Infrequent Fire 100+ year interval Frequent fires 1-40 years

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Transcripción de la presentación:

Figure 1. El Ocote moist/wet tropical forest fire state-transition model Infrequent Fire 100+ year interval Frequent fires 1-40 years Moist/wet tropical forest closed canopy 6-45 cm organic/duff Fuel model 8 Stable Ficus/Acacia woodland/savanna Open canopy, no organic soil Fuel model (2/3) Stable Clearing, grazing frequent fire No fire (100+ years) Uno grande fuego El nino-drought, infrequent Fire every 3+ years 50 + years with no fire Fire every 1-2 years and/or clearing (more than 1-50) Frequent fire Sub models for each system on response to variation in the fire regime. Given various quality categories and information we had about restoration - 20,000 acres of shrubland, 20,000 acres of low-med quality, and 20,000 acres of high quality and based on what we learned in our statewide work, we modeled the various states and transitions for each of the community alliances depending on restoration of different components of the fire regime. Given what we knew - we could use fire in a deterministic way to get the management outcome we wanted, not just restore it in the range of variation Developed some specific models for the area based on the assessment and inferred from our experimental areas. We developed a series of models and these are examples. Grassland FM 3 Shrubland FM 5-6 Once burned moist/wet tropical forest shrubs, dense, woody debris Less than 6 cm organic soil Fuel model (4,5,6 ish) Unstable Frequent fire

Grassland with some pine Figure 2. La Sepultura pine-grass woodland fire state-transition model Fire every 5-7 years Fire every 3-5 years Pine woodland with grass understory FM 2 Low risk, Stable BA = 40 - 70 Fire every 5-7 years Pine/shrubs FM 9 70-100 BA Medium risk Stable Fire every 3-5 years Annual fire or cut Fire every 3-5 years No fire Crown fire after 10 years of fuel build - up/ or cut Restoration: plant pines, fire every 3-5 years Sub models for each system on response to variation in the fire regime. Given various quality categories and information we had about restoration - 20,000 acres of shrubland, 20,000 acres of low-med quality, and 20,000 acres of high quality and based on what we learned in our statewide work, we modeled the various states and transitions for each of the community alliances depending on restoration of different components of the fire regime. Given what we knew - we could use fire in a deterministic way to get the management outcome we wanted, not just restore it in the range of variation Developed some specific models for the area based on the assessment and inferred from our experimental areas. We developed a series of models and these are examples. Grassland with some pine FM 3 Stable BA = 10 or less Grassland FM 3 Shrubland FM 5-6 Annual fire Frequent fire

Zapotanal (freshwater) Figure 3. La Encruijiada Marsh - Mangrove state transition model No fire Surface fire every 1-20 years Typha - Thalia Marsh Deep organic soil Fuel model 4 Stable Mangrove (saline) or Zapotanal (freshwater) Fuel model 8 Stable No fire Fire Ground fire Fire 1-20 No fire over the long term 50 years Sub models for each system on response to variation in the fire regime. Given various quality categories and information we had about restoration - 20,000 acres of shrubland, 20,000 acres of low-med quality, and 20,000 acres of high quality and based on what we learned in our statewide work, we modeled the various states and transitions for each of the community alliances depending on restoration of different components of the fire regime. Given what we knew - we could use fire in a deterministic way to get the management outcome we wanted, not just restore it in the range of variation Developed some specific models for the area based on the assessment and inferred from our experimental areas. We developed a series of models and these are examples. Open water Unstable

Figure 4. Iguana life history risk state-transition model Sandy hummocks lay eggs in burrows - fire proof because not organic Low risk Marsh High risk due to fires related to hunting Live in the mangroves low risk What season? And how long are they here? What season? And how long are they here? What season? And how long are they here? Sub models for each system on response to variation in the fire regime. Given various quality categories and information we had about restoration - 20,000 acres of shrubland, 20,000 acres of low-med quality, and 20,000 acres of high quality and based on what we learned in our statewide work, we modeled the various states and transitions for each of the community alliances depending on restoration of different components of the fire regime. Given what we knew - we could use fire in a deterministic way to get the management outcome we wanted, not just restore it in the range of variation Developed some specific models for the area based on the assessment and inferred from our experimental areas. We developed a series of models and these are examples.

