Kemp's Ridley Sea Turtle Population Dynamics in the Gulf of Mexico: Evaluation of Post-2010 Trends and Hypotheses Using an Age-Structured Simulation Model

Kemp’s ridley sea turtle (Lepidochelys kempii) is a critically-endangered species endemic to the Gulf of Mexico. Prior to 2010, based on nest counts at the primary nesting beach, the population appeared to be recovering. Since the lowest point in 1985, the population had exhibited an estimated annual growth rate of 19%. However, following a large mortality event between the 2009 and 2010 nesting seasons, population levels began to fluctuate. The present work describes development and use of an age-structured population simulation model to investigate plausibility of three hypothesized cause-effect relationships between the 2010 mortality event and subsequent population fluctuations. The baseline model was parameterized based on published Kemp’s ridley life history data and calibrated by adjusting the natural mortality rate of post-hatchlings (nph) such that simulated annual rate of population increase (λ) was within 2% of the observed λ based on nest counts from 1985 to 2009. Sensitivity analysis indicated λ was most sensitive to changes in nph. The calibrated model was modified to incorporate each of the three hypothesized effects of the 2010 mortality event: (1) a single year “pulse” effect increasing Kemp’s ridley mortality, (2) a multiple year “press” effect increasing Kemp’s ridley mortality, and (3) a density-dependent effect decreasing recruitment due to a lengthened remigration interval. Scenarios representing various versions of each of these hypotheses were simulated and tested based on four criteria which characterized the population fluctuations observed from 2010 to 2014. None of the scenarios representing the “pulse” or “press” hypotheses satisfied all four hypothesis-testing criteria. Two scenarios representing the “density-dependent remigration” hypothesis satisfied all four criteria: (1) an exponential and (2) an inverse logistic relationship between remigration rate and number of reproductive individuals. Only the inverse logistic relationship was tentatively validated via comparison of population projections to an independent dataset consisting of nest counts at the primary nesting beach from 2015 to 2017. Population projections to 2035 using the inverse logistic version suggested down-listing criteria may be achieved as early as 2019. The model was the first of its kind to test these hypotheses and should prove useful to management professionals considering conservation strategies for Kemp’s ridley sea turtles

Kemp's Ridley Sea Turtle Population Dynamics in the Gulf of Mexico: Evaluation of Post-2010 Trends and Hypotheses Using an Age-Structured Simulation Model

Kemp’s ridley sea turtle (Lepidochelys kempii) is a critically-endangered species endemic to the Gulf of Mexico. Prior to 2010, based on nest counts at the primary nesting beach, the population appeared to be recovering. Since the lowest point in 1985, the population had exhibited an estimated annual growth rate of 19%. However, following a large mortality event between the 2009 and 2010 nesting seasons, population levels began to fluctuate. The present work describes development and use of an age-structured population simulation model to investigate plausibility of three hypothesized cause-effect relationships between the 2010 mortality event and subsequent population fluctuations. The baseline model was parameterized based on published Kemp’s ridley life history data and calibrated by adjusting the natural mortality rate of post-hatchlings (nph) such that simulated annual rate of population increase (λ) was within 2% of the observed λ based on nest counts from 1985 to 2009. Sensitivity analysis indicated λ was most sensitive to changes in nph. The calibrated model was modified to incorporate each of the three hypothesized effects of the 2010 mortality event: (1) a single year “pulse” effect increasing Kemp’s ridley mortality, (2) a multiple year “press” effect increasing Kemp’s ridley mortality, and (3) a density-dependent effect decreasing recruitment due to a lengthened remigration interval. Scenarios representing various versions of each of these hypotheses were simulated and tested based on four criteria which characterized the population fluctuations observed from 2010 to 2014. None of the scenarios representing the “pulse” or “press” hypotheses satisfied all four hypothesis-testing criteria. Two scenarios representing the “density-dependent remigration” hypothesis satisfied all four criteria: (1) an exponential and (2) an inverse logistic relationship between remigration rate and number of reproductive individuals. Only the inverse logistic relationship was tentatively validated via comparison of population projections to an independent dataset consisting of nest counts at the primary nesting beach from 2015 to 2017. Population projections to 2035 using the inverse logistic version suggested down-listing criteria may be achieved as early as 2019. The model was the first of its kind to test these hypotheses and should prove useful to management professionals considering conservation strategies for Kemp’s ridley sea turtles

The State of the Art in Cartograms

Cartograms combine statistical and geographical information in thematic maps, where areas of geographical regions (e.g., countries, states) are scaled in proportion to some statistic (e.g., population, income). Cartograms make it possible to gain insight into patterns and trends in the world around us and have been very popular visualizations for geo-referenced data for over a century. This work surveys cartogram research in visualization, cartography and geometry, covering a broad spectrum of different cartogram types: from the traditional rectangular and table cartograms, to Dorling and diffusion cartograms. A particular focus is the study of the major cartogram dimensions: statistical accuracy, geographical accuracy, and topological accuracy. We review the history of cartograms, describe the algorithms for generating them, and consider task taxonomies. We also review quantitative and qualitative evaluations, and we use these to arrive at design guidelines and research challenges

Constructing continuous cartograms: a constraint-based approach

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references: p. 87-89.Issued also on microfiche from Lange Micrographics.We present a constraint-based automatic cartogram construction method that successfully achieves desired region areas while maintaining map topology and preserving essential shape cues to enable region recognition. Results are compared with a number of existing methods, and appear to be superior in both accuracy and preservation of shape recognition cues. A continuous area cartogram is a map transformation in which the map regions are resized relative to the geographic distribution of a data set. By spatially reflecting the data within the map base, the cartogram emphasizes each region's data instead of territorial land area, thereby providing a powerful tool for visualizing data distribution. There are two distinct and conflicting goals in the construction of cartograms: adjusting region sizes and retaining region shapes. Our Constraint-Based Method utilizes three foundational mechanisms to achieve these goals: alternating relaxation, constrained dynamics, and hierarchical resolution. We converge upon each goal in an alternating relaxation fashion, by achieving desired areas without regard to shape, and then utilizing constrained dynamics to attempt to hold the areas fixed while shape is restored. Through hierarchical resolution, we perform gross adjustments initially upon a coarsely resampled map and refinements later at progressively higher levels of detail