Indicators of deforestation in the Southern Brazilian Pre-Amazon

In recent years, Amazonian deforestation has become a question of global concern. Deforestation in Amazon is a complex phenomenon in nature and has been related to traditional agriculture expansion. In this work, land use, socioeconomic and conservation indicators, combined with statistical analysis, were used to understand forces associated with patterns of deforestation. This approach was applied in Southern Brazilian Pre-Amazon in Mato Grosso State, which represents an extensive rain forest-savanna ecotone, located in the south border of Amazon biome. Based on data from the last two agricultural censuses (1995/1996–2006), we compared agricultural expansion in this area and Mato Grosso state. Results have shown that 85 % of state deforestation was concentrated in Southern Pre-Amazon and was closely related to increase in number of cattle and pasture area. PCA results pointed that population (92 %), number of cattle (86.5 %), pastures (84.2 %) and tractors (78.4 %) were variables with highest positive correlation to deforestation. It showed that GDP contributes to an individual axis and has a low correlation to deforestation (37.8 %). Conservation units and indigenous reserves also contribute to a single axis and were negatively correlated to temporary crops area. Results revealed a significant reduction in production and commercialization of extractive products in the region, revealing that the main, almost only conservation policy in Mato Grosso remains the creation of Special Areas. We suggest that further studies are necessary to screen development alternatives to simple cutting trees down. It is important to diversify strategies for deforestation control, and development aspects must be more seriously considered to reach a sustainable deforestation control policy.     

Historical carbon fluxes in the expanding deforestation frontier of Southern Brazilian Amazonia (1985–2012)

In tropical areas, pioneer occupation fronts steer the rapid expansion of deforestation, contributing to carbon emissions. Up-to-date carbon emission estimates covering the long-term development of such frontiers depend on the availability of high spatial–temporal resolution data. In this paper, we provide a detailed assessment of carbon losses from deforestation and potential forest degradation from fragmentation for one expanding frontier in the Brazilian Amazon. We focused on one of the Amazonia’s hot-spots of forest loss, the BR-163 highway that connects the high productivity agricultural landscapes in Mato Grosso with the exporting harbors of the Amazon. We used multi-decadal (1984–2012) Landsat-based time series on forested and non-forested area in combination with a carbon book-keeping model. We show a 36% reduction in 1984s biomass carbon stocks, which led to the emission of 611.5 TgCO₂ between 1985 and 1998 (43.6 TgCO₂ year⁻¹) and 959.8 TgCO₂ over 1999–2012 (68.5 TgCO₂ year⁻¹). Overall, fragmentation-related carbon losses represented 1.88% of total emissions by 2012, with an increasing relevance since 2004. We compared the Brazilian Space Agency deforestation assessment (PRODES) with our data and found that small deforestation polygons not captured by PRODES had increasing importance on estimated deforestation carbon losses since 2000. The comparative analysis improved the understanding of data-source-related uncertainties on carbon estimates and indicated disagreement areas between datasets that could be subject of future research. Furthermore, spatially explicit, annual deforestation and emission estimates like the ones derived from this study are important for setting regional baselines for REDD+ or similar payment for ecosystem services frameworks.

     

Can preferential credit programs speed up the adoption of low-carbon agricultural systems in Mato Grosso,Brazil? Results from bioeconomic microsimulation

The need to balance agricultural production and environmental protection shifted the focus of Brazilian land-use policy toward sustainable agriculture. In 2010, Brazil established preferential credit lines to finance investments into low-carbon integrated agricultural systems of crop, livestock and forestry. This article presents a simulation-based empirical assessment of integrated system adoption in the state of Mato Grosso, where highly mechanized soybean–cotton and soybean–maize double-crop systems currently prevail. We employ bioeconomic modeling to explicitly capture the heterogeneity of farm-level costs and benefits of adoption. By parameterizing and validating our simulations with both empirical and experimental data, we evaluate the effectiveness of the ABC Integration credit through indicators such as land-use change, adoption rates and budgetary costs of credit provision. Alternative scenarios reveal that specific credit conditions might speed up the diffusion of low-carbon agricultural systems in Mato Grosso.

