Enter the Amazon: Book Three: Figures In The Landscape

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This variability is measured by A standard deviations and B coefficients of variation of among-census variation for 1-ha plots. Most other edge parameters exhibited similar trends. Three variables, cattle ranch, distance to forest edge, and the number of nearby edges, were the most important predictors of spatial variation in edge phenomena, having significant effects on six, five, and two edge-effect variables, respectively Table 1. As expected, edge phenomena increased in intensity closer to forest edges and with more nearby edges. Soil factors, slope, and fragment area per se had no significant influence on edge-effect variables.

The P value for each predictor is for a full model that includes all predictors significant P values are shown in bold. Predictors for each plot include distance to the nearest forest edge, the number of nearby forest edges, fragment or reserve area, the cattle ranch in which fragments were located, percent sand content, soil carbon content, and the mean slope. Analyses are based on 40 1-ha plots randomly stratified across the study area overall floristic change, floristic vectors 1—2, species turnover or on all 66 1-ha plots in the study all other response variables.

Spatial and temporal variability in tree mortality evidently helps to drive variability in several other edge-effect phenomena. Model improvement was greatest for four floristic variables overall change in tree-community composition, floristic vectors 1 and 2, and the rate of tree-species turnover. Finally, for several edge-effect variables, such as floristic vector 2 Figure 3A and tree-species turnover Figure 3B , tree mortality was a highly significant covariate when the variables were contrasted among cattle ranches. Differences among the ranches are shown for A floristic change floristic vector 2 and B tree-species turnover, using the mean tree-mortality rate in each plot as a covariate.

Given the apparently important impacts of tree mortality on forest ecology, we evaluated how tree-mortality rates vary over time, using data from the repeated censuses of our 66 plots. Two trends were apparent. First, although tree mortality was generally elevated near forest edges Figure S1 , it was also highly episodic, varying markedly among different census intervals.

This is illustrated by the strong tendency for plots with high mean mortality rates averaged over the entire study to have significantly elevated CVs Figure 4A. Second, mortality rates tended to decline somewhat with fragment age, at least among edge plots, which had the highest overall mortality rates Figure 4B. Collectively, these analyses suggest that elevated tree mortality partially drives changes in several other edge-effect phenomena, especially those relating to the intensity and pace of floristic change in fragments.

Tree mortality is highly variable temporally and spatially, and tends to decline somewhat as fragments become older, especially among plots near forest edges. Although many edge phenomena were significantly affected by the proximity and number of nearby forest edges, as expected, they also differed to a surprisingly extent among the three large cattle ranches in our study area. For nearly three decades, we and our colleagues have studied ecological changes in forest fragments within a km 2 experimental landscape, comprised by three large, isolated cattle ranches that were carved out of intact forest Figure 1.

Within these fragments, edge effects are clearly the dominant drivers of ecological change [5] , [55] , but the diverse edge phenomena we evaluated were often strikingly variable in space and time. Part of the pronounced spatial variability we observed arises from local factors such as the proximity and number of nearby forest edges Tables 1 , S1 , and S2.

Plots with two or more neighboring edges, such as those in small 1-ha fragments and on the corners of larger fragments, have significantly greater tree mortality and biomass loss, fewer old-growth-tree seedlings [29] , and higher abundances of pioneer and invasive tree species [30] and lianas [53] , than do those with just one nearby edge. These patterns clearly support additive models of edge effects [28] , which suggest that the intensity of edge phenomena is compounded by multiple nearby edges.

Edge age also influences edge effects. Edge-related tree mortality is especially intense in the first few years after edge creation Figure 4 [5] , [55] , [62] , in part because microclimatic changes are especially strong near newly formed edges, which are structurally open and thus highly permeable to the penetration of heat, light, and wind from outside degraded lands [24] , [63]. In addition, most trees along newly formed edges are not physiologically acclimated to the sudden heat and desiccation stress, and many simply drop their leaves and die standing [5] , [62].

Over time, the edge is partially sealed by proliferating vines and second growth, and microclimatic gradients lessen in intensity [7] , [63]. Rates of tree death from physiological stress likely decline over time, both because older edges are less permeable and because trees that are poorly adapted for edge conditions or in poor health generally tend to die and be replaced by more desiccation-tolerant species [15].

