Modelling the impact of climate change on Tanzanian forests

Nature Forest Reserves in Tanzania and their importance for conservation

Since 1997 Tanzania has undertaken a process to identify and declare a network of Nature Forest Reserves (NFRs) with high biodiversity values, from within its existing portfolio of national Forest Reserves, with 16 new NFRs declared since 2015. The current network of 22 gazetted NFRs covered 948,871 hectares in 2023. NFRs now cover a range of Tanzanian habitat types, including all main forest types-wet, seasonal, and dry-as well as wetlands and grasslands. NFRs contain at least 178 of Tanzania's 242 endemic vertebrate species, of which at least 50% are threatened with extinction, and 553 Tanzanian endemic plant taxa (species, subspecies, and varieties), of which at least 50% are threatened. NFRs also support 41 single-site endemic vertebrate species and 76 single-site endemic plant taxa. Time series analysis of management effectiveness tracking tool (METT) data shows that NFR management effectiveness is increasing, especially where donor funds have been available. Improved management and investment have resulted in measurable reductions of some critical threats in NFRs. Still, ongoing challenges remain to fully contain issues of illegal logging, charcoal production, firewood, pole-cutting, illegal hunting and snaring of birds and mammals, fire, wildlife trade, and the unpredictable impacts of climate change. Increased tourism, diversified revenue generation and investment schemes, involving communities in management, and stepping up control measures for remaining threats are all required to create a network of economically self-sustaining NFRs able to conserve critical biodiversity values.

Impacts of anthropogenic climate change on tropical montane forests: an appraisal of the evidence

In spite of their small global area and restricted distributions, tropical montane forests (TMFs) are biodiversity hotspots and important ecosystem services providers, but are also highly vulnerable to climate change. To protect and preserve these ecosystems better, it is crucial to inform the design and implementation of conservation policies with the best available scientific evidence, and to identify knowledge gaps and future research needs. We conducted a systematic review and an appraisal of evidence quality to assess the impacts of climate change on TMFs. We identified several skews and shortcomings. Experimental study designs with controls and long-term (≥10 years) data sets provide the most reliable evidence, but were rare and gave an incomplete understanding of climate change impacts on TMFs. Most studies were based on predictive modelling approaches, short-term (<10 years) and cross-sectional study designs. Although these methods provide moderate to circumstantial evidence, they can advance our understanding on climate change effects. Current evidence suggests that increasing temperatures and rising cloud levels have caused distributional shifts (mainly upslope) of montane biota, leading to alterations in biodiversity and ecological functions. Neotropical TMFs were the best studied, thus the knowledge derived there can serve as a proxy for climate change responses in under-studied regions elsewhere. Most studies focused on vascular plants, birds, amphibians and insects, with other taxonomic groups poorly represented. Most ecological studies were conducted at species or community levels, with a marked paucity of genetic studies, limiting understanding of the adaptive capacity of TMF biota. We thus highlight the long-term need to widen the methodological, thematic and geographical scope of studies on TMFs under climate change to address these uncertainties. In the short term, however, in-depth research in well-studied regions and advances in computer modelling approaches offer the most reliable sources of information for expeditious conservation action for these threatened forests.

Spatio-temporal changes in chimpanzee density and abundance in the Greater Mahale Ecosystem, Tanzania

