Animals respond to habitat alteration with changes in their behavior and physiology. These changes determine individual performance and thus precede changes in population size. They are therefore hypothesized to provide important insights into how animals cope with environmental change. Here, we investigated physiological and behavioral responses of a cooperatively breeding bird, the placid greenbul (Phyllastrephus placidus), in a severely fragmented tropical biodiversity hotspot and combined these data with remotely sensed (LiDAR) environmental data. We found that individuals had increased glucocorticoid hormone levels when breeding in territories with low native canopy cover or located within small fragments. However, when breeding with the help of subordinates, breeders in low quality territories had similar glucocorticoid levels as those in higher quality territories. Our study shows that sociality may impact how well animals cope with environmental change and contributes to our understanding of the role of glucocorticoid physiology and behavior in response to anthropogenic change.
The largest actively cycling terrestrial carbon pool, soil, has been disturbed during latest centuries by human actions through reduction of woody land cover. Soil organic carbon (SOC) content can reliably be estimated in laboratory conditions, but more cost-efficient and mobile techniques are needed for large-scale monitoring of SOC e.g. in remote areas. We demonstrate the capability of a mobile hyperspectral camera operating in the visible-near infrared wavelength range for practical estimation of soil organic carbon (SOC) and nitrogen content, to support efficient monitoring of soil properties. The 191 soil samples were collected in Taita Taveta County, Kenya representing an altitudinal gradient comprising five typical land use types: agroforestry, cropland, forest, shrubland and sisal estate. The soil samples were imaged using a Specim IQ hyperspectral camera under controlled laboratory conditions, and their carbon and nitrogen content was determined with a combustion analyzer. We use machine learning for estimating SOC and N content based on the spectral images, studying also automatic selection of informative wavelengths and quantification of prediction uncertainty. Five alternative methods were all found to perform well with a cross-validated R2 of approximately 0.8 and an RMSE of one percentage point, demonstrating feasibility of the proposed imaging setup and computational pipeline.
Impacts of land cover conversion have been studied well from the top-of-canopy level using satellite observations. Yet, the warming or cooling impacts of land cover and management change (LCMC) from below-canopy level remain less explored. Here, we studied the below-canopy temperature change from field to landscape level across multiple LCMC in southeastern Kenya. To study this, in situ microclimate sensors, satellite observations, and high-resolution below-canopy temperature modelling approaches were used. Our results show that from field to landscape scale, forest to cropland conversion, followed by thicket to cropland change, generate higher surface temperature warming than other conversion types. At field scale, tree loss increases the mean soil temperature (measured at 6 cm below ground) more than the mean below-canopy surface temperature but its impact on the diurnal temperature range was higher on surface temperature than soil temperature in both forest to cropland and thicket to cropland/grassland conversions. At landscape scale, compared with top-of-canopy land surface temperature warming, which was estimated at Landsat overpass time (â¼10:30 a.m.), forest to cropland conversion generates â¼3 °C higher below-canopy surface temperature warming. Land management change, through fencing of wildlife conservation areas and limiting mobility of mega browsers, can have an impact on woody cover and induce more below-canopy surface temperature warming than top-of-canopy in comparison with non-conservancy areas. These results indicate that human induced land changes can generate more below-canopy warming than inferred from top-of-canopy satellite observations. Together, the results highlight the importance of considering the climatic impacts of LCMC from both top-of-canopy and below-canopy level for effective mitigation of anthropogenic warming from land surface changes.
