Designing the next generation of climate adaptation research for development

Adaptation research has changed significantly in recent years as funders and researchers seek to encourage greater impact, ensure value for money and promote interdisciplinarity across the natural and social sciences. While these developments are inherently positive, they also bring fresh challenges. With this in mind, this paper presents an agenda for the next generation of climate adaptation research for development. The agenda is based on insights from a dialogue session held at the 2016 Adaptation Futures conference as well as drawing on the collective experience of the authors. We propose five key areas that need to be changed in order to meet the needs of future adaptation research, namely: increasing transparency and consultation in research design; encouraging innovation in the design and delivery of adaptation research programmes; demonstrating impact on the ground; addressing incentive structures; and promoting more effective brokering, knowledge management and learning. As new international funding initiatives start to take shape, we underscore the importance of learning from past experiences and scaling-up of successful innovations in research funding models.

     

Production of DMSP and DMS during a mesocosm study of an Emiliania huxleyi bloom: influence of bacteria and Calanus finmarchicus grazing

We investigated the influence of bacteria and metazooplankton on the production of dimethylsulfoniopropionate (DMSP) and dimethylsulfide (DMS) during blooms of Emiliania huxleyi (Lohmann) Hay and Mohler in seawater mesocosms. The phytoplankton succession was marked by the rapid collapse of an initial Skeletonema costatum (Greville) Cleve bloom followed by a small E. huxleyi bloom. The collapse of the diatom bloom was accompanied by an increase in concentrations of dissolved DMSP (DMSP_d) and bacterial abundance and activity (as determined by the thymidine incorporation technique). The increase in bacterial activity was followed by a rapid decrease in DMSP_d concentrations which remained low for the rest of the experiment, even during the subsequent collapse of the E. huxleyi blooms. The absence of DMSP_d and DMS peaks during the declining phase of the E. huxleyi blooms was attributed to the high bacterial activity prevailing at that time. The influence of metazooplankton grazing on DMSP and DMS production was investigated by adding moderate (24 mg dry weight m^-3) and high (520 mg dry weight m^-3) concentrations of Copepodite Stage V and adults of Calanus finmarchicus to two of four filtered (200 m mesh net) enclosures during the E. huxleyi blooms. The addition of C. finmarchicus, even in high concentrations, had no apparent effect on the dynamics of E. huxleyi, suggesting that the copepods were not grazing significantly on nanophytoplankton. The addition of copepods in high concentrations favored an accumulation of chlorophyll a and particulate DMSP. These results suggest that copepods were preying on the herbivorous microzooplankton which, in turn, was controlling the biomass of nanophytoplankton. DMS production was also enhanced in the enclosure with maximum metazooplankton biomass, suggesting that the grazing of C. finmarchicus on microzooplankton containing DMSP may contribute to DMS production. These results provide strong support to the emerging idea that bacteria and metazooplankton grazing play a dominant role in determining the timing and magnitude of DMS pulses following phytoplankton blooms.     

A reflection on collaborative adaptation research in Africa and Asia

Regional Environmental Change - The reality of global climate change demands novel approaches to science that are reflective of the scales at which changes are likely to occur, and of the new forms...     

Neural Network Input Selection for Hydrological Forecasting Affected by Snowmelt1

Abstract: Snowmelt largely affects runoff in watersheds in Nordic countries. Neural networks (NN) are particularly attractive for streamflow forecasting whereas they rely at least on daily streamflow and precipitation observations. The selection of pertinent model inputs is a major concern in NNs implementation. This study investigates performance of auxiliary NN inputs that allow short‐term streamflow forecasting without resorting to a deterministic snowmelt routine. A case study is presented for the Rivière des Anglais watershed (700 km²) located in Southern Québec, Canada. Streamflow (Q), precipitations (rain R and snow S, or total P), temperature (T) and snow lying (A) observations, combined with climatic and snowmelt proxy data, including snowmelt flow (Q_SM) obtained from a deterministic model, were tested. NN implemented with antecedent Q and R produced the largest gains in performance. Introducing increments of A and T to the NNs further improved the performance. Long‐term averages, seasonal data, and Q_SM failed to improve the networks.     

Assessing conservation status with extensive but low-resolution data: Application of frequentist and Bayesian models to endangered Athabasca River rainbow trout

Use of extensive but low-resolution abundance data is common in the assessment of species at-risk status based on quantitative decline criteria under International Union for Conservation of Nature (IUCN) and national endangered species legislation. Such data can be problematic for 3 reasons. First, statistical power to reject the null hypothesis of no change is often low because of small sample size and high sampling uncertainty leading to a high frequency of type II errors. Second, range-wide assessments composed of multiple site-specific observations do not effectively weight site-specific trends into global trends. Third, uncertainty in site-specific temporal trends and relative abundance are not propagated at the appropriate spatial scale. A common result is the propensity to underestimate the magnitude of declines and therefore fail to identify the appropriate at-risk status for a species. We used 3 statistical approaches, from simple to more complex, to estimate temporal decline rates for a designatable unit (DU) of rainbow trout in the Athabasca River watershed in western Canada. This DU is considered a native species for purposes of listing because of its genetic composition characterized as >0.95 indigenous origin in the face of continuing introgressive hybridization with introduced populations in the watershed. Analysis of abundance trends from 57 time series with a fixed-effects model identified 33 sites with negative trends, but only 2 were statistically significant. By contrast, a hierarchical linear mixed model weighted by site-specific abundance provided a DU-wide decline estimate of 16.4% per year and a 3-generation decline of 93.2%. A hierarchical Bayesian mixed model yielded a similar 3-generation decline trend of 91.3% and the posterior distribution showed that the estimate had a >99% probability of exceeding thresholds for an endangered listing. We conclude that the Bayesian approach was the most useful because it provided a probabilistic statement of threshold exceedance in support of an at-risk status recommendation.