ALBERT

Description: Although the impacts of nutrient pollution on coral reefs are well known, surprisingly, no statistical relationships have ever been established between water quality parameters, coral biodiversity and coral cover. Hong Kong provides a unique opportunity to assess this relationship. Here, coastal waters have been monitored monthly since 1986, at 76 stations, providing a highly spatially resolved water quality dataset including 68,903 data points. Moreover, a robust coral species richness ( S ) dataset is available from more than 100 surveyed locations, composed of 3,453 individual colonies’ observations, as well as a coral cover ( CC ) dataset including 85 sites. This wealth of data provides a unique opportunity to test the hypothesis that water quality, and in particular nutrients, drives coral biodiversity. The influence of water quality on S and CC was analyzed using GIS and multiple regression modeling. Eutrophication (as chlorophyll-a concentration; CHLA) was negatively correlated with S and CC , whereas physico-chemical parameters (DO and salinity) had no significant effect. The modeling further illustrated that PSM, DIN and DIP had a negative effect on S and on CC , however, the effect of nutrients was 1.5 to 2-fold greater. The highest S and CC occurred where CHLA 〈 2 μ g.L −1 , DIN 〈 2 μ M and DIP 〈 0.1 μ M. Where these values were exceeded, S and CC were significantly lower and no live corals were observed where CHLA 〉 15 μ g.L −1 , DIN 〉 9 μ M and DIP 〉 0.33 μ M. This study demonstrates the importance of nutrients over other water quality parameters in coral biodiversity loss and highlights the key role of eutrophication in shaping coral reef ecosystems. This work also provides ecological thresholds that may be useful for water quality guidelines and nutrient-mitigation policies. This article is protected by copyright. All rights reserved.

Description: Several studies have shown that satellite retrievals of solar-induced chlorophyll fluorescence (SIF) provide useful information on terrestrial photosynthesis or gross primary production (GPP). Here, we have incorporated equations coupling SIF to photosynthesis in a land surface model, the National Center for Atmospheric Research Community Land Model version 4 (NCAR CLM4) and have demonstrated its use as a diagnostic tool for evaluating the calculation of photosynthesis, a key process in a land surface model that strongly influences the carbon, water, and energy cycles. By comparing forward simulations of SIF, essentially as a byproduct of photosynthesis, in CLM4 with observations of actual SIF, it is possible to check whether the model is accurately representing photosynthesis and the processes coupled to it. We provide some background on how SIF is coupled to photosynthesis, describe how SIF was incorporated into CLM4, and demonstrate that our simulated relationship between SIF and GPP values are reasonable when compared with satellite (Greenhouse gases Observing SATellite; GOSAT) and in situ flux-tower measurements. CLM4 overestimates SIF in tropical forests, and we show that this error can be corrected by adjusting the maximum carboxylation rate (V max ) specified for tropical forests in CLM4. Our study confirms that SIF has the potential to improve photosynthesis simulation and thereby can play a critical role in improving land surface and carbon cycle models. This article is protected by copyright. All rights reserved.

Description: Climate data created from historic climate observations are integral to most assessments of potential climate change impacts, and frequently comprise the baseline period used to infer species-climate relationships. They are often also central to downscaling coarse resolution climate simulations from General Circulation Models (GCMs) in order to project future climate scenarios at ecologically relevant spatial scales. Uncertainty in these baseline data can be large, particularly where weather observations are sparse and climate dynamics are complex (e.g. over mountainous or coastal regions). Yet, importantly, this uncertainty is almost universally overlooked when assessing potential responses of species to climate change. Here we assessed the importance of historic baseline climate uncertainty for projections of species’ responses to future climate change. We built species distribution models (SDMs) for 895 African bird species of conservation concern, using six different climate baselines. We projected these models to two future periods (2040-2069, 2070-2099), using downscaled climate projections, and calculated species turnover and changes in species-specific climate suitability. We found that the choice of baseline climate data constituted an important source of uncertainty in projections of both species turnover and species-specific climate suitability, often comparable with, or more important than, uncertainty arising from the choice of GCM. Importantly, the relative contribution of these factors to projection uncertainty varied spatially. Moreover, when projecting SDMs to sites of biodiversity importance (Important Bird and Biodiversity Areas), these uncertainties altered site-level impacts, which could affect conservation prioritisation. Our results highlight that projections of species’ responses to climate change are sensitive to uncertainty in the baseline climatology. We recommend that this should be considered routinely in such analyses. This article is protected by copyright. All rights reserved.