Introgressive hybridization is a process that enables gene flow across species barriers through the backcrossing of hybrids into a parent population. This may make genetic material, potentially including relevant environmental adaptations, rapidly available in a gene pool. Consequently, it has been postulated to be an important mechanism for enabling evolutionary rescue, that is the recovery of threatened populations through rapid evolutionary adaptation to novel environments. However, predicting the likelihood of such evolutionary rescue for individual species remains challenging. Here, we use the example of Zosterops silvanus, an endangered East African highland bird species suffering from severe habitat loss and fragmentation, to investigate whether hybridization with its congener Zosterops flavilateralis might enable evolutionary rescue of its Taita Hills population. To do so, we employ an empirically parameterized individual-based model to simulate the species' behaviour, physiology and genetics. We test the population's response to different assumptions of mating behaviour and multiple scenarios of habitat change. We show that as long as hybridization does take place, evolutionary rescue of Z. silvanus is likely. Intermediate hybridization rates enable the greatest long-term population growth, due to trade-offs between adaptive and maladaptive introgressed alleles. Habitat change did not have a strong effect on population growth rates, as Z. silvanus is a strong disperser and landscape configuration is therefore not the limiting factor for hybridization. Our results show that targeted gene flow may be a promising avenue to help accelerate the adaptation of endangered species to novel environments, and demonstrate how to combine empirical research and mechanistic modelling to deliver species-specific predictions for conservation planning.
We studied a previously almost unknown nocturnal mammal, an apparently undescribed species of tree hyrax (Dendrohyrax sp.) in the moist montane forests of Taita Hills, Kenya. We used thermal imaging to locate tree hyraxes, observe their behavior, and to identify woody plants most frequently visited by the selective browsers. We also documented acoustic behavior in forest fragments of different sizes. Data on calling type and frequency were analyzed together with lidar data to estimate population densities and to identify forest stand characteristics associated with large populations. Viable populations were found only in the largest forest fragments (> 90 ha), where tree hyraxes preferred most pristine forest stands with high, multilayered canopies. The estimated population sizes in smaller forest fragments were very limited, and hyraxes were heard to call only during late night and early morning hours, presumably in order to avoid detection. While we frequently recorded tree hyrax songs in the largest forest fragments, we almost never heard songs in the small ones. All remaining subpopulations of the Taita tree hyrax are under threat of human disturbance and further habitat deterioration. Conservation efforts should include protection of all remaining habitat patches, but also reforestation of former habitat is urgently needed.
Hyraxes , Trees , Animals , Ecosystem , Forests , Humans , Kenya
Tropical forests store 40-50 per cent of terrestrial vegetation carbon1. However, spatial variations in aboveground live tree biomass carbon (AGC) stocks remain poorly understood, in particular in tropical montane forests2. Owing to climatic and soil changes with increasing elevation3, AGC stocks are lower in tropical montane forests compared with lowland forests2. Here we assemble and analyse a dataset of structurally intact old-growth forests (AfriMont) spanning 44 montane sites in 12 African countries. We find that montane sites in the AfriMont plot network have a mean AGC stock of 149.4 megagrams of carbon per hectare (95% confidence interval 137.1-164.2), which is comparable to lowland forests in the African Tropical Rainforest Observation Network4 and about 70 per cent and 32 per cent higher than averages from plot networks in montane2,5,6 and lowland7 forests in the Neotropics, respectively. Notably, our results are two-thirds higher than the Intergovernmental Panel on Climate Change default values for these forests in Africa8. We find that the low stem density and high abundance of large trees of African lowland forests4 is mirrored in the montane forests sampled. This carbon store is endangered: we estimate that 0.8 million hectares of old-growth African montane forest have been lost since 2000. We provide country-specific montane forest AGC stock estimates modelled from our plot network to help to guide forest conservation and reforestation interventions. Our findings highlight the need for conserving these biodiverse9,10 and carbon-rich ecosystems.
Attitude , Carbon Sequestration , Carbon/analysis , Rainforest , Trees/metabolism , Tropical Climate , Africa , Biomass , Climate Change , Conservation of Natural Resources , Datasets as Topic , Geographic Mapping
In the Amazon rainforest, land use following deforestation is diverse and dynamic. Mounting evidence indicates that the climatic impacts of forest loss can also vary considerably, depending on specific features of the affected areas. The size of the deforested patches, for instance, was shown to modulate the characteristics of local climatic impacts. Nonetheless, the influence of different types of land use and management strategies on the magnitude of local climatic changes remains uncertain. Here, we evaluated the impacts of large-scale commodity farming and rural settlements on surface temperature, rainfall patterns, and energy fluxes. Our results reveal that changes in land-atmosphere coupling are induced not only by deforestation size but also, by land use type and management patterns inside the deforested areas. We provide evidence that, in comparison with rural settlements, deforestation caused by large-scale commodity agriculture is more likely to reduce convective rainfall and increase land surface temperature. We demonstrate that these differences are mainly caused by a more intensive management of the land, resulting in significantly lower vegetation cover throughout the year, which reduces latent heat flux. Our findings indicate an urgent need for alternative agricultural practices, as well as forest restoration, for maintaining ecosystem processes and mitigating change in the local climates across the Amazon basin.
