Dispersal of released mountain pine beetles under the canopy of a mature lodgepole pine stand

Safranyik, L.; Linton, D. A.; Silversides, R. H.; McMullen, L. H.

doi:10.1111/j.1439-0418.1992.tb00687.x

Cited by 102 publications

(105 citation statements)

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“…Safranyik et al (1992) conducted a detailed markrelease-recapture study within a mature lodgepole pine forest, recapturing an average of 25% of the beetles they released. Of these, they found that the number of beetles recaptured decreased exponentially with increasing distance from the release point; approximately 90% of beetles were captured within 30 m of their release location.…”

Section: Ground Assessment Of Green Attackmentioning

confidence: 99%

Challenges for the operational detection of mountain pine beetle green attack with remote sensing

Wulder¹,

White²,

Carroll³

et al. 2009

The Forestry Chronicle

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Mountain pine beetle infestations are spatially correlated; current (green) attack is often located near previous (red) attack. This spatial correlation between the green and red attack stages enables operational survey methods, as detection of red attack trees-typically from an airborne survey such as a helicopter GPS survey or aerial photography-guides the location of subsequent ground surveys for green attack trees. Forest managers, in an attempt to understand beetle movement and infestation patterns, hope to utilize remotely sensed data to detect and map green attack trees, with the expectation that the spatial extent, accuracy, and timeliness afforded by remotely sensed data will greatly improve the efficacy of beetle treatment and control. In this communication, we present the biological, logistical, and technological factors that limit the operational utility of remotely sensed data for green attack detection and mapping. To provide context for these limitations, we identify the operational information needs associated with green attack and discuss how these requirements dictate the characteristics of any potential remotely sensed data source (e.g., spatial, spectral, and temporal characteristics). Based upon our assessment, we conclude that the remote detection of green attack is not operationally viable, and is unlikely to become so unless the limiting factors we have identified are altered substantially or removed.Key words: green attack, remote sensing, operational, insect survey, high spatial resolution, high spectral resolution RÉSUMÉ Les infestations de dendroctones du pin ponderosa démontrent une corrélation spatiale : l'attaque courante (verte) est souvent localisée à proximité de l'attaque précédente (rouge). Cette corrélation spatiale entre les périodes vertes et rouges permet l'utilisation de méthodes de relevé opérationnelles, compte tenu que la détection d'arbres rouges attaqués-habituellement à partir d'un relevé par voie aérienne par exemple effectué au moyen d'un GPS monté à bord d'un hélicoptère ou par photos aériennes-indique la localisation des relevés terrestres à venir des arbres verts attaqués. Les aménagistes forestiers, cherchant à comprendre le déplacement des dendroctones et les patrons d'infestation, souhaitent utiliser les données obtenues par télédétection afin d'identifier et de cartographier les arbres verts attaqués, avec l' espoir que la distribution et la précision dans le temps et l' espace permises par les données de télédétection permettront de grandement améliorer l' efficacité du traitement et du contrôle du dendroctone. Nous présentons dans ce texte les facteurs biologiques, logistiques et technologiques qui limitent l'utilité opérationnelle des données de télédétection dans le cas de la détection et la cartographie des attaques vertes. Afin d'illustrer le contexte de ces limites, nous identifions les besoins requis en matière d'information opérationnelle associée à une attaque verte et nous discutons comment ces besoins dictent les caractéristiques de tout...

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Section: Ground Assessment Of Green Attackmentioning

confidence: 99%

Challenges for the operational detection of mountain pine beetle green attack with remote sensing

Wulder¹,

White²,

Carroll³

et al. 2009

The Forestry Chronicle

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“…There are two modes of SDD. Emerged beetles may either attack the nearest suitable host trees, in which case beetle populations spread from a central location in a stand (i.e., spot growth), or beetles may fly over somewhat greater distances for a longer period of time in search of suitable hosts, causing infestations at new locations within a stand (spot proliferation) (Safranyik et al 1992, Robertson et al 2007). Long-distance dispersal (LDD) occurs when beetles get caught in convective upward drafts and are transported long distances above the forest canopy by wind , Robertson et al 2007.…”

Section: Introductionmentioning

confidence: 99%

Mountain pine beetle dispersal: spatiotemporal patterns and role in the spread and expansion of the present outbreak

