The Generation of an Accurate and Uniform Time Scale with Calibrations and Prediction

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The only exception was for the transition DtoR in relation to distance to settlements there was just one settlement in the study area and two in the periphery. Statistical analysis of perfect match results for the 40 modelling procedures, when compared to observed land cover in the year being validated, as a function of initial calibration year Initial year , the length of the time transition being used to calibrate the model Transition length , the model time step being validated Time step and finally the year being validated Validation year.

Model predictions improved with later starting years as well as later validation years Fig. Intuitively, this indicates that when trying to predict a land cover pattern it is better to use maps from a date closer to the one we are trying to predict.

Reliability

In particular, after choosing a recent map of land cover for modelling, a shorter transition length led to better model performance rather than a longer transition length. However, this decline in the area of primary forest has slowed down since the mids, which is probably due to a lack of good quality wood in the remaining primary forest patches that has led to a reduction in logging activity Miranda et al.

In addition, the remaining primary forest in is almost entirely sited within protected areas where logging and any other land use commonly found outside these areas such as croplands is strictly forbidden. The analyses of the three LCC transition types can be interpreted as examining the actions of different LCC processes.

Lab balance - AND Weighing GF3000 - setup lab scale

Loggers, in general, are facilitators and maybe catalysts of deforestation in some settings but rarely convert forest to other uses, therefore, we associate the transition FtoD to the establishment of new farms. By contrast, well established farming activities are better represented by the transition RtoD, where there is no primary forest but regenerated forests which get routinely deforested as part of crop rotation and fallow land practices.

The last transition DtoR can mainly be assumed to represent the process of land abandonment by farmers since no formal reforestation program has been implemented in the region. Deforestation is still an ongoing process in the region, but the forest that is being cleared is more typically regeneration, areas that have been deforested at least once in the past. This pattern suggests that the main deforestation agents in the region are now well established farmers rather than loggers followed by new farmers new colonists , and that the balance between regeneration and deforested land is maintained by farmers temporarily abandoning pastures and allowing regeneration to establish, before re-clearing the land in a cycle designed to maintain soil fertility in these nutrient-poor landscapes Smith et al.

The positive value found for the parameter associated with the frequency of clearance Fig. Our study is the first to formally and statistically analyse three decades of model parameterisation associated with multiple land cover transitions in the Brazilian Amazon. Some of our results confirm well known tendencies. For example, in both transitions that lead to deforestation, forest and regeneration areas next to already deforested areas were confirmed to be more likely to be deforested in the near future Fig. However, we found that in the transition that predicts regeneration this pattern was less evident suggesting that the process of regeneration does not follow a contagious process as strongly as deforestation does, perhaps reflecting spontaneous rather than planned activities.

Although this pattern will be found throughout the Amazon, where large areas of land have been deforested and later abandoned Galford et al. What is interesting to note is the marked difference between land cover transitions Fig.

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In the study area there are two main roads which were built by the government as part of the settlement program in the s. However, the majority of roads in the study region are unofficial roads, which are usually built by logging companies to have access to the forest to harvest the wood Arima et al.

These roads are then used by farmers to transport their goods to sell in the city markets. As such deforestation tend to happen closer to these roads, because farmers take advantage of these roads, but their decisions about where to deforest are also mediated by other factors such as soil fertility Escada et al.

Protected areas have a strong preventive effect on deforestation in the Brazilian Amazon Nepstad et al. Land abandonment DtoR tended to happen more inside protected areas than outside, which suggests that in our study area no real effort is being made to let the deforested area recover to a state closer to the original forest outside these protected areas.

As such, although forest cover seems to be improving inside protected areas, these might become very isolated and their future can be undermined Ribeiro et al. The temporal analysis on model parameters revealed some degree of variation within time transitions, but for the majority of the parameters no significant trend through time. This lack of variability can partially be explained by the fact that some of these variables simply do not change through time such as the rivers as it does not change their relation with the land cover process being modelled.

For others, such as roads, it should be expected that the estimated coefficients will have low variability with a closest fit in the time where roads and land use corresponded to each other and deviate from it as time moves away from this point in time.

However, due to lack of data, we were unable to update the road map annually. This most likely affects the results found for the parameter associated with this variable.

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The relatively constancy in the parameter associated with human settlements is potentially limited by the fact that there is only one settlement in our study area. As it is well known that settlements can strongly influence landscape change in the Amazon Brandao and Souza Jr. Their impact on the landscape is not only about the number of people that live in it, but also their distribution.

There were, however, some parameters that exhibited strong temporal trends, such as protected areas, which became increasingly more important in preventing deforestation of primary forests, while the landscape changes and the forest outside protected areas gets heavily degraded. Also the contagion effect of deforestation RtoD transition and regeneration DtoR transition became increasingly more important, showing how LCC strongly follows a contagion pattern. The changes found in the parameter associated with frequency of clearance show that when large areas of primary forest are unprotected, people tend to deforest those areas.