Description: Anthropogenic climate change compromises reef growth as a result of increasing temperatures and ocean acidification. Scleractinian corals vary in their sensitivity to these variables, suggesting species composition will influence how reef communities respond to future climate change. Because data are lacking for many species, most studies that model future reef growth rely on uniform scleractinian calcification sensitivities to temperature and ocean acidification. In order to address this knowledge gap, calcification of twelve common and understudied Caribbean coral species was measured for two months under crossed temperatures (27°C, 30.3°C) and CO 2 partial pressures ( p CO 2 ) (400, 900, 1300 μ atm). Mixed effects models of calcification for each species were then used to project community-level scleractinian calcification using Florida Keys reef composition data and IPCC AR5 ensemble climate model data. Three of the four most abundant species, Orbicella faveolata, Montastraea cavernosa, and Porites astreoides , had negative calcification responses to both elevated temperature and p CO 2 . In the business-as-usual CO 2 emissions scenario, reefs with high abundances of these species had projected end-of-century declines in scleractinian calcification of 〉50% relative to present-day rates. Siderastrea siderea , the other most-common species, was insensitive to both temperature and p CO 2 within the levels tested here. Reefs dominated by this species had the most stable end-of-century growth. Under more optimistic scenarios of reduced CO 2 emissions, calcification rates throughout the Florida Keys declined 〈20% by 2100. Under the most extreme emissions scenario, projected declines were highly variable among reefs, ranging 10 to 100%. Without considering bleaching, reef growth will likely decline on most reefs, especially where resistant species like S. siderea are not already dominant. This study demonstrates how species composition influences reef community responses to climate change and how reduced CO 2 emissions can limit future declines in reef calcification. This article is protected by copyright. All rights reserved.

Description: Accurate ground-based estimation of the carbon stored in terrestrial ecosystems is critical to quantifying the global carbon budget. Allometric models provide cost-effective methods for biomass prediction. But do such models vary with ecoregion or plant functional type? We compiled 15,054 measurements of individual tree or shrub biomass from across Australia to examine the generality of allometric models for prediction above-ground biomass. This provided a robust case study because Australia includes ecoregions ranging from arid shrublands to tropical rainforests, and has a rich history of biomass research, particularly in planted forests. Regardless of ecoregion, for five broad categories of plant functional type (shrubs; multi-stemmed trees; trees of the genus Eucalyptus and closely related genera; other trees of high wood density; and other trees of low wood density), relationships between biomass and stem diameter were generic. Simple power-law models explained 84-95% of the variation in biomass, with little improvement in model performance when other plant variables (height, bole wood density), or site characteristics (climate, age, management) were included. Predictions of stand-based biomass from allometric models of varying levels of generalisation (species-specific, plant functional type) were validated using whole-plot harvest data from 17 contrasting stands (range: 9 to 356 Mg ha −1 ). Losses in efficiency of prediction were 〈 1% if generalised models were used in place of species-specific models. Furthermore, application of generalised multi-species models did not introduce significant bias in biomass prediction in 92% of the 53 species tested. Further, overall efficiency of stand -level biomass prediction was 99%, with a mean absolute prediction error of only 13%. Hence, for cost-effective prediction of biomass across a wide range of stands, we recommend use of generic allometric models based on plant functional types. Development of new species-specific models is only warranted when gains in accuracy of stand-based predictions are relatively high (e.g. high-value monocultures). This article is protected by copyright. All rights reserved.

Description: Abstract As countries advance in greenhouse gas (GHG) accounting for climate change mitigation, consistent estimates of aboveground net biomass change (∆AGB) are needed. Countries with limited forest monitoring capabilities in the tropics and subtropics rely on IPCC 2006 default ∆AGB rates, which are values per ecological zone, per continent. Similarly, research on forest biomass change at large scale also make use of these rates. IPCC 2006 default rates come from a handful of studies, provide no uncertainty indications, and do not distinguish between older secondary forests and old‐growth forests. As part of the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, we incorporate ∆AGB data available from 2006 onwards, comprising 176 chronosequences in secondary forests and 536 permanent plots in old‐growth and managed/logged forests located in 42 countries in Africa, North and South America, and Asia. We generated ∆AGB rate estimates for younger secondary forests (≤20 years), older secondary forests (〉20 years and up to 100 years) and old‐growth forests, and accounted for uncertainties in our estimates. In tropical rainforests, for which data availability was the highest, our ∆AGB rate estimates ranged from 3.4 (Asia) to 7.6 (Africa) Mg ha‐1 yr‐1 in younger secondary forests, from 2.3 (North and South Ameri09ca) to 3.5 (Africa) Mg ha‐1 yr‐1 in older secondary forests, and 0.7 (Asia) to 1.3 (Africa) Mg ha‐1 yr‐1 in old‐growth forests. We provide a rigorous and traceable refinement of the IPCC 2006 default rates in tropical and subtropical ecological zones, and identify which areas require more research on ∆AGB. In this respect, this study should be considered as an important step towards quantifying the role of tropical and subtropical forests as carbon sinks with higher accuracy; our new rates can be used for large‐scale GHG accounting by governmental bodies, non‐governmental organisations and in scientific research. This article is protected by copyright. All rights reserved.