     

Development of a land-use forecast tool for future water resources assessment: case study for the Mekong River 3S Sub-basins

Land-use change is one of the major factors that alter local and regional hydrology. For areas experiencing fast expansion of urban and agriculture areas, land-use changes often adversely affect stream flow and water resources at the local and watershed scale. The Sekong, Sesan, and Srepok (3S) Sub-basins are a part of the Lower Mekong River Basin and include land in Cambodia, Lao People’s Democratic Republic (Laos), and Viet Nam. The region is experiencing a dynamic land-use transition because of rapid changes in its economy, society, and environment. Major land-use changes include deforestation of native rain forest, expansion of agricultural and urban areas, and expansion of commercial plantation such as rubber trees. These land-use alterations have affected local and regional hydrologic processes, resulting in stream flow shortages during the dry season and flash flooding due to deforestation. In this research, deforestation in the 3S Sub-basins over the period 1993–1997 was analyzed using multi-logistic regression. The regression analysis indicated that density of agricultural cells within a 5-km radius from each forest cell and slope strongly affected the deforestation process. A land-use forecast model to simulate deforestation and urbanization sites was developed in GIS based on local land-use change trends. The model was applied to 2003 land use to forecast 2033 land use and future water demand, which was further compared with present stream flow measurements during the dry season at various places in the region. The entire approach from the land-use forecast to its impact assessment on stream flow could help local stakeholders understand watershed-wide future water resources risks and develop future water resources plans. With the 3S Sub-basins being used as a case study area, this article presents a land-use forecast tool; simulated 2033 land-use and water demand; and the estimation of the impact of the forecasted future water demand on the local stream flow.     

Energy and environmental challenges: bringing together economics and engineering (ICEE’17)

Agricultural landscapes of the southern Brazilian Amazon are the result of 80 years of governmental policies to install a powerful agricultural sector. Yet, this rapid expansion raised important environmental considerations especially with regard to deforestation. The agricultural frontier is thus now facing a huge challenge: to combine socioeconomic development with environmental conservation in the context of frontier expansion. Based on a conceptual model of the agricultural frontier, we review historical changes in environmental and development policies in the Brazilian state of Mato Grosso and emphasize their ambivalent trend to both encourage and control the progress of the frontier. We then extend this model with an integration stage where environmental governance and economic development evolve from competing to complementary concepts. At this stage, the efforts to slow down deforestation are accompanied with programs to promote new agricultural practices and support industrialization. Finally, we put into perspective this recent evolution with regard to the underlying reasons for changing the agricultural model, thus considering the agricultural frontier to be at a tipping point where first positive results need to be confirmed in spite of an unstable economic and political situation.     

Agricultural adjustment,population dynamics and forests redistribution in a subtropical watershed of NW Argentina

Patterns of land-use and land-cover change are usually grouped into one of two categories defined by the dominant trend: (1) deforestation resulting from expanding agriculture and (2) forest expansion, usually related to the abandonment of marginal lands. At regional scale, however, both processes can occur simultaneously even in the absence of net change. Given the focus on net change, such redistribution of agricultural and natural and seminatural lands has been generally overlooked. The interaction between agriculture modernization, human demography and complex topographic gradients of northwestern Argentina has resulted in processes of both forest recovery and deforestation, thus providing the opportunity to analyze patterns and driving forces of land-cover redistribution. We analyzed 20 years (1986–2006) of land-cover change in a subtropical watershed in relation to topographic and demographic variables. Although net forest change represented <1 %, forests redistribution affected 7 % of forest lands. There was a consistent geographic segregation of deforestation and forest recovery, with forests expanding over steep highlands and agriculture expanding over lowland irrigated areas. Population trends were not associated to forest expansion in lowlands but they explained 32 % of forest recovery in highlands. Highland forest expansion and lowland deforestation, respectively, imply conservation opportunities for humid montane forests and the environmental services they provide (e.g., watershed conservation) and threats for the conservation of dry forests and its biodiversity. Our study exemplifies the importance of land-use redistribution (rather than net change) with relevant environmental consequences at regional scale.     