These changes probably explain the moderate decline in tree-mortality rates with edge age observed in this study Figure 4B. Another driver of both spatial and temporal variability in edge effects is extreme weather events. The abrupt, artificial boundaries of forest fragments are especially vulnerable to windstorms, which can exert strong lateral-shear forces on exposed trees and create downwind turbulence for at least 2—10 times the height of the forest edge [64] , [65].

In the Amazon, the most intense wind blasts come from convectional thunderstorms, which can cause severe but localized forest disturbance [66] , [67]. Such windstorms are largely random events [66] that interact with local topography, leading to spatially complex patterns of forest disturbance [68].

Since our study commenced in , fragments in the Dimona and, to a lesser extent, Porto Alegre ranches have been heavily damaged by windstorms, whereas those in Esteio ranch have remained largely unscathed [30] , [38] Figure 1. These episodic wind disturbances cause considerable spatial and temporal variability in tree mortality and other correlated edge effects, such as floristic change and forest-biomass loss Tables S1 and S2.

Periodic droughts also contribute to the temporal variability of edge effects, given the inherent vulnerability of rainforest edges to desiccation [23] , [27]. During the strong ENSO drought, dry-season rainfall was less than a third of average in our study area, and tree mortality and leaf-shedding by drought-stressed trees rose markedly near forest edges [40] , [54]. In addition, destructive, edge-related forest fires proliferated dramatically across the Amazon [16] , [42].

Finally, the structure and composition of the adjoining matrix vegetation can have a strong influence on edge effects. In our study area, forest edges adjoined by young regrowth forest, which helps to provide a physical buffer from wind and light, suffered less-intensive edge-related changes in microclimate [24] and lower tree mortality [32] than did those adjoined by cattle pastures. The species composition of the matrix vegetation is also important, because it influences the seed rain entering fragments [4] , [27].

In our study area, tree species regenerating in fragments adjoined by Vismia -dominated regrowth were very different more diverse and less dominated by the pioneer Cecropia sciadophylla from those in fragments bordered by Cecropia -dominated regrowth [38]. Such differences can propel surprisingly rapid changes in the floristic composition of fragments [4] , [15].

In this study, several of the factors described above manifested themselves as important differences in edge effects among our three large cattle ranches Table 1 , Figure 3. Such differences initially surprised us. Our three sprawling ranches Figure 1 were carved out of the surrounding old-growth forest almost simultaneously, as part of the same government-sponsored program to promote large-scale cattle ranching in the central Amazon. The three ranches had broadly similar vegetation and climate despite certain differences in soils, slope, and their initial tree-community composition; see Methods and Protocol S1.

Moreover, given that our study is a carefully controlled experiment, none of the ranches was subject to various complicating pressures, such as wildfires, selective logging, and overhunting, that plague many human-dominated landscapes [2] , [69]. Yet despite such similarities, the fragments within the three landscapes have undertaken remarkably different trajectories of change. Why have these landscapes diverged? The reason is that even small initial differences among the ranches quickly multiplied into much larger differences.

Parts of the Porto Alegre and Esteio ranches were cleared in , when an early wet season prevented burning of the felled forest [48]. Tall and floristically diverse Cecropia -dominated regrowth quickly developed in these areas, whereas areas cleared in other years became cattle pastures or, eventually, scrubby Vismia -dominated regrowth [70]. The differing matrix vegetation had major impacts on both the dynamics and trajectories of floristic change [15] , [30] , [38] and the composition of faunal communities [36] , [48] in nearby fragments. These differences were magnified by subsequent windstorms, which severely damaged some fragments at Dimona and to a lesser extent at Porto Alegre, yet left the Esteio fragments unscathed.

Even identically sized fragments in the three ranches have had remarkably different dynamics Figure S1 and trajectories of compositional change. The apparently acute sensitivity of fragments to local landscape and weather dynamics—even within a study area as initially homogeneous as ours—prompts us to propose a new hypothesis about the functioning of fragmented ecosystems. We suggest that fragments within the same landscape will tend to have similar dynamics and trajectories of change in species composition, which will often differ from those in other landscapes.