Description: Tropical forests are getting hotter—and in places drying—but how are they responding to the changing climate? A new analysis assesses long‐term records of thousands of tree species from across the vast Amazon basin. The team of 102 researchers discovered subtle but troubling changes in forest communities since the 1980s: trees preferring the wettest conditions and humid understorey are now in decline. With drought‐resistant plants gaining too slowly to track the changing climate Amazon forests appear increasingly vulnerable. Abstract Most of the planet's diversity is concentrated in the tropics, which includes many regions undergoing rapid climate change. Yet, while climate‐induced biodiversity changes are widely documented elsewhere, few studies have addressed this issue for lowland tropical ecosystems. Here we investigate whether the floristic and functional composition of intact lowland Amazonian forests have been changing by evaluating records from 106 long‐term inventory plots spanning 30 years. We analyse three traits that have been hypothesized to respond to different environmental drivers (increase in moisture stress and atmospheric CO2 concentrations): maximum tree size, biogeographic water‐deficit affiliation and wood density. Tree communities have become increasingly dominated by large‐statured taxa, but to date there has been no detectable change in mean wood density or water deficit affiliation at the community level, despite most forest plots having experienced an intensification of the dry season. However, among newly recruited trees, dry‐affiliated genera have become more abundant, while the mortality of wet‐affiliated genera has increased in those plots where the dry season has intensified most. Thus, a slow shift to a more dry‐affiliated Amazonia is underway, with changes in compositional dynamics (recruits and mortality) consistent with climate‐change drivers, but yet to significantly impact whole‐community composition. The Amazon observational record suggests that the increase in atmospheric CO2 is driving a shift within tree communities to large‐statured species and that climate changes to date will impact forest composition, but long generation times of tropical trees mean that biodiversity change is lagging behind climate change.

Description: Understanding the processes that determine aboveground biomass (AGB) in Amazonian forests is important for predicting the sensitivity of these ecosystems to environmental change and for designing and evaluating dynamic global vegetation models (DGVMs). AGB is determined by inputs from woody productivity (woody NPP) and the rate at which carbon is lost through tree mortality. Here, we test whether two direct metrics of tree mortality (the absolute rate of woody biomass loss and the rate of stem mortality) and/or woody NPP, control variation in AGB among 167 plots in intact forest across Amazonia. We then compare these relationships and the observed variation in AGB and woody NPP with the predictions of four DGVMs. The observations show that stem mortality rates, rather than absolute rates of woody biomass loss, are the most important predictor of AGB, which is consistent with the importance of stand size-structure for determining spatial variation in AGB. The relationship between stem mortality rates and AGB varies among different regions of Amazonia, indicating that variation in wood density and height/diameter relationships also influence AGB. In contrast to previous findings, we find that woody NPP is not correlated with stem mortality rates, and is weakly positively correlated with AGB. Across the four models, basin-wide average AGB is similar to the mean of the observations. However, the models consistently overestimate woody NPP, and poorly represent the spatial patterns of both AGB and woody NPP estimated using plot data. In marked contrast to the observations, DGVMs typically show strong positive relationships between woody NPP and AGB. Resolving these differences will require incorporating forest size structure, mechanistic models of stem mortality and variation in functional composition in DGVMs. This article is protected by copyright. All rights reserved.

Description: Abstract In the context of a changing climate, understanding the environmental drivers of marine megafauna distribution is important for conservation success. The extent of humpback whale breeding habitats and the impact of temperature variation on their availability are both unknown. We used 19 years of dedicated survey data from seven countries and territories of Oceania (1,376 survey days), to investigate humpback whale breeding habitat diversity and adaptability to climate change. At a fine scale (1 km resolution), seabed topography was identified as an important influence on humpback whale distribution. The shallowest waters close to shore or in lagoons were favored, although humpback whales also showed flexible habitat use patterns with respect to shallow offshore features such as seamounts. At a coarse scale (1° resolution), humpback whale breeding habitats in Oceania spanned a thermal range of 22.3–27.8°C in August, with interannual variation up to 2.0°C. Within this range, both fine and coarse scale analyses of humpback whale distribution suggested local responses to temperature. Notably, the most detailed dataset was available from New Caledonia (774 survey days, 1996–2017), where encounter rates showed a negative relationship to sea surface temperature, but were not related to the El Niño Southern Oscillation or the Antarctic Oscillation from previous summer, a proxy for feeding conditions that may impact breeding patterns. Many breeding sites that are currently occupied are predicted to become unsuitably warm for this species (〉28°C) by the end of the 21st century. Based on modeled ecological relationships, there are suitable habitats for relocation in archipelagos and seamounts of southern Oceania. Although distribution shifts might be restrained by philopatry, the apparent plasticity of humpback whale habitat use patterns and the extent of suitable habitats support an adaptive capacity to ocean warming in Oceania breeding grounds.