Accounting for carbon stocks in models of land-use change: an application to Southern Yucatan

To assess the impact of land-use change on carbon stocks, we apply a new methodology, linking ecological and economic modeling, to southern Yucatan, Mexico. A spatial econometric multinomial logit model of ten land-cover classes is estimated (four primary forest categories, three secondary growth categories, an invasive species, and two agricultural land-cover categories), using satellite data on land cover, linked with census socioeconomic data and other biophysical spatial data from 2000. The analysis is novel in that it is the first attempt to link detailed satellite data on land use, with on-the-ground estimates of carbon stocks in a spatial econometric model of land use. The estimated multinomial logit model is then used with two scenarios of future economic growth (“low growth” and “high growth” changes in population, agricultural land use, market access, and education levels) in the region to predict land-cover changes resulting from the economic growth. The per hectare carbon (C) stocks in each land-cover class are derived from previously published estimates of biomass from field sampling across the study region. We consider aboveground-only, aboveground plus soil, transient and non-transient pools of carbon. These estimates are scaled up to the total area in each class according to the predictions of the model baseline and the two development scenarios. Subsequently, the changes in carbon stocks resulting from the predicted land-cover changes are calculated. Under the low growth scenario, carbon stocks declined by 5%; under the high growth scenario, losses were 12%. Including soil C, the proportional losses were lower, but the absolute amount lost was more than double (to 6 Tg C under the low and almost 15 Tg C under the high-growth scenario). This methodology could be further developed for applications in global change policy, such as payments for environmental services (PES) or reduction in emissions from deforestation and degradation (REDD).     

National and regional determinants of tropical deforestation in Colombia

Global tropical deforestation continues to occur at high rates despite political attention. National-level forest baselines are being established all over the world to guide the implementation of several policy mechanisms. However, identifying the direct and indirect drivers of deforestation and understanding the complexity of their interlinkages are often difficult. We first analyzed deforestation between 1990 and 2005 at the national level and found an annual deforestation rate of 0.62 %. Next, we performed separate analyses for four natural regions in Colombia and found annual deforestation rates between 0.42 and 1.92 %. Using general linear models, we identified several direct causes and underlying factors influencing deforestation at the national level: rural population density, cattle, protected areas, and slope. Significant differences in deforestation rates and causes were found across regions. In the Caribbean region, drivers of loss are urban population, unsatisfied basic needs, slope, and precipitation and four land use variables (illicit crops, pastures, cattle, and fires). In the Orinoco region, crops are the main driver of forest loss, and in the Amazonian region, deforestation is primarily due to fires related to the colonization front. Policy mechanisms will have to take into account regional patterns to successfully balance development and forest preservation in Colombia.     

Temporal patterns of road network development in the Brazilian Amazon

The Brazilian Amazon is a globally important ecosystem that is undergoing rapid development and land-use change. Roads are a key spatial determinant of land-use conversion and strongly influence the rates and patterns of habitat loss and represent a key component of models that attempt to predict the spatio-temporal patterns of Amazonian land-use change and the consequences of such changes. However, the spatio-temporal patterns of road network development are poorly understood and seldom quantified. Here, we used manually digitised satellite imagery at multiple temporal and spatial scales across the Brazilian Amazon to quantify and model the rate at which road networks are proliferating. We found that the road network grew by almost 17,000 km per year between 2004 and 2007. There was large spatial variation in road network density, with some municipalities having road densities as high as 0.5 km/km², and road network growth rates were highest in municipalities with an intermediate road network density. Simulations indicated that road network development within municipalities follows a logistic growth pattern through time, with most of the development occurring within a 39-year time period. This time period is similar to those of other boom and bust development dynamics observed in the Brazilian Amazon. Understanding the temporal patterns of road development will aid the development of better predictive land-use change models for the Amazon, given the key importance of roads as a predictor of deforestation in many existing models.     

Understanding deforestation in the southern Yucatán: insights from a sub-regional, multi-temporal analysis

The southern Yucatán has been identified as a deforestation hot spot. Land-change studies document the extent of forest loss at a regional scale, and case studies provide insights into the drivers of deforestation at the household level. Those studies have paid minimal attention to sub-regional analysis, especially to discrete land-management units above the household level. This analysis of upland forest change addresses the range of variation in deforestation among 96 ejidos (communal lands) and the Calakmul Biosphere Reserve, the two dominant land-tenure and land-management units in the region. Satellite imagery, census, and land-tenure data are used to establish the extent and location of deforestation patterns, and multivariate techniques are employed to identify biophysical and socioeconomic variables that explain such patterns. Results show that, for the 1984–1993 period, deforestation in the southern Yucatán was not as prevalent as implied by its hot spot designation. Three clusters of deforestation are identified. A logistic regression analysis establishes that size of forest holdings, population growth, and location in the precipitation gradient correlate with ejidos that experienced higher deforestation rates than the rest of the land-tenure units. For the 1993–2000 period, conservation programs and changes in the economic context of this “hollow frontier” contributed to reduce deforestation rates and extent. This analysis illustrates the spatio-temporal heterogeneity of much tropical forest change and caution that it should bring to simple formulations of modeling this change and prescribing policies to control it.     