Over time, we believe, this process will act as a homogenizing force for fragments within the same landscape, and will promote increasing ecological divergence among fragments in different landscapes as a corollary, fragments that experience similar matrix, disturbance, and environmental conditions are predicted to converge in composition, even if they are not in the same vicinity. This concept is illustrated by the rapidly changing tree communities in our study area, which appear to be diverging in composition among the three cattle ranches Figure 5 , and by other key differences in ecological dynamics among the ranches Table 1 , Figure 3.

The ordination used importance values for all tree genera found in the plots. According to the nested-subsets concept, the biota in low-diversity fragments will comprise a proper subset of those in higher-diversity fragments or intact habitat. Although the predictions of the landscape-divergence and nested-subsets hypotheses differ markedly, they are not mutually exclusive: Landscape divergence might help to explain, for example, the weakly nested structure observed in some fragmented communities [see 72 and references therein].

If our hypothesis is correct, then different fragmented landscapes may tend to diverge not only in species composition but also in ecosystem functioning. Differences in characteristics such as forest dynamics, carbon storage, functional-guild composition, and species invasions could gradually accumulate over time, leaving an increasingly pervasive signature of divergence on community composition and functioning. In practice, however, discriminating the effects of landscape divergence from preexisting patterns of beta diversity may not be straightforward, at least in the absence of pre-fragmentation data.

Statistical techniques such as additive partitioning [73] , [74] might be useful for apportioning variation in species diversity within and among landscapes, and thus for contrasting certain predictions of the nested-subsets versus landscape-divergence hypotheses. We conclude by highlighting three potential implications of our findings. First, the striking variability in edge effects we observed suggests that short-term or small-scale studies may fail to detect important edge phenomena, or may characterize them inadequately [7] , [9].

In this study, our confidence was bolstered by the fact that we had pre-fragmentation data on tree-species distributions, stand structure, and biomass across our entire network of study plots. Even so, further replication would have been helpful for characterizing spatial variability in edge phenomena. Because of such inherent variability, it has been suggested that the known penetration-distance of edge effects should be doubled for management purposes [75] , such as when designing buffer zones for nature reserves.

Second, our landscape-divergence hypothesis suggests that, rather than simply homogenizing biotas via selective extinctions, habitat fragmentation could also promote important landscape-scale differences among biotas. If so, this phenomenon should be incorporated into conservation planning, as it could imply, for example, that protected areas in different landscapes could preserve biologically and functionally different components of ecosystems.

Finally, our findings highlight the key impact of matrix vegetation on fragment dynamics [see also 76]. In the Amazon, among the worst and unfortunately most common land-use practices is one in which forest fragments are encircled by pastures, which are regularly burned by ranchers to control weeds and promote a flush of green grass for cattle. In your opinion, which of the following characteristics are associated with Apple?

49 Companies Amazon Could Destroy (And 1 It Already Has)

Facebook's annual revenue from to in million U. Facebook's annual revenue from to , by segment in million U. Facebook's global revenue as of 3rd quarter in million U. Facebook's revenue and net income from to in million U. Facebook's net income from 1st quarter to 3rd quarter in million U. Which of the following Facebook services and products do you use at least occasionally?

Do you use your own user account or a personal account for Facebook services and products? How much have you spent on Facebook apps and in-app purchases over the past 12 months? In your opinion, which of the following characteristics are associated with Facebook? Which of the following statements do you agree with regarding Facebook? Net sales revenue of Amazon from to in billion U.

Global net revenue of Amazon. Net revenue of Amazon from 1st quarter to 3rd quarter in billion U. Annual net income of Amazon. Amazon's net income from 1st quarter to 3rd quarter in million U. Which of the following Amazon services and products do you use at least occasionally? Do you use your own user account or a personal account for Amazon services and products? How much have you spent on consumer goods on Amazon over the past 12 months?

How much have you spent, whether bought or rented, on selected types of digital media at Amazon over the past 12 months? How much have you spent on Amazon apps and in-app purchases over the past 12 months? In your opinion, which of the following characteristics are associated with Amazon? Which of the following statements do you agree with regarding Amazon? Share of people in the U.