Figura 1. Modelo ecológico del fuego para El Ocote (bosques humedos y muy humedos tropicales) Fuego frequente 1-40 años Fuego pequeños infrequente en intervalos de 100 años o mas Bosque humedo/muy humedo tropical bosque cerrado 6-45 cm suelo organico Modelo de combustible 8 Estable Aclareo, pastoreo fuego frequente Ficus/Acacia bosque/sabana Bosque abierto, sin suelo organico Modelos de combustibles (2 y 3) Estable Sin fuego por 100+ años Fuego cada 3+ años Un gran incendio, infrequente por la sequía de El Niño Fuego cada 1-2 años y/o aclareo 50 + años sin fuego Fuego frequente (mas que 1-50) Sub models for each system on response to variation in the fire regime. Given various quality categories and information we had about restoration - 20,000 acres of shrubland, 20,000 acres of low-med quality, and 20,000 acres of high quality and based on what we learned in our statewide work, we modeled the various states and transitions for each of the community alliances depending on restoration of different components of the fire regime. Given what we knew - we could use fire in a deterministic way to get the management outcome we wanted, not just restore it in the range of variation Developed some specific models for the area based on the assessment and inferred from our experimental areas. We developed a series of models and these are examples. Un incendio intensivo bosque humedo/muy humedo tropical arbustos densos, combustible matorral Menos que 6 cm suelo organico Modelo de combustible (4 o 5 o 6?) Inestable Pastizales Modelo de combustible 3 Matorral Modelos de combustibles 5 y/o 6 Fuego frequente

Figura 2. Modelo ecologíco del fuego para La Sepultura (bosque de pino-pastizales) Frecuencia del fuego de 5-7 años Frecuecia del fuego de 3 a 5 años Frecuencia del fuego 5-7 años Bosque de pino y pastizales Modelo de combustible 2 Riesgo bajo, estable AB = 40 - 70 Bosque de pino con arbustos Modelo de combustible 9 70-100 AB Riesgo medio Estable Frecuencia del fuego 3-5 años Fuego de copa despues de 10 años por acumulacion de combustibles o de una corta Frecuencia del fuego 3-5 años Reforestacion de pinos y fuego 3-5 años Sub models for each system on response to variation in the fire regime. Given various quality categories and information we had about restoration - 20,000 acres of shrubland, 20,000 acres of low-med quality, and 20,000 acres of high quality and based on what we learned in our statewide work, we modeled the various states and transitions for each of the community alliances depending on restoration of different components of the fire regime. Given what we knew - we could use fire in a deterministic way to get the management outcome we wanted, not just restore it in the range of variation Developed some specific models for the area based on the assessment and inferred from our experimental areas. We developed a series of models and these are examples. Paztizales Modelo de combustible 3 o Matorrales Modelo de combustible 5-6 Pastizales con pinos escasos Modelo de combustible 3 Estable AB = 10 o menos Fuego frecuente (natural y quemas) Fuego anual (natural y quemas)

Figura 3. Modelo ecologico del fuego para La Encrucijada (tulares, popales y manglares) Sin fuego Fuego superficial de 1-20 años Tulares - Popales con suelo organico modelo de combustibles 4 Estable Manglares (agua salada) o Zapotanal (agua dulce) modelo de combustibles 8 Estable Sin fuego Fuego Frecuencia del Fuego 1-20 Incendios subterraneo Sin fuego por un periodo de 50 o mas años Sub models for each system on response to variation in the fire regime. Given various quality categories and information we had about restoration - 20,000 acres of shrubland, 20,000 acres of low-med quality, and 20,000 acres of high quality and based on what we learned in our statewide work, we modeled the various states and transitions for each of the community alliances depending on restoration of different components of the fire regime. Given what we knew - we could use fire in a deterministic way to get the management outcome we wanted, not just restore it in the range of variation Developed some specific models for the area based on the assessment and inferred from our experimental areas. We developed a series of models and these are examples. Laguna inestable

Estando en los manglares Figura 4. Modelo del ciclo de vida de la Iguana verde Islotes de popal y tular Anidacion por debajo de los islotes Sin peligro del fuego por estar debajo del suelo Estando en los manglares riesgo de muerte bajo Epoca Movimientos continuos entre popales, tulares y los islotes el riesgo de muerte aumenta caza por fuego para consumo y coleccion Sub models for each system on response to variation in the fire regime. Given various quality categories and information we had about restoration - 20,000 acres of shrubland, 20,000 acres of low-med quality, and 20,000 acres of high quality and based on what we learned in our statewide work, we modeled the various states and transitions for each of the community alliances depending on restoration of different components of the fire regime. Given what we knew - we could use fire in a deterministic way to get the management outcome we wanted, not just restore it in the range of variation Developed some specific models for the area based on the assessment and inferred from our experimental areas. We developed a series of models and these are examples. Cuando y cuanto tiempo permanecen en este refugio? Cuando y cuanto tiempo permanecen en este lugar? Cuando y cuanto tiempo permanecen en este refugio?