Sketching sustainable land use in Europe by 2040: a multi-stakeholder participatory approach to elicit cross-sectoral visions

The continuously growing global demands on a finite land resource will require better strategic policies and management of trade-offs to avoid conflicts between different land-use sectors. Visions of the future can support strategic planning by stimulating dialogue, building a consensus on shared priorities and providing long-term targets. We present a novel approach to elicit stakeholder visions of future desired land use, which was applied with a broad range of experts to develop cross-sectoral visions in Europe. The approach is based on (i) combination of software tools and facilitation techniques to stimulate engagement and creativity; (ii) methodical selection of stakeholders; (iii) use of land attributes to deconstruct the multifaceted sectoral visions into land-use changes that can be clustered into few cross-sectoral visions, and (iv) a rigorous iterative process. Three cross-sectoral visions of sustainable land use in Europe in 2040 emerged from applying the approach in participatory workshops involving experts in nature conservation, recreation, agriculture, forestry, settlements, energy, and water. The three visions—Best Land in Europe, Regional Connected and Local Multifunctional—shared a wish to achieve a land use that is sustainable through multifunctionality, resource use efficiency, controlled urban growth, rural renewal and widespread nature. However, they differ on the scale at which land services are provided—EU-wide, regional or local—reflecting the land-sparing versus land-sharing debate. We discuss the usefulness of the approach, as well as the challenges posed and solutions offered by the visions to support strategic land-use planning.

     

Simulating the effects of urbanization,afforestation and cropland abandonment on a regional carbon balance: a case study for Central Germany

The newly developed model system HILLS is used to simulate recent (1990–2000) and future (up to 2020) changes in land use and carbon sequestration over Central Germany. HILLS is unique as that it integrates the spatially explicit land-use-change model LUC-Hesse with the dynamic ecosystem model Century under a GIS platform. With this new tool, the concurrent effects of urbanization, afforestation and cropland abandonment on regional carbon sequestration are analyzed for an exemplary “Business as Usual” scenario. During the simulation period, afforestation was estimated to sequester 880 Gg C and cropland abandonment 783 Gg C. Urbanization was estimated to release 336 Gg C formerly stored in soil organic matter and thereby offsets about 20% of the C sequestered by cropland abandonment and afforestation. The case study shows that urbanization can partly counteract the benefits of carbon sequestration resulting from other land-use changes and should be investigated in other carbon balances.     

Soil carbon stocks after conversion of Amazonian tropical forest to grazed pasture: importance of deep soil layers

Recent studies suggest that carbon (C) is stored in the topsoil of pastures established after deforestation. However, little is known about the long-term capacity of tropical pastures to sequester C in different soil layers after deforestation. Deep soil layers are generally not taken into consideration or are underestimated when C storage is calculated. Here we show that in French Guiana, the C stored in the deep soil layers contributes significantly to C stocks down to a depth of 100 cm and that C is sequestered in recalcitrant soil organic matter in the soil below a depth of 20 cm. The contribution of the 50–100 cm soil layer increased from 22 to 31 % with the age of the pasture. We show that long-term C sequestration in C4 tropical pastures is linked to the development of C3 species (legumes and shrubs), which increase both inputs of N into the ecosystem and the C:N ratio of soil organic matter. The deep soil under old pastures contained more C3 carbon than the native forest. If C sequestration in the deep soil is taken into account, our results suggest that the soil C stock in pastures in Amazonia would be higher with sustainable pasture management, in particular by promoting the development of legumes already in place and by introducing new species.     