Species conservation and management require reliable information about animal distribution and population size. Better management actions within a species' range can be achieved by identifying the location and timing of population changes. In the Greater Mahale Ecosystem (GME), western Tanzania, deforestation due to the expansion of human settlements and agriculture, annual burning, and logging are known threats to wildlife. For one of the most charismatic species, the endangered eastern chimpanzee (Pan troglodytes schweinfurthii), approximately 75% of the individuals are distributed outside national park boundaries, requiring monitoring and protection efforts over a vast landscape of various protection statuses. These efforts are especially challenging when we lack data on trends in density and population size. To predict spatio-temporal chimpanzee density and abundance across the GME, we used density surface modeling, fitting a generalized additive model to a 10-year time-series data set of nest counts based on line-transect surveys. The chimpanzee population declined at an annual rate of 2.41%, including declines of 1.72% in riparian forests (from this point forward, forests), 2.05% in miombo woodlands (from this point forward, woodlands) and 3.45% in nonforests. These population declines were accompanied by ecosystem-wide declines in vegetation types of 1.36% and 0.32% per year for forests and woodlands, respectively; we estimated an annual increase of 1.35% for nonforests. Our model predicted the highest chimpanzee density in forests (0.86 chimpanzees/km² , 95% confidence intervals (CIs) 0.60-1.23; as of 2020), followed by woodlands (0.19, 95% CI 0.12-0.30) and nonforests (0.18, 95% CI 0.10-1.33). Although forests represent only 6% of the landscape, they support nearly one-quarter of the chimpanzee population (769 chimpanzees, 95% CI 536-1103). Woodlands dominate the landscape (71%) and therefore support more than a half of the chimpanzee population (2294; 95% CI 1420-3707). The remaining quarter of the landscape is represented by nonforests and supports another quarter of the chimpanzee population (750; 95% CI 408-1381). Given the pressures on the remaining suitable habitat in Tanzania, and the need of chimpanzees to access both forest and woodland vegetation to survive, we urge future management actions to increase resources and expand the efforts to protect critical forest and woodland habitat and promote strategies and policies that more effectively prevent irreversible losses. We suggest that regular monitoring programs implement a systematic random design to effectively inform and allocate conservation actions and facilitate interannual comparisons for trend monitoring, measuring conservation success, and guiding adaptive management.

Modeling the Climate Suitability of Northernmost Mangroves in China under Climate Change Scenarios

Mangroves are important wetland ecosystems on tropical and subtropical coasts. There is an urgent need to better understand how the spatial distribution of mangroves varies with climate change factors. Species distribution models can be used to reveal the spatial change of mangroves; however, global models typically have a horizontal resolution of hundreds of kilometers and more than 1 km, even after downscaling. In the present study, a maximum entropy model was used to predict suitable areas for the northernmost mangroves in China in the 2050s. An approach was proposed to improve the resolution and credibility of suitability predictions by incorporating land-use potential. Predictions were made based on two CMIP6 scenarios (i.e., SSP1-2.6 and SSP5-8.5). The results show that the northern edge of the natural mangrove distribution in China would migrate from 27.20° N to 27.39° N–28.15° N, and the total extent of suitable mangrove habitats would expand. By integrating 30 m resolution land-use data to refine the model’s predictions, under the SSP1-2.6 scenario, the suitable habitats of mangroves are predicted to be 13,435 ha, which would increase by 33.9% compared with the current scenario. Under the SSP5-8.5 scenario, the suitable area would be 23,120 ha, with an increased rate of 96.5%. Approximately 40–44% of the simulated mangrove patches would be adjacent to aquacultural ponds, cultivated, and artificial land, which may restrict mangrove expansion. Collectively, our results showed how climate change and land use could influence mangrove distributions, providing a scientific basis for adaptive mangrove habitat management despite climate change.

A Forest Monitoring System for Tanzania

Tropical forests provide essential ecosystem services related to human livelihoods. However, the distribution and condition of tropical forests are under significant pressure, causing shrinkage and risking biodiversity loss across the tropics. Tanzania is currently undergoing significant forest cover changes, but monitoring is limited, in part due to a lack of remote sensing knowledge, tools and methods. This study has demonstrated a comprehensive approach to creating a national-scale forest monitoring system using Earth Observation data to inform decision making, policy formulation, and combat biodiversity loss. A systematically wall-to-wall forest baseline was created for 2018 through the application of Landsat 8 imagery. The classification was developed using the extreme gradient boosting (XGBoost) machine-learning algorithm, and achieved an accuracy of 89% and identified 45.76% of the country’s area to be covered with forest. Of those forested areas, 45% was found within nationally protected areas. Utilising an innovative methodology based on a forest habitat suitability analysis, the forest baseline was classified into forest types, with an overall accuracy of 85%. Woodlands (open and closed) were found to make up 79% of Tanzania’s forests. To map changes in forest extent, an automated system for downloading and processing of the Landsat imagery was used along with the XGBoost classifiers trained to define the national forest extent, where Landsat 8 scenes were individually downloaded and processed and the identified changes summarised on an annual basis. Forest loss identified for 2019 was found to be 157,204 hectares, with an overall accuracy of 82%. These forest losses within Tanzania have already triggered ecological problems and alterations in ecosystem types and species loss. Therefore, a forest monitoring system, such as the one presented in this study, will enhance conservation programmes and support efforts to save the last remnants of Tanzania’s pristine forests.