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  CLIMEX 物种分布潜在区域预测软件

CLIMEX帮助您理解气候变化(climate change)对种群分布和入侵物种对农业区域的潜在风险的影响。

CLIMEX是通过物种已知地理分布区域的气候参数来预测物种潜在分布区的软件。

CLIMEX有2个基本假设:

(1)物种在1年内经历2个时期,即适合种群增长时期和不适合以至于危及生存的时期;
(2)气候是影响物种分布的主要因素,并利用增长指数、胁迫指数和限制条件(滞育和有效积温)描述物种对气候的不同反应,

这2组参数构成生态气候指数,作为全面描述物种在某地区和年份适合度的指标.模型预测结果以表、图和地图输出.

CLIMEX可以用于检疫、生物防治、有害生物风险分析、害虫管理和流行病的预测等.目前已经用于几十种有害生物的适生性研究.

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What is CLIMEX ?

The CLIMEX software contains two quite different climate-modelling tools. There is the CLIMEX model (referred to as 'CLIMEX' or as the 'CLIMEX model'), and the CLIMEX 'Match Climates' function. The latter is a tool for comparing the meteorological data of different places without reference to any particular species and we will return to it later. The CLIMEX simulation model was first described by Sutherst and Maywald (1985) and a number of enhancements and further caveats and insights into using the model have been described in a series of publications listed at the end of this Guide, particularly (Sutherst et al 1995, Sutherst 1998, and Kriticos et al. 2005). The CLIMEX model is based on the assumption that if you know where a species lives you can infer what climatic conditions it can tolerate. This is the cardinal assumption underpinning most species distribution models. However, where other models such as Bioclim, Domain, GARP and MaxEnt attempt to characterise the environment occupied by the species, CLIMEX attempts to simulate the mechanisms that limit species' geographical distributions and determine their seasonal phenology and to a lesser extent their relative abundance. In this respect, CLIMEX shares a similarity of approach with Nichemapper (Kearney and Porter 2009), but is less complex. In CLIMEX the species functional responses are formulated in accordance with a set of theory supported by more than a century of study of the response of species to climatic variables. The Growth Indices and Stress Indices accord with the Law of Tolerance (reviewed in Shelford 1963), and the Growth Indices are combined in accordance with the Law of the Minimum (reviewed in van der Ploeg et al. 1999). CLIMEX enables the user to estimate the potential geographical distribution and seasonal abundance of a species in relation to climate. It does not try to match the patterns of climate and species' distribution in the same way that a correlative statistical model would seek to achieve. More on this later.

CLIMEX is applied to a species by selecting values for a set of parameters that describe its response to temperature, moisture and light. In this guide the term 'population' is used as the target entity, representing an average population of an animal or plant species or biotype for example. An Annual Growth Index (GIA) describes the potential for growth of a population during the favourable season. Four stress indices (Cold, Hot, Wet and Dry), and in some cases their interactions, describe the extent to which the population is reduced during the unfavourable season. The Growth and Stress Indices are combined into an Ecoclimatic Index (EI), to give an overall measure of favourableness of the location or year for permanent occupation by the target species. Two limiting conditions, ie the length of the growing season and obligate diapause, act as overall constraints to the EI value where relevant. Results are presented as tables, graphs, maps or movies.

CLIMEX can combine inductive and deductive modelling techniques. A species' climatic requirements can be inferred from its known geographical distribution (either in its native range or in another region where it has been established for a long time), relative abundance and seasonal phenology. Where it is available, laboratory data, such as developmental threshold temperatures, or optimal temperatures can be used to fit or fine tune CLIMEX parameter values. Initial estimates of parameter values are fine-tuned by comparing the indices with the known presence or absence, seasonal phenology and, preferably, relative abundance of the species in each location.

Once the parameter values have been estimated and where possible validated against independent data, CLIMEX can be used to make predictions for other, independent locations. Independent data means that there is no connection between the data and those data used for fitting the model, hence it is not appropriate to sub-sample a geographical distribution and then use the remaining data to test the model.

What is DYMEX ?