Proximate causes of deforestation in the Bolivian lowlands: an analysis of spatial dynamics

Forests in lowland Bolivia suffer from severe deforestation caused by different types of agents and land use activities. We identify three major proximate causes of deforestation. The largest share of deforestation is attributable to the expansion of mechanized agriculture, followed by cattle ranching and small-scale agriculture. We utilize a spatially explicit multinomial logit model to analyze the determinants of each of these proximate causes of deforestation between 1992 and 2004. We substantiate the quantitative insights with a qualitative analysis of historical processes that have shaped land use patterns in the Bolivian lowlands to date. Our results suggest that the expansion of mechanized agriculture occurs mainly in response to good access to export markets, fertile soil, and intermediate rainfall conditions. Increases in small-scale agriculture are mainly associated with a humid climate, fertile soil, and proximity to local markets. Forest conversion into pastures for cattle ranching occurs mostly irrespective of environmental determinants and can mainly be explained by access to local markets. Land use restrictions, such as protected areas, seem to prevent the expansion of mechanized agriculture but have little impact on the expansion of small-scale agriculture and cattle ranching. The analysis of future deforestation trends reveals possible hotspots of future expansion for each proximate cause and specifically highlights the possible opening of new frontiers for deforestation due to mechanized agriculture. Whereas the quantitative analysis effectively elucidates the spatial patterns of recent agricultural expansion, the interpretation of long-term historic drivers reveals that the timing and quantity of forest conversion are often triggered by political interventions and historical legacies.     

Modeling changes in paddy rice sown areas in Asia

Paddy rice fields in Asia account for over 90% of global total rice cultivation area, and the major rice-producing countries of Asia account for over one-half of the world’s population. Monitoring and understanding the dynamic changes in paddy rice agriculture in Asia are very important for agricultural sustainability, food and water security, and greenhouse gas emissions. This paper presents a crop choice decision model that dynamically simulates future changes in sown areas of paddy rice in Asia. This model was developed under the framework of Action-in-Context (AiC) with the aim of understanding land users’ decisions on crop choices among a set of available alternatives using a crop utility function. Empirical validation for the model conducted after model construction indicated the reliability of the model for addressing the complexity of current agricultural land-use change and its capacity for investigating long-term scenarios in the future. Finally, the model was applied for future scenario analysis over a time frame of 30 years with 5-year increments, beginning from the year 2005. The simulation results provided insights into rates and trajectories of changes in Asian rice areas over the test period, with the resulting implications for future agricultural sustainability in Asia. These outcomes can improve understanding of projected land-use changes and explain their causes, locations and consequences, as well as providing support for land-use planning and policy making.     

Commodity production as restoration driver in the Brazilian Amazon? Pasture re-agro-forestation with cocoa (<Emphasis Type="Italic">Theobroma cacao</Emphasis>) in southern Pará

The increasing demand for agricultural commodities is a major cause of tropical deforestation. However, pressure is increasing for greater sustainability of commodity value chains. This includes the demand to establish new crop plantations and pasture areas on already deforested land so that new forest clearing for agriculture is minimized. Where tree crops are planted as part of agroforestry systems on deforested land, this amounts to a form of re-agro-forestation which can generate environmental benefits in addition to crop production. Here, we discuss a case where agroforestry systems based on cocoa (Theobroma cacao) are being established on crop and pasture land in the south of Pará state, Brazilian Amazon. The adoption of cocoa by farmers and ranchers of the region is stimulated by the coincidence of (1) favorable prospects for cocoa on the national and international markets including the expectation of a global cocoa supply gap; (2) environmental policies obliging land owners to reforest excess cleared land with native trees, with agroforests based on the native cocoa tree being an economically attractive option; and (3) biophysical conditions (especially soil fertility) favorable for growing cocoa in part of the region. We show that in the state of Pará at least 1.26 million hectares of naturally high-fertility soils in deforested areas outside legally protected and indigenous lands are potentially suitable for cocoa production with low agrochemical inputs, sufficient to make a significant contribution to closing the predicted supply gap. Their actual suitability depends on their state of degradation after years of pasture use and the availability of technologies and finance to convert them into tree crop agroforests. We discuss the significant environmental benefits of pasture re-agro-forestation with cocoa-based systems, including reduced emissions of up to 135 Mg of carbon per hectare compared to the historically common scenario of planting cocoa after forest clearing. We identify important research questions related to the scaling up of this practice and the maximization of its environmental benefits. We conclude that the coincidence of the afore-mentioned factors could drive a re-agro-forestation frontier in this part of the Amazon, with potential for positive outcomes in terms of commodity production while generating social and environmental benefits.     

