A spatial multi-objective approach for modeling the ecosystem services and benefits of urban trees.

Trees provide important ecosystem services and benefits, with some, such as air pollutant and heat reductions, being linked to improved human health and well-being. With numerous tree planting initiatives being undertaken in different cities, careful thought needs to be put into considering the placement of trees, their beneficiaries as well as viable alternatives. Using a spatially distributed implementation of the i-Tree suite of ecosystem service models and mapping tools, this research estimated the current and future ecosystem services and benefits of a recent tree planting initiative within each census block group of the Bronx, NY for 2010 and for three 2030 tree cover scenarios (assuming different mortality rates). Results highlight how tree cover and benefits can be enhanced by maintaining existing canopy and ensuring the survival of newly planted trees. Traditional and non-traditional quantitative approaches of assessing environmental equity were used to establish whether there is an equitable distribution of ecosystem services derived from trees among various socio-demographic and socio- economic variables at the census block group level in the Bronx, NY. All ecosystem services and benefits appear to be unequally and inequitably distributed, with disadvantaged socio- demographic and socio-economic block groups receiving disproportionately lower ecosystem services from urban trees. The vast majority of the inequality is explained by variations within each socio-demographic and socio-economic subgroup rather than variations between subgroups. To guide future greening initiatives towards prioritizing planting locations that maximize multiple objectives, as well as the best areas to preserve urban forests and achieve equity, a spatially explicit methodology was used to develop a multi-objective decision support framework which was applied in the Bronx, NY to identify optimal planting locations. Overall, the findings of this research have the potential to guide more local and fine scale decision making regarding where to improve or protect tree cover and maximize the services and benefits of trees

Urban poverty in Zimbabwe

This paper breaks down the characteristics of urban poverty, which differs from rural poverty in its occurrence and depth. The urban poor face challenges such as high food prices and cost of accommodation, user fees for water and electricity and associated debt. The situation is compounded by high unemployment and low economic activity. Within urban areas there are variations and significant inequalities between the better off and the poor. A case study of Bulawayo illustrates the heterogeneity of the situation of households within one urban area. The paper ends with some recommendations for addressing urban poverty

Land reform migrations and forest resources management in Zimbabwe

PowerPoint presentationMeeting: Migration, Rural Livelihoods and Natural Resource Management, Hotel Entre Pinos, San Ignacio, Chalatenango, February 21-24, 2011The presentation provides information on three study sites in Chimanimani district, Manicaland province (Eastern Zimbabwe) representing a gradient of agrarian settlement models before and after Zimbabwe’s national independence. It outlines livelihoods, forest resources, and economic and environmental dynamics in the region in relation to forest management and migration. Management of forest resources in Chimanimani resettlement areas can be enhanced through improved local institutions and improved local capacity for forest management

Rewilding and restoring nature in a changing world

Overview of the PLoS ONE collection 'Rewilding and Restoration'

Habitat fragmentation, tree species diversity and land cover dynamics in a resettlement area in Chimanimani district of Zimbabwe : a spatio-temporal approach

Agricultural expansion into forests leads to habitat fragmentation and the creation of habitat patches that can only support a limited amount of biodiversity. Adopting theoretical frameworks which seek to understand biodiversity variations in agricultural landscapes currently undergoing rapid land cover changes mainly due to agricultural expansion into forests is important for promoting biodiversity-agriculture coexistence. The main objective of this thesis was to test whether the area-diversity prediction of the island biogeography theory can successfully be used to explain differences in tree species diversity among different woodland patch sizes in Nyabamba resettlement area of south-eastern Zimbabwe. We also tested whether cropland expansion drives land cover change in resettled landscapes of Zimbabwe. The area-diversity prediction of the island biogeography theory was used to explore whether woodland patches of different sizes had significant differences in tree species diversity. We also used remotely sensed data in a GIS-Markov chain modelling framework to determine historic as well as future predictions of land cover dynamics in the study area. Our results show that larger woodland patches had significantly higher species diversity than smaller woodland patches, indicating that the island biogeography theory can be used to explain tree species diversity differences in agriculturally fragmented woodlands. Results of historic land cover modelling and futuristic spatial predictions showed increases in cropland and wooded grassland accompanied by decreases in plantation and woodland. We also found that soil types and distance from rivers significantly influence land cover conversions. Results of this thesis imply that habitat fragmentation has a significant effect on tree species diversity and that cropland expansion is a major driver of land cover conversions in newly resettled agricultural landscapes

A review of machine learning and big data applications in addressing ecosystem service research gaps

