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Aim The dynamic nature of climate change diminishes the effectiveness of static approaches to nature conservation. Areas that were once suitable for species may no longer be suitable, and areas that are suitable now, may be unsuitable in the future. Despite increasing global awareness of the threats posed by climate change, it remains poorly accounted for in conservation programmes, such as translocation. In this study, we project changes in climate suitability for populations of ectothermic species that have been successfully established through translocation efforts. Location Biogeographical realms: Australasia, Holarctic, Palearctic and Nearctic. Methods We use species distribution models (SDMs) to project changes in macroclimatic suitability across 65 translocation recipient sites involving 38 ectothermic species. We consider optimistic (SSP126) and pessimistic (SSP370) scenarios of climate change for five general circulation models spanning three time horizons from 2021–2040 up to 2061–2080. Results Our models predict that at least 74% of recipient sites are projected to decline in climate suitability, regardless of the SSP scenario or time horizon. While recipient site suitability, scaled from 0 to 1 (low–high), was typically very high (>0.75, 39% of sites) under baseline climate conditions (1960–2010), models project a marked shift towards low suitability climates (<0.25, 40% of sites) by the middle of the century (2041–2060) onwards under the more pessimistic scenario. Relative to species' ranges, recipient sites located closer to the equator are projected to experience the most significant declines in suitability. Main Conclusions Our results call for greater consideration of spatiotemporal factors during the recipient site selection process, so that translocated populations are more strategically placed for long‐term persistence under climate change.

Projects to reduce emissions from deforestation and degradation (REDD) are designed to reduce carbon emissions through avoided deforestation and degradation, and in many cases, to produce additional community and biodiversity conservation co-benefits. While these co-benefits can be significant, quantifying conservation impacts has been challenging, and most projects use simple species presence to demonstrate positive biodiversity impact. Some of the same tools applied in the quantification of climate mitigation benefits have relevance and potential application to estimating co-benefits for biodiversity conservation. In western Tanzania, most chimpanzees live outside of national park boundaries, and thus face threats from human activity, including competition for suitable habitat. Through a case study of the Ntakata Mountains REDD project in western Tanzania, we demonstrate a combined application of deforestation modelling with species distribution models to assess forest conservation benefits in terms of avoided carbon emissions and improved chimpanzee habitat. The application of such tools is a novel approach that we argue permits the better design of future REDD projects for biodiversity co-benefits. This approach also enables project developers to produce the more manageable, accurate and cost-effective monitoring, reporting and verification of project impacts that are critical to verification under carbon standards.

Effective species conservation management relies on detailed species distribution data. For many species, such as chimpanzees (Pan troglodytes), distribution data are collected during ground surveys. For chimpanzees, such ground surveys usually focus on detection of the nests they build instead of detection of the chimpanzees themselves due to their low density. However, due to the large areas they still occur in, such surveys are very costly to conduct and repeat frequently to monitor populations over time. Species distribution models are more accurate if they include presence as well as absence data. Earlier studies used drones to determine chimpanzee presence using nests. In this study, therefore, we explored the use of drones to determine the absence of chimpanzee nests in areas we flew over on the edge of the chimpanzee distribution in western Tanzania. We conducted 13 flights with a fixed-wing drone and collected 3560 images for which manual inspection took 180 h. Flights were divided into a total of 746 25 m2 plots for which we determined the absence probability of nests. In three flights, we detected nests, in eight, absence was assumed based on a 95% probability criterion, and in two flights, nest absence could not be assumed. Our study indicates that drones can be used to cover relatively large areas to determine the absence of chimpanzees. To fully benefit from the usage of drones to determine the presence and absence of chimpanzees, it is crucial that methods are developed to automate nest detection in images.

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 andsupports 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.

Chimpanzees are declining at a rate of up to 6.5% per year in some parts of Africa due to human impacts. Effective conservation relies on accurate and reliable information on population density, distribution and connectivity. Yet, traditional line transect surveys are costly to conduct over large areas and particularly at sufficiently regular intervals to determine trends in abundance. Moreover, they often fail to identify critical areas for animal movement. Given the vast landscape across which chimpanzees are found, we need new methods that are time and cost efficient while providing precise and accurate data across broad spatial scales. This thesis explores the potential of multiple remote sensing technologies along with molecular methods to provide critical information on population distribution, density and connectivity across broad spatial and temporal scales. My research first investigated the potential of drones for chimpanzee population surveys in Tanzania. More specifically, I evaluated drone performance in detecting chimpanzee nests by comparing ground and aerial surveys in the Issa valley, western Tanzania. I found ground and aerial nest numbers to be correlated, with an average of 10% of nests observed from the ground detectable from the air. Although I highlight challenges in using drones for chimpanzee surveys, the study provides guidance for future investigations and emphasises the importance of contrasting background and high-resolution images. Next, using satellite imagery from 1973 and 2018 and a landcover projection for 2027, I model landscape connectivity change for chimpanzees within the Greater Mahale Ecosystem (GME), an area containing nearly all of Tanzanian’s chimpanzees. The model reveals a series of corridors allowing chimpanzee movement throughout the ecosystem, as well as a reduction of connectivity over time likely to continue through 2027. By identifying critical areas for chimpanzee movement, the model provides valuable guidance on where to focus conservation efforts. Finally, using two molecular markers (mitochondrial control region sequences and 10 microsatellite loci), I describe population structure and genetic diversity of Tanzania’s chimpanzees. My analyses confirm historical gene flow between Gombe National Park (GNP) and the GME but also suggest complete interruption of chimpanzee movements between the two areas in recent years. Both genetic markers suggest high genetic diversity with no evidence of inbreeding and a greater mitochondrial DNA diversity within GNP. This surprising result might be explained by potential gene flow with extra-park chimpanzees and evidence of Gombe females preference for genetically dissimilar mates. Results of this study resolve previous contrasting findings on connectivity between GNP and the GME and support the establishment of two conservation units. Together, these chapters demonstrate the diversity of non-invasive technologies that can be applied, not only to help chimpanzee conservation, but also any large-bodied species facing accelerated rates of anthropogenic disturbance.

The bone marrow (BM) niche impacts the progression of acute myeloid leukemia (AML) by favoring the chemoresistance of AML cells. Intimate interactions between leukemic cells and BM mesenchymal stromal cells (BM-MSCs) play key roles in this process. Direct intercellular communications between hematopoietic cells and BM-MSCs involve connexins, components of gap junctions. We postulated that blocking gap junction assembly could modify cell–cell interactions in the leukemic niche and consequently the chemoresistance. The comparison of BM-MSCs from AML patients and healthy donors revealed a specific profile of connexins in BM-MSCs of the leukemic niche and the effects of carbenoxolone (CBX), a gap junction disruptor, were evaluated on AML cells. CBX presents an antileukemic effect without affecting normal BM-CD34+ progenitor cells. The proapoptotic effect of CBX on AML cells is in line with the extinction of energy metabolism. CBX acts synergistically with cytarabine (Ara-C) in vitro and in vivo. Coculture experiments of AML cells with BM-MSCs revealed that CBX neutralizes the protective effect of the niche against the Ara-C-induced apoptosis of leukemic cells. Altogether, these results suggest that CBX could be of therapeutic interest to reduce the chemoresistance favored by the leukemic niche, by targeting gap junctions, without affecting normal hematopoiesis.

The original version of this Article omitted the following from the Acknowledgements: This research was also supported by grants to KZ (UL and L-CNRS). This has now been corrected in both the PDF and HTML versions of the Article.