Attributing changes in land cover using independent disturbance datasets: a case study of the Yucatan Peninsula,Mexico

Detailed observations of natural and anthropogenic disturbance events that impact forest structure and the distribution of carbon are essential to estimate changes in terrestrial carbon pools and the associated emissions and removals of greenhouse gasses. Recent advances in remote sensing approaches have resulted in annual and decadal estimates of land-cover change derived from observations using broad-scale moderate resolution imaging spectroradiometer (MODIS) 250 m–1 km imagery. These land-use change estimates, however, are often not attributed directly to a cause or activity and are not well validated, especially in tropical areas. Knowledge of the type of disturbance that caused the observed land-cover changes is important, however, for the quantification of the associated impacts on ecosystem carbon stocks and fluxes. In this paper, we provide estimates of the amount of forest land-cover change in a Mexican forested region and propose an approach for attributing the cause of the observed changes to the underlying disturbance driver. To do so, we collate geospatial and remote sensing data from a variety of sources to summarize statistics about the major disturbances within the Yucatan Peninsula, an “early action” region for the reduction of emissions from deforestation and degradation, from 2005 to 2010. We combine the datasets to develop rules to estimate the likely disturbances that caused the observed land-cover changes based on their spatially explicit location. Finally, we compare our observed disturbance rates to those detected using classified land-cover data derived from MODIS.     

Exploring the efficiency of bias corrections of regional climate model output for the assessment of future crop yields in Europe

Excessive summer drying and reduced growing season length are expected to reduce European crop yields in future. This may be partly compensated by adapted crop management, increased CO₂ concentration and technological development. For food security, changes in regional to continental crop yield variability may be more important than changes in mean yields. The assessment of changes in regional and larger scale crop variability requires high resolution and spatially consistent future weather, matching a specific climate scenario. Such data could be derived from regional climate models (RCMs), which provide changes in weather patterns. In general, RCM output is heavily biased with respect to observations. Due to the strong nonlinear relation between meteorological input and crop yields, the application of this biased output may result in large biases in the simulated crop yield changes. The use of RCM output only makes sense after sufficient bias correction. This study explores how RCM output can be bias corrected for the assessment of changes in European and subregional scale crop yield variability due to climate change. For this, output of the RCM RACMO of the Royal Netherlands Meteorological Institute was bias corrected and applied within the crop simulation model WOrld FOod STudies to simulate potential and water limited yields of three divergent crops: winter wheat, maize and sugar beets. The bias correction appeared necessary to successfully reproduce the mean yields as simulated with observational data. It also substantially improved the year-to-year variability of seasonal precipitation and radiation within RACMO, but some bias in the interannual variability remained. This is caused by the fact that the applied correction focuses on mean and daily variability. The interannual variability of growing season length, and as a consequence the potential yields too, appeared even deteriorated. Projected decrease in mean crop yields is well in line with earlier studies. No significant change in crop yield variability was found. Yet, only one RCM is analysed in this study, and it is recommended to extend this study with more climate models and a slightly adjusted bias correction taking into account the variability of larger time scales as well.     

Climate and human impact on lowland lake sedimentation in Central Coastal California: the record from c. 650 ad to the present

The limnological record of human impact on catchment land cover and on lake sedimentation during the historical period has been established for Pinto Lake in Central Coastal California. In addition, the sedimentary record of the ‘pre-impact’ condition preserves evidence of a climatic control on the nature of lake sedimentation. Chronological marker horizons have been determined using pollen data in combination with the documented land-use history and introductions of exotic species. Further chronological data have been determined using ¹⁴C and ¹³⁷Cs. The impact of Mexican and Euro-American immigrants and their ‘imported’ land-use practices is clearly reflected in an order of magnitude increase in the rate of lake sedimentation to c. 9 kg m⁻² year⁻¹ (c. 2 cm year⁻¹) between 1770 and 1850. Here, the occurrence of exotic plant species indicates disturbance as early as c. 1769–1797, whilst redwood deforestation between 1844 and 1860 represents the most significant human impact. Changes in the nature of sedimentation prior to this reveal a high degree of sensitivity to changes in precipitation where subtle decreases in lake level and the supply of runoff-derived mineral matter have resulted in two periods of organic lake sedimentation c. 650–900 and 1275–1750. Set against this background condition of high sensitivity, the dramatic impacts of Euro-American settlement are unsurprising. An erratum to this article can be found at