Ecosystem services are essential for human well-being, but are currently facing many natural and anthropogenic threats. Modeling and mapping ecosystem services helps us mitigate, adapt to, and manage these pressures, but overall the field faces multiple major limitations. These include: 1) data availability, 2) understanding, estimation, and reporting of uncertainties, and 3) connecting socio-ecological aspects of ecosystem services. Recent technological advancements in machine learning coupled with rising availability of big data, offer an opportunity to overcome these challenges. We review studies utilizing machine learning and/or big data to overcome these limitations. We collect 56 papers that exemplify the current use of machine learning and big data to address the three identified gaps in the ecosystem service field. We find that although the use of these tools in ecosystem service research is relatively new, it is growing quickly. Big data can directly address data gaps, especially as new big data resources relevant to ecosystem service mapping become available (ex. social media data). Some properties of machine learning can also contribute to addressing data gaps in data sparse environments. Also, many machine learning algorithms can estimate and consider uncertainty, whereas big data can significantly increase sample size, reducing uncertainties in some situations. Some big data sources, like crowdsourced data, provide direct sources of social behaviors and preferences that relate to ecosystem service demand, thus allowing researchers to connect social and biophysical aspects of ecosystem services. Machine learning algorithms provide an effective and efficient tool for handling these large nonlinear socio-ecological datasets in tandem, giving researchers the ability to more realistically model and map ecosystem services without relying on oversimplified proxies or linear algorithms. Despite these opportunities, implementation is still lacking and limitations still hinder use

Poverty dynamics in Zimbabwe

A position paper on the dynamics of urban poverty in Zimbabwe

Using social media data and machine learning to map recreational ecosystem services

Crowdsourced geotagged social media data and machine learning approaches have emerged as promising tools for mapping ecosystem services, especially cultural ecosystem services that are difficult to assess. Here, we use recreation to show how social media data, machine learning, and spatial analysis techniques can improve our understanding of human-nature interactions and the mapping of recreational ecosystem services. We extracted 80,500 photographs taken in non-urban areas of the Tahoe Central Sierra Initiative project area in California between 2005 and 2019 that were posted to the photo sharing application Flickr and used these as a proxy for recreational visits to the area. Automated image content analysis was used to identify the objects and concepts in the photographs and uncover the types of nature experiences that are important to visitors. Additionally, variable importance, a Random Forest machine learning technique, was used to examine the environmental and landscape variables that drive recreation in the area and to create a classification model that predicts the recreation potential of the entire area based on important variables. The automated image content analysis identified 1,239 unique labels linked to recreation, with mountains, hills, and rocks being the most prominent features (22%). Our Random Forest model indicates that vegetation cover, land cover, elevation, smoke days, and landscape features are major drivers of recreation in the area and are of interest to visitors in the area. The model predicted that 25.9% of the area has the potential to support recreational visits. Most of these recreation potential areas are in protected areas (77.8%), predominantly in conifer forests (66%) and within national forest boundaries, especially the Tahoe National Forest area (37.6%). These results show that recreational ecosystem services vary across landscapes and illustrate the need for improved mapping approaches to determine the provision of ecosystem services in different places. The analysis provides novel insights into the various ways social media data and machine learning techniques can be powerful components of ecosystem service research and how they hold great potential for monitoring and informing management interventions on ecosystem service provision, especially in places with limited traditional onsite visitation data

Using social media data to estimate recreational travel costs: A case study from California

Understanding the economic value of ecosystem services is necessary to facilitate sustainable land use management, and to inform policy and decision making. However, valuing and monetizing ecosystem services remains challenging. Benefit-transfer and non-market valuation methods typically rely on administrative data and surveys, but this is time consuming, limited, and requires much more resources. Social media and other types of big data provide accessible and georeferenced data that can be incorporated into valuation approaches. We use recreation as an example and the Tahoe Central Sierra Initiative (TCSI) project area in California as a case study to explore the usefulness of such data in estimating travel costs that form an integral part of determining the value of recreational ecosystem services through the travel cost model. We estimated 6,951 person user days of recreation from 2,245 visitors who uploaded photographs to the Flickr photo-sharing application between 2005 and 2019. We used metadata from the images to infer visitor origins and estimate trip distance and costs of travel for visitors that took day trips (<500 miles (∼800 kms) roundtrip) to the area. Our results show that the most demand for recreational opportunities in the TCSI came from domestic visitors, particularly those from California and Nevada who took day trips. On average, visitors spent $156 per single day trip. The total cost of travel for recreational visits to the TCSI for the period was$491,500 (an average of $32,800 per year). However, when adjusted to align with actual visitation, the travel costs could range from$1.35 to $1.84 billion per year. Estimating recreational use and highlighting the travel cost for recreational opportunities illustrates how crowdsourced data can refine valuation approaches such as the widely used travel cost approach, which may fill in data gaps in valuing ecosystem services