Projected future climate change and Baltic Sea ecosystem management

Climate change is likely to have large effects on the Baltic Sea ecosystem. Simulations indicate 2–4 °C warming and 50–80 % decrease in ice cover by 2100. Precipitation may increase ~30 % in the north, causing increased land runoff of allochthonous organic matter (AOM) and organic pollutants and decreased salinity. Coupled physical–biogeochemical models indicate that, in the south, bottom-water anoxia may spread, reducing cod recruitment and increasing sediment phosphorus release, thus promoting cyanobacterial blooms. In the north, heterotrophic bacteria will be favored by AOM, while phytoplankton production may be reduced. Extra trophic levels in the food web may increase energy losses and consequently reduce fish production. Future management of the Baltic Sea must consider the effects of climate change on the ecosystem dynamics and functions, as well as the effects of anthropogenic nutrient and pollutant load. Monitoring should have a holistic approach, encompassing both autotrophic (phytoplankton) and heterotrophic (e.g., bacterial) processes.     

Metabolism of nC11 fatty acid fed to Trichoderma koningii and Penicillium janthinellum II: Production of intracellular and extracellular lipids

Abstract

Little is known about the fungal metabolism of nC₁₀ and nC₁₁ fatty acids and their conversion into lipids. A mixed batch culture of soil fungi, T. koningii and P. janthinellum, was grown on undecanoic acid (UDA), a mixture of UDA and potato dextrose broth (UDA+PDB), and PDB alone to examine their metabolic conversion during growth. We quantified seven intracellular and extracellular lipid classes using Iatroscan thin-layer chromatography with flame ionization detection (TLC-FID). Gas chromatography with flame ionization detection (GC-FID) was used to quantify 42 individual fatty acids. Per 150 mL culture, the mixed fungal culture grown on UDA+PDB produced the highest amount of intracellular (531 mg) and extracellular (14.7 mg) lipids during the exponential phase. The content of total intracellular lipids represented 25% of the total biomass-carbon, or 10% of the total biomass dry weight produced. Fatty acids made up the largest class of intracellular lipids (457 mg/150 mL culture) and they were synthesized at a rate of 2.4 mg/h during the exponential phase, and decomposed at a rate of 1.8 mg/h during the stationary phase, when UDA+PDB was the carbon source. Palmitic acid (C_16:0), stearic acid (C_18:0), oleic acid (C_18:1), linoleic acid (C_18:2) and vaccenic acid (C_18:1) accounted for >80% of the total intracellular fatty acids. During exponential growth on UDA+PDB, hydrocarbons were the largest pool of all extracellular lipids (6.5 mg), and intracellularly they were synthesized at a rate of 64 μg/h. The mixed fungal species culture of T. koningii and P. janthinellum produced many lipids for potential use as industrial feedstocks or bioproducts in biorefineries.     

Testing for thresholds of ecosystem collapse in seagrass meadows

Although the public desire for healthy environments is clear‐cut, the science and management of ecosystem health has not been as simple. Ecological systems can be dynamic and can shift abruptly from one ecosystem state to another. Such unpredictable shifts result when ecological thresholds are crossed; that is, small cumulative increases in an environmental stressor drive a much greater change than could be predicted from linear effects, suggesting an unforeseen tipping point is crossed. In coastal waters, broad‐scale seagrass loss often occurs as a sudden event associated with human‐driven nutrient enrichment (eutrophication). We tested whether the response of seagrass ecosystems to coastal nutrient enrichment is subject to a threshold effect. We exposed seagrass plots to different levels of nutrient enrichment (dissolved inorganic nitrogen) for 10 months and measured net production. Seagrass response exhibited a threshold pattern when nutrient enrichment exceeded moderate levels: there was an abrupt and large shift from positive to negative net leaf production (from approximately 0.04 leaf production to 0.02 leaf loss per day). Epiphyte load also increased as nutrient enrichment increased, which may have driven the shift in leaf production. Inadvertently crossing such thresholds, as can occur through ineffective management of land‐derived inputs such as wastewater and stormwater runoff along urbanized coasts, may account for the widely observed sudden loss of seagrass meadows. Identification of tipping points may improve not only adaptive‐management monitoring that seeks to avoid threshold effects, but also restoration approaches in systems that have crossed them.     

A systematic survey of the integration of animal behavior into conservation

The role of behavioral ecology in improving wildlife conservation and management has been the subject of much recent debate. We sought to answer 2 foundational questions about the current use of behavioral knowledge in conservation: To what extent is behavioral knowledge used in wildlife conservation and management, and how does the use of animal behavior differ among conservation fields in both frequency and types of use? We searched the literature for intersections between key fields of animal behavior and conservation and created a systematic heat map (i.e., graphical representation of data where values are represented as colors) to visualize relative efforts. Some behaviors, such as dispersal and foraging, were commonly considered (mean [SE] of 1147.38 [353.11] and 439.44 [108.85] papers per cell, respectively). In contrast, other behaviors, such as learning, social, and antipredatory behaviors were rarely considered (mean [SE] of 33.88 [7.62], 44.81 [10.65], and 22.69 [6.37] papers per cell, respectively). In many cases, awareness of the importance of behavior did not translate into applicable management tools. Our results challenge previous suggestions that there is little association between the fields of behavioral ecology and conservation and reveals tremendous variation in the use of different behaviors in conservation. We recommend that researchers focus on examining underutilized intersections of behavior and conservation themes for which preliminary work shows a potential for improving conservation and management, translating behavioral theory into applicable and testable predictions, and creating systematic reviews to summarize the behavioral evidence within the behavior‐conservation intersections for which many studies exist.     

Minimizing predation risk in a landscape of multiple predators: effects on the spatial distribution of African ungulates

Studies that focus on single predator-prey interactions can be inadequate for understanding antipredator responses in multi-predator systems. Yet there is still a general lack of information about the strategies of prey to minimize predation risk from multiple predators at the landscape level. Here we examined the distribution of seven African ungulate species in the fenced Karongwe Game Reserve (KGR), South Africa, as a function of predation risk from all large carnivore species (lion, leopard, cheetah, African wild dog, and spotted hyena). Using observed kill data, we generated ungulate-specific predictions of relative predation risk and of riskiness of habitats. To determine how ungulates minimize predation risk at the landscape level, we explicitly tested five hypotheses consisting of strategies that reduce the probability of encountering predators, and the probability of being killed. All ungulate species avoided risky habitats, and most selected safer habitats, thus reducing their probability of being killed. To reduce the probability of encountering predators, most of the smaller prey species (impala, warthog, waterbuck, kudu) avoided the space use of all predators, while the larger species (wildebeest, zebra, giraffe) only avoided areas where lion and leopard space use were high. The strength of avoidance for the space use of predators generally did not correspond to the relative predation threat from those predators. Instead, ungulates used a simpler behavioral rule of avoiding the activity areas of sit-and-pursue predators (lion and leopard), but not those of cursorial predators (cheetah and African wild dog). In general, selection and avoidance of habitats was stronger than avoidance of the predator activity areas. We expect similar decision rules to drive the distribution pattern of ungulates in other African savannas and in other multi-predator systems, especially where predators differ in their hunting modes.