Temporal and spatial variability in plant pathogens refers to the dynamic changes in the distribution, abundance, and activity of plant pathogens over time and across different geographical locations. This variability plays a crucial role in understanding disease dynamics, predicting disease outbreaks, and implementing effective management strategies in agricultural systems. Here's a detailed description:
Temporal Variability:**
**Seasonal Dynamics:** Plant pathogens often exhibit seasonal fluctuations in their populations due to changes in environmental conditions such as temperature, humidity, and rainfall. For example, some pathogens thrive in warm and humid conditions, leading to increased disease incidence during certain seasons.
**Life Cycle of Pathogens:** The life cycle of a pathogen influences its temporal variability. Some pathogens have short life cycles, rapidly reproducing and spreading during favorable conditions. Others may have dormant stages, surviving adverse conditions until favorable conditions return.
**Epidemic Cycles:** Plant diseases can show epidemic cycles, with periods of rapid disease spread followed by periods of decline. Factors such as host susceptibility, pathogen virulence, and environmental conditions contribute to the timing and severity of disease outbreaks.
**Long-Term Trends:** Changes in climate patterns and human activities can lead to long-term trends in disease occurrence. For instance, global warming may alter the geographic range of pathogens, affecting their temporal patterns of activity.
**Management Implications:** Understanding the temporal variability of plant pathogens helps in timing disease control measures such as fungicide applications, planting disease-resistant cultivars, and adjusting cropping schedules to avoid peak disease periods.
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**Spatial Variability:**
**Geographic Distribution:** Plant pathogens may exhibit different levels of prevalence and severity across geographical regions. This variation is influenced by factors such as climate, soil type, topography, and host plant diversity.
**Localized Hotspots:** Within a field or orchard, there can be localized hotspots of disease where pathogen populations are particularly high. These hotspots can be influenced by factors like soil moisture, nutrient availability, and previous cropping history.
**Disease Gradient:** The severity of disease often decreases with distance from the infection source. This creates a disease gradient, where plants closest to the infection source are most affected, and disease severity decreases farther away.
**Vector Movement:** Some plant pathogens rely on vectors, such as insects or wind, for dispersal. This leads to spatial patterns of disease that follow the movement of these vectors.
**Management Implications:** Mapping the spatial distribution of plant pathogens helps in targeted disease management. This includes precision application of control measures, zoning for quarantine or er
A Business-Centric Approach to Design System Strategy
TEMPORAL AND SPATIAL VARIABILITY IN PLANT PATHOENS.pptx
1. TEMPORAL AND SPATIAL VARIABILITY IN
PLANT PATHOGENS
SUNIL SURIYA M
M. Sc., (Ag.) Plant Pathology
Annamalai University
2. OVERVIEW
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• IN TR OD U C TION
• C ON C EPTU A L STIMU LU S -
R ESPON SE
• U N IQU E SPEC TR A L SIGN ATU R E
• D ETU C TION OF D ISEA SED A R EA
• PATH OGEN - SPEC IFIC TEMPOR A L
AND SPATIAL
3. 3
INTRODUCTION
• Plant disease risk varies not only temporally, but also spatially. Adding
the spatial component to disease risk detection and disease risk
assessment will help farmers, researchers, and policy decision makers
make informed, science-based decisions.
• By integrating GPS, GIS, and remote sensing technologies (especially
satellite remote sensing platforms), new, quantitative information
concerning disease risk can now be obtained.
• The temporal and spatial dynamics of plant pathogens can be
quantified by visually assessing disease intensity.
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4. 4
• Remote sensing can be defined as the acquisition of data from an object using a sensor
that is not in direct contact with the object of interest (Nutter 1990).
• A GIS is a computer (hardware and software) system that captures, stores, manages,
queries, analyzes, and displays geographically-referenced (or geospatially-referenced) data
(Wang 2006).
• Data is often geospatially-referenced using a GPS that provides users with accurate
positioning, navigation, and timing services (Burrough 1986, Chang et al. 2007)
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5. 5
TESTING CONCEPTUAL STIMULUS -RESPONSE
RELATIONSHIPS USING GPS, GIS, AND REMOTE SENSING
• One of the primary advantages in coupling GPS, GIS, and remote
sensing technologies with geospatially-referenced data is that GIS maps
can be produced for each variable.
• Maps can then be rectified and overlaid upon each other to visually
assess which variables are likely to have associations with response
variables
• For example, a new, large-scale pathogen dissemination mechanism was
found to play a critical role in the prevalence of Moko disease of banana
(caused by Ralstonia solanacearum) in the Amazon River Basin.
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8. THE ‘UNIQUE SPECTRAL SIGNATURE’ PARADIGM
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• Scientists have long hypothesized that for every new sensor developed (multispectral,
hyperspectral, etc.) and every new platform (hand-held aerial satellite), specific biotic or abiotic
stresses must elicit unique spectral signatures or spectral indices or ratios that can be used to
discriminate among specific biotic and abiotic stress agents.
• Although this approach has been tried for many decades
(without much success), and researchers continue to search
for the silver bullet of pathogen specific spectral signatures.
