborne platforms. We used the coefficient of variation (CV) of spectral reflectance in space as the indicator of spectral diversity and then compared CV at different scales ranging from 1 mm2 to 1 m2 to conventional biodiversity metrics, including species richness, Shannon's index, Simpson's index, phylogenetic species variation, and phylogenetic species evenness. In this study, higher species richness plots generally had higher CV. CV showed higher correlations with Shannon's index and Simpson's index than did species richness alone, indicating evenness contributed to the spectral diversity. Correlations with species richness and Simpson's index were generally higher than with phylogenetic species variation and evenness measured at comparable spatial scales, indicating weaker relationships between spectral diversity and phylogenetic diversity metrics than with species diversity metrics. High resolution imaging spectrometer data (1 mm2 pixels) showed the highest sensitivity to diversity level. With decreasing spatial resolution, the difference in CV between diversity levels decreased and greatly reduced the optical detectability of biodiversity. The optimal pixel size for distinguishing α diversity in these prairie plots appeared to be around 1 mm to 10 cm, a spatial scale similar to the size of an individual herbaceous plant. These results indicate a strong scale-dependence of the spectral diversity-biodiversity relationships, with spectral diversity best able to detect a combination of species richness and evenness, and more weakly detecting phylogenetic diversity. These findings can be used to guide airborne studies of biodiversity and develop more effective large-scale biodiversity sampling methods.


Assuntos
Biodiversidade , Pradaria , Tecnologia de Sensoriamento Remoto , Análise Espacial
13.
Tree Physiol ; 37(11): 1582-1591, 2017 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-29036552

RESUMO

Drought frequency is predicted to increase in future environments. Leaf water potential (ΨLW) is commonly used to evaluate plant water status, but traditional measurements can be logistically difficult and require destructive sampling. We used reflectance spectroscopy to characterize variation in ΨLW of Quercus oleoides Schltdl. & Cham. under differential water availability and tested the ability to predict pre-dawn ΨLW (PDΨLW) using spectral data collected hours after pressure chamber measurements on dark-acclimated leaves. ΨLW was measured with a Scholander pressure chamber. Leaf reflectance was collected at one or both of two time points: immediately (ΨLW) and ~5 h after pressure chamber measurements (PDΨLW). Predictive models were constructed using partial least-squares regression. Model performance was evaluated using coefficient of determination (R2), root-mean-square error (RMSE), bias, and the percent RMSE of the data range (%RMSE). ΨLW and PDΨLW were well predicted using spectroscopic models and successfully estimated a wide variation in ΨLW (light- or dark-acclimated leaves) as well as PDΨLW (dark-acclimated leaves only). Mean ΨLWR2, RMSE and bias values were 0.65, 0.51 MPa and 0.09, respectively, with a %RMSE between 8% and 20%, while mean PDΨLWR2, RMSE and bias values were 0.60, 0.44 MPa and 0.01, respectively, with a %RMSE between 9% and 20%. Estimates of PDΨLW produced similar statistical outcomes when analyzing treatment effects on PDΨLW as those found using reference pressure chamber measurements. These findings highlight a promising approach to evaluate plant responses to environmental change by providing rapid measurements that can be used to estimate plant water status as well as demonstrating that spectroscopic measurements can be used as a surrogate for standard, reference measurements in a statistical framework.


Assuntos
Botânica/métodos , Secas , Folhas de Planta/fisiologia , Quercus/fisiologia , Análise Espectral/métodos , Geografia , Honduras , Fisiologia/métodos
15.
Plant Cell Environ ; 40(11): 2743-2753, 2017 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-28755489

RESUMO

Anticipated consequences of climate change in temperate regions include early spring warmup punctuated by intermittent hard freezes. Warm weather accelerates leaf flush in perennial woody species, potentially exposing vulnerable young tissues to damaging frosts. We employed a 2 × 6 randomized factorial design to examine how the interplay of vernal (springtime) freeze damage and genetic variation in a hardwood species (Populus tremuloides) influences tree growth, phytochemistry, and interactions with an insect herbivore (Chaitophorus stevensis). Acute effects of freezing included defoliation and mortality. Surviving trees exhibited reduced growth and altered biomass distribution. Reflushed leaves on these trees had lower mass per area, lower lignin concentrations, and higher nitrogen concentrations, altered chemical defence profiles, and supported faster aphid population growth. Many effects varied among plant genotypes and were related with herbivore performance. This study suggests that a single damaging vernal freeze event can alter tree-insect interactions through effects on plant growth and chemistry. Differential responses of various genotypes to freeze damage suggest that more frequent vernal freeze events could also influence natural selection, favouring trees with greater freeze hardiness, and more resistance or tolerance to herbivores following damage.