Chen

Walton

2011

Ecosphere

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Abstract. Dispersal has been least understood in mountain pine beetle ecology. We developed a novel regional dynamic conceptual model of mountain pine beetle infestation using the tree mortality estimated from the British Columbia annual aerial overview survey to quantitatively determine short-distance dispersal (SDD) and long-distance dispersal (LDD) at local (forest district) and regional (provincial) scales. The dispersal patterns were characterized based on distances between a sink patch to its nearest source patch. At the regional scale, SDD accounted for 85.3% of mountain pine beetle dispersal to non-infested areas and 96.8% of beetle dispersal to infested areas. Although SDD was a dominant dispersal mode, LDD played a more important role in the early stage of the current mountain pine beetle outbreak. At the local scale, three patterns of dispersal to non-infested areas were identified. First, LDD dominated in the forest districts where only sparse infestations occurred. Second, LDD was a dominant or important factor in the early stages of the infestations in some districts. Third, SDD dominated throughout the infestations in more severely infested forest districts. However, for dispersal to infested areas, SDD was a dominant mode in most of the forest districts. We conclude from the spatiotemporal patterns of dispersal observed at local and regional scales that LDD is a key factor in the initiation and early stage of the infestations in new remote areas, and SDD dominate in the spread and expansion of the outbreak as the infestations intensify and reach epidemic levels. However, it should be conscious that there is uncertainty that LDD might have been over emphasized in the dispersal before local dynamics is fully taken into account.

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“…In this study, a 2-km disk radius was chosen to optimize tradeoffs between detail and representations of infestation trends. The 2-km disk radius is also large enough to be relatively robust with respect to errors in point locations, and is reflective of stand-scale research, which indicates that a high proportion of local beetle dispersal occurs within 2 km of emergence (Safranyik et al 1992).…”

Section: Data Representation Via Kernel Density Estimationmentioning

confidence: 99%

Large-area mountain pine beetle infestations: Spatial data representation and accuracy

Nelson¹,

Boots²,

Wulder³

2006

The Forestry Chronicle

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Point data generated from helicopter surveys are used to determine the location and magnitude of mountain pine beetle infestations. Although collected for tactical planning, these data also provide a rich source of information for scientific investigations. To facilitate spatial research, it is important to consider how to best represent spatially explicit mountain pine beetle infestation data. This paper focuses on the spatial representation of point-based aerial helicopter surveys, which can be difficult to represent due to issues associated with large data quantities and data uncertainty. In this paper, the benefit of using a kernel density estimator to convert point data to a continuous raster surface is demonstrated. Field data are used to assess the accuracy of the point-based aerial helicopter survey data and the kernel density estimator is extended to incorporate data uncertainty. While the accuracy of point-based aerial surveys is high, with 92.6% of points differing by no more than ± 10 trees, there is a general tendency to overestimate infestation magnitude. The method developed for incorporating uncertainty into the kernel density estimator reduces overestimation and improves the correspondence between estimated infestation intensities and field data values.Key words: mountain pine beetle, data representation, visualization, kernel density estimators, uncertainty RÉSUMÉ Des points de données tirées de sondage par hélicoptère sont utilisés pour déterminer la localisation et l'importance des infestations de dendroctone du pin. Même si elles ont été recueillies pour des raisons de planification tactiques, ces données constituent également une importante source d'information pour les études scientifiques. Afin de faciliter la localisation spatiale, il est important d'étudier comment on peut représenter le mieux les données explicites en terme de localisation des infestations de dendroctone du pin. Cet article porte sur la représentation spatiale des points tirés des sondages par hélicoptère, ce qui peut être difficile à réaliser compte tenu des questions entourant les grandes quantités de données et l'incertitude qu'elles comportent. Dans le cas présent, l'avantage de l'utilisation d'un estimateur de noyau de densité pour la conversion des points de données en surface continue est démontré. Des données de terrain sont utilisées pour évaluer la précision des points de données du sondage aérien par hélicoptère et l'estimateur de noyau de densité est conçu pour comprendre l'incertitude entourant les données. Même si la précision des points de données par sondage aérien est élevée, 92 % des points ne différant par pas plus de ± 10 arbres, on retrouve une tendance générale de surestimer l'étendue de l'Infestation. La méthode élaborée pour incorporer l'incertitude au sein de l'estimateur de noyau de densité réduit la surestimation et améliore la correspondance entre les intensités d'infestation estimées et les valeurs en provenance des données du terrain.Mots clés : dendroctone du pin, représentation d...

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Dispersal of released mountain pine beetles under the canopy of a mature lodgepole pine stand

Cited by 102 publications

References 12 publications

Challenges for the operational detection of mountain pine beetle green attack with remote sensing

Challenges for the operational detection of mountain pine beetle green attack with remote sensing

Mountain pine beetle dispersal: spatiotemporal patterns and role in the spread and expansion of the present outbreak

Large-area mountain pine beetle infestations: Spatial data representation and accuracy

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