Predicting the effects of climate change on sea turtle nesting habitat in Florida

Rising global temperatures threaten the survival of many plant and animal species. Having already risen at an unprecedented rate in the past century, temperatures are predicted to rise between 0.3 and 7.5C in North America over the next 100 years (Hawkes et al. 2007). Studies have documented the effects of climate warming on phenology (timing of seasonal activities), with observations of early arrival at breeding grounds, earlier ends to the reproductive season, and delayed autumnal migrations (Pike et al. 2006). In addition, for species not suited to the physiological demands of cold winter temperatures, increasing temperatures could shift tolerable habitats to higher latitudes (Hawkes et al. 2007). More directly, climate warming will impact thermally sensitive species like sea turtles, who exhibit temperature-dependent sexual determination. Temperatures in the middle third of the incubation period determine the sex of sea turtle offspring, with higher temperatures resulting in a greater abundance of female offspring. Consequently, increasing temperatures from climate warming would drastically change the offspring sex ratio (Hawkes et al. 2007). Of the seven extant species of sea turtles, three (leatherback, Kemp’s ridley, and hawksbill) are critically endangered, two (olive ridley and green) are endangered, and one (loggerhead) is threatened. Considering the predicted scenarios of climate warming and the already tenuous status of sea turtle populations, it is essential that efforts are made to understand how increasing temperatures may affect sea turtle populations and how these species might adapt in the face of such changes. In this analysis, I seek to identify the impact of changing climate conditions over the next 50 years on the availability of sea turtle nesting habitat in Florida given predicted changes in temperature and precipitation. I predict that future conditions in Florida will be less suitable for sea turtle nesting during the historic nesting season. This may imply that sea turtles will nest at a different time of year, in more northern latitudes, to a lesser extent, or possibly not at all. It seems likely that changes in temperature and precipitation patterns will alter the distribution of sea turtle nesting locations worldwide, provided that beaches where the conditions are suitable for nesting still exist. Hijmans and Graham (2006) evaluate a range of climate envelope models in terms of their ability to predict species distributions under climate change scenarios. Their results suggested that the choice of species distribution model is dependent on the specifics of each individual study. Fuller et al. (2008) used a maximum entropy approach to model the potential distribution of 11 species in the Arctic Coastal Plain of Alaska under a series of projected climate scenarios. Recently, Pike (in press) developed Maxent models to investigate the impacts of climate change on green sea turtle nest distribution and timing. In each of these studies, a set of environmental predictor variables (including climate variables), for which ‘current’ conditions are available and ‘future’ conditions have been projected, is used in conjunction with species occurrence data to map potential species distribution under the projected conditions. In this study, I will take a similar approach in mapping the potential sea turtle nesting habitat in Florida by developing a Maxent model based on environmental and climate data and projecting the model for future climate data. (PDF contains 5 pages)

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.

La pêche au chalut en Côte d'Ivoire. Maximum de rendement économique

This paper analyses the relations between effort and catch per unit effort of trawlers which worked in Côte d'Ivoire from Jan 1966 to Dec 1970. A fishing effort permitting to exploit fishery in the best rentability conditions is proposed.

Estimating the maximum sustainable yield of bonito (Sarda chiliensis, Scombridae) of northern Chile from monthly catch data

Monthly catch data of bonito Sarda chiliensis from northern Chile, from 1976 to 1989, were used to obtain a series of estimates of the Z-G parameter (i.e., total mortality minus the growth coefficient in weight). This series was then used to estimate a maximum sustainable yield of 4,500 t/year through a modified version of the surplus production model of J. Csirke and J. Caddy. The status of the fishery is discussed.

Maximum likelihood estimation of mortality and growth with individual variability from multiple length-frequency data

We consider estimation of mortality rates and growth parameters from length-frequency data of a fish stock and derive the underlying length distribution of the population and the catch when there is individual variability in the von Bertalanffy growth parameter L∞. The model is flexible enough to accommodate 1) any recruitment pattern as a function of both time and length, 2) length-specific selectivity, and 3) varying fishing effort over time. The maximum likelihood method gives consistent estimates, provided the underlying distribution for individual variation in growth is correctly specified. Simulation results indicate that our method is reasonably robust to violations in the assumptions. The method is applied to tiger prawn data (Penaeus semisulcatus) to obtain estimates of natural and fishing mortality.

