Heated aquatic microcosms for climate change experiments

Ponds and shallow lakes are likely to be strongly affected by climate change, and by increase in environmental temperature in particular. Hydrological regimes and nutrient cycling may be altered, plant and animal communities may undergo changes in both composition and dynamics, and long-term and difficult to reverse switches between alternative stable equilibria may occur. A thorough understanding of the potential effects of increased temperature on ponds and shallow lakes is desirable because these ecosystems are of immense importance throughout the world as sources of drinking water, and for their amenity and conservation value. This understanding can only come through experimental studies in which the effects of different temperature regimes are compared. This paper reports design details and operating characteristics of a recently constructed experimental facility consisting of 48 aquatic microcosms which mimic the pond and shallow lake environment. Thirty-two of the microcosms can be heated and regulated to simulate climate change scenarios, including those predicted for the UK. The authors also summarise the current and future experimental uses of the microcosms.

Effect of environmental conditions on the distribution of Pacific mackerel (Scomber japonicus) larvae in the California Current system

We modeled the probability of capturing Pacif ic mackerel (Scomber japonicus) larvae as a function of environmental variables for the Southern California Bight (SCB) most years from 1951 through 2008 and Mexican waters offshore of Baja California from 1951 through 1984. The model exhibited acceptable fit, as indicated by the area under a receiver-operating-characteristic curve of 0.80 but was inconsistent with the zero catches that occurred frequently in the 2000s. Two types of spawners overlapped spatially within the survey area: those that exhibited peak spawning during April in the SCB at about 15.5°C and a smaller group that exhibited peak spawning in August near Punta Eugenia, Mexico, at 20°C or greater. The SCB generally had greater zooplankton than Mexican waters but less appropriate (lower) geostrophic f lows. Mexican waters generally exhibited greater predicted habitat quality than the SCB in cold years. Predicted quality of the habitat in the SCB was greater from the 1980s to 2008 than in the earlier years of the survey primarily because temperatures and geostrophic flows were more appropriate for larvae. However, stock size the previous year had a larger effect on predictions than any environmental variable, indicating that larval Pacific mackerel did not fully occupy the suitable habitat during most years.

Prediction of mesophotic coral distributions in the Au‘au Channel, Hawaii

The primary objective of this study was to predict the distribution of mesophotic hard corals in the Au‘au Channel in the Main Hawaiian Islands (MHI). Mesophotic hard corals are light-dependent corals adapted to the low light conditions at approximately 30 to 150 m in depth. Several physical factors potentially influence their spatial distribution, including aragonite saturation, alkalinity, pH, currents, water temperature, hard substrate availability and the availability of light at depth. Mesophotic corals and mesophotic coral ecosystems (MCEs) have increasingly been the subject of scientific study because they are being threatened by a growing number of anthropogenic stressors. They are the focus of this spatial modeling effort because the Hawaiian Islands Humpback Whale National Marine Sanctuary (HIHWNMS) is exploring the expansion of its scope—beyond the protection of the North Pacific Humpback Whale (Megaptera novaeangliae)—to include the conservation and management of these ecosystem components. The present study helps to address this need by examining the distribution of mesophotic corals in the Au‘au Channel region. This area is located between the islands of Maui, Lanai, Molokai and Kahoolawe, and includes parts of the Kealaikahiki, Alalākeiki and Kalohi Channels. It is unique, not only in terms of its geology, but also in terms of its physical oceanography and local weather patterns. Several physical conditions make it an ideal place for mesophotic hard corals, including consistently good water quality and clarity because it is flushed by tidal currents semi-diurnally; it has low amounts of rainfall and sediment run-off from the nearby land; and it is largely protected from seasonally strong wind and wave energy. Combined, these oceanographic and weather conditions create patches of comparatively warm, calm, clear waters that remain relatively stable through time. Freely available Maximum Entropy modeling software (MaxEnt 3.3.3e) was used to create four separate maps of predicted habitat suitability for: (1) all mesophotic hard corals combined, (2) Leptoseris, (3) Montipora and (4) Porites genera. MaxEnt works by analyzing the distribution of environmental variables where species are present, so it can find other areas that meet all of the same environmental constraints. Several steps (Figure 0.1) were required to produce and validate four ensemble predictive models (i.e., models with 10 replicates each). Approximately 2,000 georeferenced records containing information about mesophotic coral occurrence and 34 environmental predictors describing the seafloor’s depth, vertical structure, available light, surface temperature, currents and distance from shoreline at three spatial scales were used to train MaxEnt. Fifty percent of the 1,989 records were randomly chosen and set aside to assess each model replicate’s performance using Receiver Operating Characteristic (ROC), Area Under the Curve (AUC) values. An additional 1,646 records were also randomly chosen and set aside to independently assess the predictive accuracy of the four ensemble models. Suitability thresholds for these models (denoting where corals were predicted to be present/absent) were chosen by finding where the maximum number of correctly predicted presence and absence records intersected on each ROC curve. Permutation importance and jackknife analysis were used to quantify the contribution of each environmental variable to the four ensemble models.