Amazon River

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Value of the leading 10 textile exporters worldwide. World coffee per capita consumption: Cosmetics Industry in the U. Instagram accounts with the most followers worldwide Most popular global mobile messenger apps Number of paying Spotify subscribers worldwide Global all time unit sales of Call of Duty franchise games as of January Number of Starbucks locations worldwide Market share of leading carbonated beverage companies worldwide. Total number of Nike retail stores worldwide Revenue and financial key figures of Coca-Cola National Basketball Association all-time scoring leaders Super Bowl wins by team Also, it has patents on drone design for better maneuvering, secure landing, and long flights.

On an advanced level, they got a patent for a method to charge electric vehicles through drones. This shows their interest in automobiles as future will require many methods to charge an EV. Further, there would be no surprise if Amazon ventures in automobiles domain. Artificial Intelligence is one tough area where despite having many competitors Amazon got a big draw.

During the same month, the company and Microsoft partnered to roll out new tools that will make it easier for developers to use open-source artificial intelligence software. Amazon announced its Alexa Everywhere strategy in and surprisingly it became a huge success despite the presence of other top personal assistants in the market. In , Amazon announced to install Alexa AI in every echo device and launched a number of new products. Amazon also announced two major Alexa integrations for non-Echo devices.

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Amazon further revealed that Alexa would be supported in BMW cars beginning of the next year. Further, the Fire TV set-top box was launched with microphones embedded in the device so consumers can shout Alexa commands across their homes. If that was not it, Alexa based in-house drones were released, which could be called from anywhere around the house. The store has no checkout point and therefore has no cashier for making payments of your purchases. The payment can be added automatically to cart whenever you take a product from shelves. And after the purchase, the payment automatically gets deducted from your account or digital wallet.

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Amazon has recently acquired companies in cloud computing space and invested in businesses based on the cloud. In early , the company acquired a number of companies to strengthen its AWS Cloud business. Additionally, Amazon invested in Grail which is a potential future customer of Amazon cloud services.

In order to increase the usage of its cloud technology, Amazon Web Services AWS is investing some of its money to open data centers in Britain and France. During the first half of , Amazon invested in AWS and added new features and services to the segment.

In , Amazon acquired many TV shows and movies. Amazon also spent a big amount on some small budget movies that have excellent reviews. Amazon has over 80 million Prime members in the U. S while Netflix has After the success of Prime in the U. A brilliant move, which would ensure you do not leave them for their rivals.

Amazon has millions of songs in its library which they are offering to their prime members. Even for the better experience, Amazon integrated Alexa in its music app which can help you find the songs you are searching for. Amazon is opening small warehouses to support Prime Now and Amazon Fresh — its grocery delivery service.

In Germany where a rapid expansion in online grocery delivery is expected , Amazon has been running warehouse purchase plans to tap on the market opportunity. Its plans include investing heavily in expanding fulfillment centers and other logistics capabilities. Driving further growth in the number of sellers and packages going through Fulfilled by Amazon FBA is a key focus.

To strengthen the logistics and delivery network, Amazon announced developing an app to help truck drivers. Amazon hired aggressively for the project and announced to launch it in In November , they secretly launched the app , Relay. The app makes it easier for truck drivers to pick up and drop off packages at Amazon warehouses.

Besides, Amazon is also working on a second app which could connect truck drivers with cargos. The concept has multiple regulatory barriers. However, the situation may get better, as in October , the Trump Government issued an order giving local governments more authority to conduct tests of such new technologies.

Why is Amazon believed to be the most successful company of Future?

Amazon is experimenting with a new delivery service intended to make more products available for free two-day delivery and relieve overcrowding in its warehouses. The service, Seller Flex, began two years ago in India, and Amazon has been slowly marketing it to US merchants in preparation for a national expansion. The trial began this year on the West Coast with a broader rollout planned in Amazon will oversee pickup of packages from warehouses of third-party merchants selling goods on Amazon. In October , French supermarket operator Leclerc was approached by Amazon for a possible logistics partnership.

The partnership and other logistic investment became an immediate reason for other services fall as companies like UPS and FedEx share price dropped drastically. UPS shares fell as much as 2. FedEx dipped by 1. No doubt, Amazon has been filing patents on logistics but the activity took a sudden increase in with the filing of 78 patents that were focused on developing its logistics network, which is already far more than just a few years old. In the first two quarters of alone, the company closed eight deals which cost them an arm and a leg. During the last few years, Amazon made multiple acquisitions to strengthen its core e-commerce operations.

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