Agriculture/statistics & numerical data , Climatic Processes , Conservation of Natural Resources/statistics & numerical data , Ecosystem
Animal spatial behaviour is often presumed to reflect responses to visual cues. However, inference of behaviour in relation to the environment is challenged by the lack of objective methods to identify the information that effectively is available to an animal from a given location. In general, animals are assumed to have unconstrained information on the environment within a detection circle of a certain radius (the perceptual range; PR). However, visual cues are only available up to the first physical obstruction within an animal's PR, making information availability a function of an animal's location within the physical environment (the effective visual perceptual range; EVPR). By using LiDAR data and viewshed analysis, we modelled forest birds' EVPRs at each step along a movement path. We found that the EVPR was on average 0.063% that of an unconstrained PR and, by applying a step-selection analysis, that individuals are 1.55 times more likely to move to a tree within their EVPR than to an equivalent tree outside it. This demonstrates that behavioural choices can be substantially impacted by the characteristics of an individual's EVPR and highlights that inferences made from movement data may be improved by accounting for the EVPR.
Birds , Ecosystem , Animals , Forests , Movement , Trees
Extensive research has focused on the response of vegetation to climate change, including potential mechanisms and resulting impacts. Although many studies have explored the relationship between vegetation and climate change in China, research on spatiotemporal distribution changes of climate regimes using natural vegetation as an indicator is still lacking. Further, limited information is available on the response of vegetation to shifts in China's regional climatic zones. In this study, we applied Mann-Kendall, and correlation analysis to examine the variabilities in temperature, precipitation, surface soil water, normalised difference vegetation index (NDVI), and albedo in China from 1982 to 2012. Our results indicate significant shifts in the distribution of Köppen-Geiger climate classes in China from 12.08% to 18.98% between 1983 and 2012 at a significance level of 0.05 (MK). The percentage areas in the arid and continental zones expanded at a rate of 0.004%/y and 0.12%/y, respectively, while the percentage area in the temperate and alpine zones decreased by -0.05%/y and - 0.07%/y. Sensitivity fitting results between simulated and observed changes identified temperature to be a dominant control on the dynamics of temperate (r2 = 0.98) and alpine (r2 = 0.968) zones, while precipitation was the dominant control on the changes of arid (r2 = 0.856) and continental (r2 = 0.815) zones. The response of the NDVI to albedo infers a more pronounced radiative response in temperate (r = -0.82, p < .01) and alpine (r = -0.476, p < .05) compared to arid and continental zones. Furthermore, we identified more pronounced monthly increasing trends in NDVI and soil water, corresponding to weak changes in albedo during vegetation growing periods. Our results suggest that climate zone shifting has considerable impacts on the vegetation in China and will have larger ecological impacts through radiative or non-radiative feedback mechanisms in future warming scenarios.
Bushlands (Acacia-Commiphora) constitute the largest and one of the most threatened ecosystems in East Africa. Although several studies have investigated the climatic impacts of land changes on local and global climate, the main focus has been on forest loss and the impacts of bushland clearing thus remain poorly understood. Measuring the impacts of bushland loss on local climate is challenging given that changes often occur at fragmented and small patches. Here, we apply high-resolution satellite imagery and land surface flux modeling approaches to unveil the impacts of bushland clearing on surface biophysical properties and its associated effects on surface energy balance and land surface temperature. Our results show that bushland clearing leads to an average reduction in evapotranspiration of 0.4 mm day-1. The changes in surface biophysical properties affected the surface energy balance components with different magnitude. The reduction in latent heat flux was stronger than other surface energy fluxes and resulted in an average net increase in daytime land surface temperature (LST) of up to 1.75 K. These results demonstrate the important impact of bushland-to-cropland conversion on the local climate, as they reveal increases in LST of a magnitude comparable to those caused by forest loss. This finding highlights the necessity of bushland conservation for regulating the land surface temperature in East Africa and, at the same time, warns of the climatic impacts of clearing bushlands for agriculture.