Assuntos
Afídeos/fisiologia , Congelamento , Variação Genética , Populus/crescimento & desenvolvimento , Populus/genética , Árvores/crescimento & desenvolvimento , Árvores/genética , Análise de Variância , Animais , Biomassa , Genótipo , Análise dos Mínimos Quadrados , Populus/parasitologia , Árvores/parasitologia
16.
Plant Cell Environ ; 40(9): 1791-1806, 2017 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-28543133

RESUMO

Warming climate is allowing tree-killing bark beetles to expand their ranges and access naïve and semi-naïve conifers. Conifers respond to attack using complex mixtures of chemical defences that can impede beetle success, but beetles exploit some compounds for host location and communication. Outcomes of changing relationships will depend on concentrations and compositions of multiple host compounds, which are largely unknown. We analysed constitutive and induced chemistries of Dendroctonus ponderosae's primary historical host, Pinus contorta, and Pinus albicaulis, a high-elevation species whose encounters with this beetle are transitioning from intermittent to continuous. We quantified multiple classes of terpenes, phenolics, carbohydrates and minerals. Pinus contorta had higher constitutive allocation to, and generally stronger inducibility of, compounds that resist these beetle-fungal complexes. Pinus albicaulis contained higher proportions of specific monoterpenes that enhance pheromone communication, and lower induction of pheromone inhibitors. Induced P. contorta increased insecticidal and fungicidal compounds simultaneously, whereas P. albicaulis responses against these agents were inverse. Induced terpene accumulation was accompanied by decreased non-structural carbohydrates, primarily sugars, in P. contorta, but not P. albicaulis, which contained primarily starches. These results show some host species with continuous exposure to bark beetles have more thoroughly integrated defence syndromes than less-continuously exposed host species.


Assuntos
Besouros/fisiologia , Ecossistema , Pinus/parasitologia , Casca de Planta/parasitologia , Doenças das Plantas/parasitologia , Animais , Carboidratos/análise , Carbono/metabolismo , Besouros/microbiologia , Minerais/análise , Compostos Orgânicos/análise , Fenóis/análise , Floema/metabolismo , Análise de Componente Principal , Terpenos/análise
17.
Ecol Appl ; 26(8): 2598-2608, 2016 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-27875008

RESUMO

Increases in natural or noncrop habitat surrounding agricultural fields have been shown to be correlated with declines in insect crop pests. However, these patterns are highly variable across studies suggesting other important factors, such as abiotic drivers, which are rarely included in landscape models, may also contribute to variability in insect population abundance. The objective of this study was to explicitly account for the contribution of temperature and precipitation, in addition to landscape composition, on the abundance of a widespread insect crop pest, the soybean aphid (Aphis glycines Matsumura), in Wisconsin soybean fields. We hypothesized that higher soybean aphid abundance would be associated with higher heat accumulation (e.g., growing degree days) and increasing noncrop habitat in the surrounding landscape, due to the presence of the overwintering primary hosts of soybean aphid. To evaluate these hypotheses, we used an ecoinformatics approach that relied on a large dataset collected across Wisconsin over a 9-year period (2003-2011), for an average of 235 sites per year (n = 2,110 fields total). We determined surrounding landscape composition (1.5-km radius) using publicly available satellite-derived land cover imagery and interpolated daily temperature and precipitation information from the National Weather Service COOP weather station network. We constructed linear mixed models for soybean aphid abundance based on abiotic and landscape explanatory variables and applied model averaging for prediction using an information theoretic framework. Over this broad spatial and temporal extent in Wisconsin, we found that variation in growing season precipitation was positively related to soybean aphid abundance, while higher precipitation during the nongrowing season had a negative effect on aphid populations. Additionally, we found that aphid populations were higher in areas with proportionally more forest but were lower in areas where minor crops, such as small grains, were more prevalent. Thus, our findings support our hypothesis that including abiotic drivers increases our understanding of crop pest abundance and distribution. Moreover, by explicitly modeling abiotic factors, we may be able to explore how variable climate in tandem with land cover patterns may affect current and future insect populations, with potentially critical implications for crop yields and agricultural food webs.


Assuntos
Afídeos , Florestas , Agricultura , Animais , Ecossistema , Cadeia Alimentar , Wisconsin
18.
Ecology ; 97(11): 3019-3030, 2016 11.
Artigo em Inglês | MEDLINE | ID: mdl-27870035