DYMEX is a computer software package that enables you to interactively build and then run models of fluctuating populations of organisms in changing environments. Ecologists can create a wide range of process-based population models without the need to know a programming language. Models are structured around lifecycles, which in turn consist of the stages that individuals pass through during their life. A DYMEX lifecycle describes cohorts of individuals and the processes that affect the size, age and number of individuals in the cohort

Models created within DYMEX consist of a series of modules, with each module responsible for a particular task. Modules use information from other modules as input, and supply information to other modules. DYMEX comes with a library of modules that can be incorporated into any model constructed with the Builder (Fig. 11). Each module performs a specific function (for example, MetBase is used to read a standard set of meteorological variables from a file). Models created in the Builder can be opened in the Simulator within which simulations can be run. The results of these simulations can be displayed in tables, graphs and maps as well as exported to other programs.

Models will normally be developed around one or more Lifecycle modules. Other modules provide data to the lifecycle modules, or manipulate lifecycle output in some way. Many modules have multiple uses (e.g. Function module) and may be used in several places in a model, while others are more specialised (e.g. the Soil Moisture module). Most modules receive input from another module or from an outside source. For example, the MetBase (Meteorological Database) module reads meteorological data from text files and provides the data as variables that can be accessed by other modules.

CLIMEX helps you understand the impact of climate change on species distribution and the potential risk from invasive species to an agricultural region.

CLIMEX enables you to assess the risk of a pest establishing in a new location and the potential success or failure of a biological control agent with no knowledge of the species, except for knowing the current locations they do occur.

In almost forty countries around the world, Climex is used to model, predict and help control invasive insects. Insect infestation destroys billions of dollars worth of commercial crops annually and monitoring and controlling invasive insects in a warming world is increasingly important.

The CLIMEX software contains two quite different climate-matching tools. There is the CLIMEX model (referred to as ‘CLIMEX' or as the ‘CLIMEX model'), and the CLIMEX ‘ Match Climates ' function. The latter is a tool for comparing the meteorological data of different places without reference to any particular species.

The CLIMEX simulation model was first described by Sutherst and Maywald (1985) and a number of enhancements and further caveats and insights into using the model have been described in a series of publications listed at the end of the user manual, particularly (Sutherst et al 1995, Sutherst 1998). The model is based on the assumption that if you know where a species lives you can infer what climatic conditions it can tolerate. In other words, CLIMEX attempts to mimic the mechanisms that limit species' geographical distributions and determine their seasonal phenology and to a lesser extent their relative abundance.

CLIMEX enables the user to estimate the potential geographical distribution and seasonal abundance of a species in relation to climate. It does not try to match the patterns of climate and species' distribution in the same way that a statistical fitting would seek to achieve.

CLIMEX is applied to a species by selecting values for a set of parameters that describe its response to temperature, moisture and light. The term ‘population' is used as the target entity, representing an average population of an animal or plant species or biotype for example. An Annual Growth Index (GIA) describes the potential for growth of a population during the favourable season. Four stress indices (Cold, Hot, Wet and Dry), and in some cases their interactions, describe the extent to which the population is reduced during the unfavourable season. The Growth and Stress Indices are combined into an Ecoclimatic Index (EI), to give an overall measure of favourableness of the location or year for permanent occupation by the target species. Two limiting conditions, ie the length of the growing season and obligate diapause, act as overall constraints to the EI value where relevant. Results are presented as tables, graphs, or maps.

A species' climatic requirements are inferred from its known geographical distribution (either in its native range or in another region where it has been established for a long time), relative abundance and seasonal phenology. Some laboratory data, such as developmental threshold temperatures, can be used to fit or fine tune CLIMEX parameter values. Initial estimates of parameter values are fine-tuned by comparing the indices with the known presence or absence, seasonal phenology and, preferably, relative abundance of the species in each location.

Once the parameter values have been estimated and where possible validated against independent data, CLIMEX can be used to make predictions for other, independent locations. Independent data means that there is no connection between the data and those data used for fitting the model, hence it is not appropriate to sub-sample a geographical distribution and then use the remaining data to test the model.

 

 

 

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