Crops, Agricultural , Africa, Eastern , Forests , Satellite Imagery
Mensuration of tree growth habits is of considerable importance for understanding forest ecosystem processes and forest biophysical responses to climate changes. However, the complexity of tree crown morphology that is typically formed after many years of growth tends to render it a non-trivial task, even for the state-of-the-art 3D forest mapping technology-light detection and ranging (LiDAR). Fortunately, botanists have deduced the large structural diversity of tree forms into only a limited number of tree architecture models, which can present a-priori knowledge about tree structure, growth, and other attributes for different species. This study attempted to recruit Hallé architecture models (HAMs) into LiDAR mapping to investigate tree growth habits in structure. First, following the HAM-characterized tree structure organization rules, we run the kernel procedure of tree species classification based on the LiDAR-collected point clouds using a support vector machine classifier in the leave-one-out-for-cross-validation mode. Then, the HAM corresponding to each of the classified tree species was identified based on expert knowledge, assisted by the comparison of the LiDAR-derived feature parameters. Next, the tree growth habits in structure for each of the tree species were derived from the determined HAM. In the case of four tree species growing in the boreal environment, the tests indicated that the classification accuracy reached 85.0%, and their growth habits could be derived by qualitative and quantitative means. Overall, the strategy of recruiting conventional HAMs into LiDAR mapping for investigating tree growth habits in structure was validated, thereby paving a new way for efficiently reflecting tree growth habits and projecting forest structure dynamics.
It has been suggested that above-ground biomass (AGB) inventories should include tree height (H), in addition to diameter (D). As H is a difficult variable to measure, H-D models are commonly used to predict H. We tested a number of approaches for H-D modelling, including additive terms which increased the complexity of the model, and observed how differences in tree-level predictions of H propagated to plot-level AGB estimations. We were especially interested in detecting whether the choice of method can lead to bias. The compared approaches listed in the order of increasing complexity were: (B0) AGB estimations from D-only; (B1) involving also H obtained from a fixed-effects H-D model; (B2) involving also species; (B3) including also between-plot variability as random effects; and (B4) involving multilevel nested random effects for grouping plots in clusters. In light of the results, the modelling approach affected the AGB estimation significantly in some cases, although differences were negligible for some of the alternatives. The most important differences were found between including H or not in the AGB estimation. We observed that AGB predictions without H information were very sensitive to the environmental stress parameter (E), which can induce a critical bias. Regarding the H-D modelling, the most relevant effect was found when species was included as an additive term. We presented a two-step methodology, which succeeded in identifying the species for which the general H-D relation was relevant to modify. Based on the results, our final choice was the single-level mixed-effects model (B3), which accounts for the species but also for the plot random effects reflecting site-specific factors such as soil properties and degree of disturbance.
Biomass , Forests , Models, Theoretical , Trees/growth & development , Africa, Western , Trees/anatomy & histology
Weather station measurements were used to force the SNOWPACK snow model and combined with reindeer herders' experiences to study the local and regional variations in snow conditions in a Finnish reindeer herding area for the 1981-2010 period. Winter conditions varied significantly between the four selected herding districts and between open and forest environments within the districts. The highest snow depths and densities, the thicknesses of ground ice, and the lengths of snow cover period were generally found in the northernmost districts. The snow depths showed the strongest regional coherence, whereas the thicknesses of ground ice were weakly correlated among the districts. The local variation in snow depths was higher than the regional variation and limits for rare or exceptional events varied notably between different districts and environments. The results highlight that forests diversify snow and foraging conditions, e.g., ground ice rarely forms simultaneously in different environments. Sufficient and diverse forest pastures are important during the critical winter season if reindeer herding is pursued on natural grazing grounds also in the future.
Animal Husbandry , Climate , Environment , Reindeer , Snow , Animals , Finland , Seasons