• This paradigm has met with less than satisfactory results.
Most such investigations have used types of correlation
analyses to look for unique pathogen-specific spectral
signatures, and incorporated the most promising spectral
indices/ratios with discriminant analyses
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9. USE OF SATELLITE IMAGERY TO DETECT AND QUANTIFY
HEALTHY GREEN LEAF AREA GRADIENTS (1 -Y) VERSUS
DISEASE GRADIENTS (Y)
• Disease gradients are the result of two biological processes: pathogen dissemination and pathogen
infection
• The process of dissemination can be broken down into three sub-processes: (I) removal/escape of
dispersal units from a source of inoculum, (II) transport (dispersal) of dispersal units from a
source of inoculum to distance (x), and (III) the deposition of dispersal units onto a susceptible
host
• dispersal unit is defined as any device for the spread and/or the survival of a pathogen that can be
visually recognized and counted
• Dispersal units may be pathogen (spores, cells, sclerotia, etc.) and/or potential inoculum carriers
(insect vectors, pollen, infected/infested seed, cultivation, planting equipment, infested soil, pots,
etc.).
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PLANT
PATHOLOGY
11. PATHOGEN-SPECIFIC TEMPORAL AND SPATIAL
SIGNATURES – A NEW PARADIGM
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• Plant pathogens can create HGLA gradients by differentially removing healthy green leaf area with
respect to distance from a source of inoculum
• Based upon this concept, we have advanced a new paradigm that quantifies the removal of HGLA
within a plant canopy over time and space as a means to extract unique, pathogen-specific,
spatiotemporal signatures.
• Some plant pathogens are r-strategists and produce tremendous numbers of wind-dispersed spores,
resulting in large dispersal, deposition, infection, disease, and HGLA gradients.
• Smaller dispersal units, such as rust spores, will result in shallower HGLA gradients compared to
HGLA gradients caused by largerspored pathogens (thereby resulting in unique HGLA gradients).
Fungal pathogens that are k-strategists produce fewer dispersal units per infection and will have a
slower rate of focal expansion than r-strategists.
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12. • A s plant pat ho g ens sp rea d ov er t ime a nd spa ce wit hin a crop ca no py,
HGLA is remov ed; it is o ur hy pot hesis t ha t t he result ing t empo ra l a nd
spa t ia l pa t t erns a re unique t o specif ic pla nt pa t ho g ens .
• The tempo ral a nd spat ia l spread of asian soy bea n rust (ASR) wa s
qua nt if ied f o r a n inf ect ed so y bea n f ield lo ca t ed in ceda ra , so ut h a f rica .
• Sat ellit e imag ery (IKONOS) wit h 1 m2 per pix el resolut ion wa s o bta ined
f o r 6 a nd 11 a pril in 2 0 0 6 .
• Ima g e int ensit ies in t h e near-inf ra red band ( reco rded as g raysca le va lues
ra ng ing fro m 0 to 25 5) were ex tra cted and g eo spatia lly - referenced using
IM AGIN E ( ER D A S, inc . , Atla nta , GA ) a nd arcg is sof twa re ( ESR I,
redla nds , C A ) .
• A pot ent ia l pat hogen -specif ic spat ia l sig nat ure might b e dev elo ped by
quantify ing the chang e in disea se int ensity ( Y) wit h respect t o dist ance
fro m a so urce of ino culum. Fo ur disea se g ra dient mo dels ha v e been
pro po sed t o qua nt if y disea se g ra dient s .
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DETECTING AND QUANTIFYING HEALTHY GREEN LEAF AREA
(1-Y) GRADIENTS
PLANT
PATHOLOGY
13. IMPLICATIONS FOR PLANT PATHOGEN FORENSICS
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• The ability to accurately detect and geospatially-reference the exact GPS locations of the epicenters
of disease foci has important implications with regards to pathogen forensics (Fletcher et al. 2006,
Nutter 2005), given the potential threats associated with the deliberate introduction of plant
pathogens (Nutter and Madden 2008).
• After the GPS coordinates of the epicenters of primary
disease foci have been determined (using integrated
remote sensing, GPS, and GIS technologies), this
information can be passed immediately to law
enforcement personnel on the ground to direct forensic
teams where to best search for physical evidence (such as
the presence of chemical surfactants (Tween 20), culture
media residue or gelatin used as sticking agents for spore
deposition, spray bottles, syringes, and other pathogen
delivery tools).
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14. CONCLUSION
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• The integration and use of GPS, GIS, and remote sensing technologies has tremendous potential to
obtain temporal and spatial information concerning disease risk at multiple spatial scales.
• Moreover, integrated GPS, GIS, and remote sensing technologies using aerial and satellite platforms
have cutting-edge applications to obtain science-based, pathogen-specific temporal and spatial
‘signatures’ that can be used to correctly identify the cause(s) of crop stress.
• Exciting opportunities are on the horizon using GPS,
GIS, and remote sensing technologies to develop new
metrics for evaluating and monitoring IPM
performance.
• . Finally, imagery provides a permanent record that can
be stored and re-analyzed as GPS and GIS
technologies advance in the future.
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