RESUMO

Dams, levees, and water withdrawals disrupt hydrologic regimes and associated floodplain forests. Because these forests are also responding to changes in land use, species invasions, and climate change, the relative effects of these factors are hard to disentangle. Most studies of floodplain forests lack historic data, requiring us to rely on recent data or contemporary spatial relationships to these drivers to infer those causes of vegetation dynamics. Here, we use survey data from the 1950s to reconstruct plant community changes across 40 floodplain forests in Wisconsin. We applied two partial least squares regression (PLS) models to evaluate how current site and landscape scale conditions and changes in these conditions since the 1950s influence contemporary patterns of community diversity and composition. Local site variables were among the most important in explaining current composition metrics and their changes, but historic landscape variables and changes in these were also important. Current local diversity (α) was the highest at sites prone to frequent flooding, even at sites in fragmented landscapes. Sites along sinuous rivers in large watershed areas with more contiguous forest had the highest abundance of wetland indicator plants in the re-survey and had the largest increases in α diversity since the 1950s, despite having the highest presence of exotic species then. These same sites have converged in composition, reflecting increases in wetland indicator plants and common native species. These patterns of increasing α diversity coupled with declines in community distinctiveness are uncommon among long-term studies. Increases in wetland plants may indicate that sites have become wetter with hydrologic changes, but these increases may also reflect improved colonization and establishment processes involving a robust regional pool of generalist wetland taxa. Woody and exotic plants typical of upland forests increased at rarely flooded sites in fragmented and urbanizing landscapes, indicating shifts towards a later-successional conditions and a dampened disturbance regime. This has reduced local species diversity and increased regional distinctness at some sites. As hydrologic connections appear to best maintain native species diversity and composition, even in fragmented landscapes, managers should seek to recreate these whenever feasible.


Assuntos
Biodiversidade , Inundações , Florestas , Modelos Biológicos
19.
J Econ Entomol ; 109(3): 1177-1187, 2016 Apr 13.
Artigo em Inglês | MEDLINE | ID: mdl-27076674

RESUMO

The soybean aphid, Aphis glycines Matsumura, an exotic species in North America that has been detected in 21 U.S. states and Canada, is a major pest for soybean that can reduce maximum photosynthetic capacity and yields. Our existing knowledge is based on relatively few studies that do not span a wide variety of environmental conditions, and often focus on relatively high and damaging population pressure. We examined the effects of varied populations and duration of soybean aphids on soybean photosynthetic rates and yield in two experiments. In a 2011 field study, we found that plants with low cumulative aphid days (CAD, less than 2,300) had higher yields than plants not experiencing significant aphid pressure, suggesting a compensatory growth response to low aphid pressure. This response did not hold at higher CAD, and yields declined. In a 2013 controlled-environment greenhouse study, soybean plants were well-watered and fertilized with nitrogen (N), and aphid populations were manipulated to reach moderate to high levels (8,000-50,000 CAD). Plants tolerated these population levels when aphids were introduced during the vegetative or reproductive phenological stages of the plant, showing no significant reduction in yield. Leaf N concentration and CAD were positively and significantly correlated with increasing ambient photosynthetic rates. Our findings suggest that, given the right environmental conditions, modern soybean plants can withstand higher aphid pressure than previously assumed. Moreover, soybean plants also responded positively through a compensatory photosynthetic effect to moderate population pressure, contributing to stable or increased yield.

20.
Ecol Appl ; 24(7): 1651-69, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-29210229

RESUMO

The morphological and biochemical properties of plant canopies are strong predictors of photosynthetic capacity and nutrient cycling. Remote sensing research at the leaf and canopy scales has demonstrated the ability to characterize the biochemical status of vegetation canopies using reflectance spectroscopy, including at the leaf level and canopy level from air- and spaceborne imaging spectrometers. We developed a set of accurate and precise spectroscopic calibrations for the determination of leaf chemistry (contents of nitrogen, carbon, and fiber constituents), morphology (leaf mass per area, Marea), and isotopic composition (δ15N) of temperate and boreal tree species using spectra of dried and ground leaf material. The data set consisted of leaves from both broadleaf and needle-leaf conifer species and displayed a wide range in values, determined with standard analytical approaches: 0.7­4.4% for nitrogen (Nmass), 42­54% for carbon (Cmass), 17­58% for fiber (acid-digestible fiber, ADF), 7­44% for lignin (acid-digestible lignin, ADL), 3­31% for cellulose, 17­265 g/m2 for Marea, and −9.4‰ to 0.8‰ for δ15N. The calibrations were developed using a partial least-squares regression (PLSR) modeling approach combined with a novel uncertainty analysis. Our PLSR models yielded model calibration (independent validation shown in parentheses) R2 and the root mean square error (RMSE) values, respectively, of 0.98 (0.97) and 0.10% (0.13%) for Nmass, R2 = 0.77 (0.73) and RMSE = 0.88% (0.95%) for Cmass, R2 = 0.89 (0.84) and RMSE = 2.8% (3.4%) for ADF, R2 = 0.77 (0.69) and RMSE = 2.4% (3.9%) for ADL, R2 = 0.77 (0.72) and RMSE = 1.4% (1.9%) for leaf cellulose, R2 = 0.62 (0.60) and RMSE = 0.91‰ (1.5‰) for δ15N, and R2 = 0.88 (0.87) with RMSE = 17.2 g/m2 (22.8 g/m2) for Marea. This study demonstrates the potential for rapid and accurate estimation of key foliar traits of forest canopies that are important for ecological research and modeling activities, with a single calibration equation valid over a wide range of northern temperate and boreal species and leaf physiognomies. The results provide the basis to characterize important variability between and within species, and across ecological gradients using a rapid, cost-effective, easily replicated method.


Assuntos
Fotossíntese , Folhas de Planta , Análise Espectral , Florestas , Nitrogênio , Árvores
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