However, as the landscape evolves and these forests become scarce and constrained to protected areas , people opt to re-deforest areas that have already been cleared. The temporal variability found in parameter estimates needs to be incorporated into models, although this represents a considerable challenge to a modelling discipline that rarely quantifies the uncertainty around their predictions Rosa et al.

Parameters that exhibit directional trends, however, will require more extensive time series data to allow that trend to be quantified. The next generation of LCC models will need to embed those trends within simulations in order to allow the processes determining LCC to change through time and exert their influence on model predictions. Maeda et al.

The reason for this dependence of model accuracy on calibration year is the non-stationarity of the processes underlying LCC. This suggests that accurate predictions of future landscapes cannot be made using maps from a long time in the past as model inputs, because the processes determining LCC are dynamic and change through time. As such, parameters included in the model that predict future landscapes should allow variation which would reflect this change rather than being kept constant assuming that the same process of LCC perpetuates through time.

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The calibration year was the most important factor influencing the performance of the LCC models; however, once this was controlled the time step and the transition length became important as well. This suggests that after choosing the most recent land cover maps for modelling future landscape alterations, we achieved higher values of perfect match when we validated our annual predictions against land cover maps from a date closer to the one modelled shorter time step.

The validation results tended to get worse or more uncertain when we validated our predictions against a map from a distant date in time larger time step. Finally, a similar result was found for the transition length, meaning that after choosing the most recent maps of land cover for modelling, a shorter transition length led to better model performance rather than a longer transition length.

This result can be counterintuitive as people usually think that a larger transition length might be best for model calibration because a short transition length might not be representative of the dynamics of land cover in the region, rather it might represent a spontaneous event. However, as we show in this study, a long transition for a long-past period will likely generate a poor model because the processes of LCC are dynamic through time, and the aspects of it being captured by a model calibrated in the past either with a long or short transition length will not be able to capture new processes occurring in the landscape.

Our results suggest it is better to use a short transition length from a recent calibration period than it is to rely on a long transition period from the past. However, the pattern was found to be less strong than the one found for the time step and, as such, more work needs to be done in order to further analyse this relationship.

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LCC models are used in many studies of human impacts on the environment, but knowing how well these models predict observed changes in the landscape is still a challenge. We used nearly three decades of LCC maps to determine that parameters associated with the different drivers of change were selected differently for each land cover transition; and these transitions, which varied significantly through time, could be associated with different landscape change activities.

In addition, our model validations revealed a strong importance of calibration year and validation year in determining the predictive power of the model. Furthermore, some of the parameters associated with different LCC drivers exhibited strong directional trends. At long timescales, the problem is saturation. When enough time has passed, many sites have undergone more than one change, but it is impossible to detect more than one. This means that the observed number of changes is no longer linear with time, but instead flattens out.

Even at intermediate genetic distances, with phylogenetic data still sufficient to estimate topology, signal for the overall scale of the tree can be weak under complex likelihood models, leading to highly uncertain molecular clock estimates. At very short time scales, many differences between samples do not represent fixation of different sequences in the different populations.

Instead, they represent alternative alleles that were both present as part of a polymorphism in the common ancestor. The inclusion of differences that have not yet become fixed leads to a potentially dramatic inflation of the apparent rate of the molecular clock at very short timescales.

The molecular clock technique is an important tool in molecular systematics , the use of molecular genetics information to determine the correct scientific classification of organisms or to study variation in selective forces. Knowledge of approximately constant rate of molecular evolution in particular sets of lineages also facilitates establishing the dates of phylogenetic events, including those not documented by fossils , such as the divergence of living taxa and the formation of the phylogenetic tree.

From Wikipedia, the free encyclopedia. Technique to deduce the time in prehistory when two or more life forms diverged. Not to be confused with Chemical clock. Darwin's finches by John Gould.


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Key topics. Introduction to evolution Evidence of evolution Common descent Evidence of common descent. Processes and outcomes. Natural history. History of evolutionary theory. Fields and applications. Applications of evolution Biosocial criminology Ecological genetics Evolutionary aesthetics Evolutionary anthropology Evolutionary computation Evolutionary ecology Evolutionary economics Evolutionary epistemology Evolutionary ethics Evolutionary game theory Evolutionary linguistics Evolutionary medicine Evolutionary neuroscience Evolutionary physiology Evolutionary psychology Experimental evolution Phylogenetics Paleontology Selective breeding Speciation experiments Sociobiology Systematics Universal Darwinism.

Social implications. Evolution as fact and theory Social effects Creation—evolution controversy Objections to evolution Level of support. In Kasha, M. Horizons in Biochemistry. Academic Press, New York. Bibcode : PNAS Bibcode : Natur.