An environmentally based growth model that uses finite difference calculus with maximum likelihood method: its application to the brackish water bivalve Corbicula japonica in Lake Abashiri, Japan

We present a growth analysis model that combines large amounts of environmental data with limited amounts of biological data and apply it to Corbicula japonica. The model uses the maximum-likelihood method with the Akaike information criterion, which provides an objective criterion for model selection. An adequate distribution for describing a single cohort is selected from available probability density functions, which are expressed by location and scale parameters. Daily relative increase rates of the location parameter are expressed by a multivariate logistic function with environmental factors for each day and categorical variables indicating animal ages as independent variables. Daily relative increase rates of the scale parameter are expressed by an equation describing the relationship with the daily relative increase rate of the location parameter. Corbicula japonica grows to a modal shell length of 0.7 mm during the first year in Lake Abashiri. Compared with the attain-able maximum size of about 30 mm, the growth of juveniles is extremely slow because their growth is less susceptible to environmental factors until the second winter. The extremely slow growth in Lake Abashiri could be a geographical genetic variation within C. japonica.

Centennial oscillation of the climatic crescendo during the last glacial maximum: evidence from Lake Estancia, central New Mexico

Sediments deposited in late Pleistocene Lake Estancia, central New Mexico, contain a paleoclimatic record that includes the last glacial maximum and deglacial episode. Stratigraphic reconstruction of an interval representing the highstand of the lake that occurred during the last glacial maximum reveals ~2000-, ~600-, and ~200-year oscillations in lake level and climate. Shifting position of the polar jetstream in response to expansion and contraction of the North American ice sheet may be partly responsible for the millenial-scale changes in Lake Estancia but probably does not explain the centennial-scale oscillations.

The California heat island: seasonal trends in maximum and minimum temperatures

This paper summarizes progress in an ongoing study of California's temperature trends. It supplements studies reported at PACLIM in 1984, 1986, and 1987. ... Objectives of this study are twofold: to examine and map the trends in maximum and minimum temperatures for the warm and cool seasons separately, and to examine regional differences in maximum and minimum temperature trends in California.

Climatology of the seasonal precipitation maximum in the western United States

The western United States is characterized by heterogeneous patterns of seasonal precipitation regimes due to the hierarchy of climatic controls that operate at different spatial scales. A climatology of intermonthly precipitation changes, using data from more than 4,000 stations including high-elevation sites, illustrate how different climatic controls explain the spatial distribution of the seasonal precipitation maximum. These results indicate that smaller-scale climatic controls must be considered along with larger-scale ones to explain patterns of spatial climate heterogeneity over mountainous areas. The results also offer important implications for scholars interested in assessing spatial climatic variations of the western United States at different timescales.

Estimation of maximum sustainable yield (MSY) of hilsa (Tenualosa ilisha Ham.) in the Meghna river of Bangladesh

MSY per recruit of Tenualosa ilisha in the Meghna river was predicted as 112 g per recruit at the F(msy)=0.6/yr and at T(c)=0.6/yr. But Y/R=95 g per recruit was obtained at the existing fishing level, F=1.14/yr and at T(c)=0.6/yr. Existing F level was nearly double than the F(msy) level. Fishing pressure should be reduced immediately from F=1.14/yr to F(msy)=0.6/yr. F(msy)=1.14/yr was the same at first capture, T(c)=1.0, 1.2 and 1.4/yr, and MSY could be obtained as 142 g, 162 g and 176 g per recruit respectively. It is easier to change the first capture age (Tc) rather than changing off level. So, hilsa fishery manager may adopt F(msy)=1.14/yr while age at first capture must be increased from T(c)=0.6/yr (3 cm size group) to T(c)=1.4/yr (25 cm size group), by which 1.8 times production could be increased than the present production. MSY also possible to obtain as 201 g and 210 g per recruit at F(msy)=2.0/yr and 4.0/yr at T(c)=1.7/yr and 1.9/yr respectively. Under both the situations, hilsa production could be increased 2 times than the present production. To obtain the MSY=210 g per recruit the fishing level could be increased up to F=4.0/yr at T(c)=1.9/yr (34 cm size group). Economic point of view, hilsa fishery managers may choose to obtain the economic MSY as 201 g per recruit at F(msy)=2.0/yr and T(c)=1.7yr (31 cm size group) in the Meghna river of Bangladesh.