Addressing data integration challenges to link ecological processes across scales

Data integration is a statistical modeling approach that incorporates multiple data sources within a unified analytical framework. Macrosystems ecology – the study of ecological phenomena at broad scales, including interactions across scales – increasingly employs data integration techniques to expand the spatiotemporal scope of research and inferences, increase the precision of parameter estimates, and account for multiple sources of uncertainty in estimates of multiscale processes. We highlight four common analytical challenges to data integration in macrosystems ecology research: data scale mismatches, unbalanced data, sampling biases, and model development and assessment. We explain each problem, discuss current approaches to address the issue, and describe potential areas of research to overcome these hurdles. Use of data integration techniques has increased rapidly in recent years, and given the inferential value of such approaches, we expect continued development and wider application across ecological disciplines, especially in macrosystems ecology

Evaluating Monitoring Strategies and Habitat for Tortoises in the Sonoran Desert

Effective conservation requires efficient population monitoring, which can be challenging for rare species like the desert tortoise (Gopherus agassizii). We compared two alternative survey methods that can be used to monitor tortoise populations: distance sampling and site occupancy estimation. In 2005 and 2006 combined, we surveyed 120 1-km transects to estimate density and 40 3-ha plots with five presence-“absence” surveys to estimate occupancy of Sonoran desert tortoises in two mountain ranges in southern Arizona. We found that monitoring programs based on an occupancy framework were more efficient and had greater power to detect linear trends. We also found that habitat use by Sonoran desert tortoises was influenced most by slope and aspect, contrasting with patterns observed in the Mojave Desert. Given its efficiency, power, and ability to gauge changes in distribution while accounting for variation in detectability, occupancy offers a promising alternative for long-term monitoring of Sonoran desert tortoise populations

A Bayesian state-space model for seasonal growth of terrestrial vertebrates

The rate of somatic growth determines when individuals transition between life stages, which along with survival and reproduction, are principal factors governing the rate of population change. For short-lived species that inhabit seasonally dynamic environments, accounting for fluctuations in somatic growth is necessary to make reliable inferences about population dynamics. We describe a Bayesian, state-space formulation of a von Bertalanffy growth model that integrates a sinusoidal model to allow for seasonal fluctuations in growth while also accounting for individual heterogeneity and measurement error. We use this model to describe post-metamorphic growth of canyon treefrogs, Hyla arenicolor, based on capture-recapture data from 404 individuals over a two-year period. For simulated data where we assumed growth varies seasonally, our model provides unbiased estimates of growth rate, mean asymptotic length, standard deviation of individual asymptotic lengths, and date of maximum growth. For field data from canyon treefrogs, we found strong evidence of seasonal variation in growth, with maximum growth during the summer monsoon season. Growth rate of females was 19 % lower than males, although on average, females reached asymptotic lengths that were 15 % greater than males. Ignoring systematic Intra-annual variation in growth can bias inferences about population dynamics, particularly for fast-growing species that reproduce more than once per year or inhabit environments with strong seasonal signals. We present a straightforward approach for using repeated length measurements from individuals of unknown age to estimate growth while accounting for seasonality and individual heterogeneity, which are sources of variation common in many vertebrate populations.24 month embargo; published online: 12 February 2020This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Hydrologic variability governs population dynamics of a vulnerable amphibian in an arid environment.

Dynamics of many amphibian populations are governed by the distribution and availability of water. Therefore, understanding the hydrological mechanisms that explain spatial and temporal variation in occupancy and abundance will improve our ability to conserve and recover populations of vulnerable amphibians. We used 16 years of survey data from intermittent mountain streams in the Sonoran Desert to evaluate how availability of surface water affected survival and adult recruitment of a threatened amphibian, the lowland leopard frog (Lithobates yavapaiensis). Across the entire study period, monthly survival of adults ranged from 0.72 to 0.99 during summer and 0.59 to 0.94 during winter and increased with availability of surface water (Z = 7.66; P < 0.01). Recruitment of frogs into the adult age class occurred primarily during winter and ranged from 1.9 to 3.8 individuals/season/pool; like survival, recruitment increased with availability of surface water (Z = 3.67; P < 0.01). Although abundance of frogs varied across seasons and years, we found no evidence of a systematic trend during the 16-year study period. Given the strong influence of surface water on population dynamics of leopard frogs, conservation of many riparian obligates in this and similar arid regions likely depends critically on minimizing threats to structures and ecosystem processes that maintain surface waters. Understanding the influence of surface-water availability on riparian organisms is particularly important because climate change is likely to decrease precipitation and increase ambient temperatures in desert riparian systems, both of which have the potential to alter fundamentally the hydrology of these systems

Predicted number of recruits and monthly survival of adult lowland leopard frogs as a function of seasonal water availability between 1996 and 2011 in the Rincon Mountains, Arizona, USA.

<p>Predicted number of recruits into the adult stage class for an average-sized pool complex (A) and monthly survival (B) with 95% confidence intervals. We made predictions from a model of recruitment as a function of season, seasonal water availability, number of pools per complex, and connectivity, and a model of survival as a function of season and seasonal water availability (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125670#pone.0125670.t002" target="_blank">Table 2</a>). To predict the number of recruits, we held the number of pools per complex and connectivity values at the mean observed across all pool complexes (5 and 0.24, respectively).</p

Detection probabilities of adult lowland leopard frogs as a function of survey-specific water availability between 1996 and 2011 in the Rincon Mountains, Arizona, USA.

<p>Shaded areas represent 95% confidence intervals. We made predictions from a model of detection probability as a function of effort, survey-specific water availability, and date (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125670#pone.0125670.t002" target="_blank">Table 2</a>). We predicted detection probability for the midpoint of spring and fall sampling periods (approximately 7 June and 31 Oct) when observers surveyed pool complexes in their entirety (i.e., effort = 1).</p

Estimated abundance of adult lowland leopard frogs in the Rincon Mountains, Arizona, USA between 1996 and 2011.

<p>Estimated number of adult lowland leopard frogs, with 95% confidence intervals, in four canyons in the Rincon Mountains during spring (1 May–15 July) and fall (1 October–30 November). Estimates were obtained using empirical Bayes methods based on a model of recruitment as a function of season, seasonal water availability, number of pools per complex, and connectivity, and a model of survival as a function of season and seasonal water availability (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125670#pone.0125670.t002" target="_blank">Table 2</a>).</p

Estimated abundance of adult lowland leopard frogs in the Rincon Mountains, Arizona, USA between 1996 and 2011 as a function of regional precipitation for 12 months immediately preceding each sampling period.

<p>Estimated number of adult lowland leopard frogs, with 95% confidence intervals, in four canyons in the Rincon Mountains during spring (1 May–15 July) and fall (1 October–30 November). Estimates were obtained using empirical Bayes methods based on a model of recruitment as a function of season, seasonal water availability, number of pools per complex, and connectivity, and a model of survival as a function of season and seasonal water availability (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125670#pone.0125670.t002" target="_blank">Table 2</a>). We obtained regional precipitation data from an Arizona Meteorological Network weather station in Tucson, AZ located approximately 25 km from our study area. For spring sampling periods, we summed monthly precipitation totals from May–April; for fall sampling periods, we summed monthly precipitation totals from October–September. The solid line represents a linear regression of abundance on precipitation.</p

Addressing data integration challenges to link ecological processes across scales

Data integration is a statistical modeling approach that incorporates multiple data sources within a unified analytical framework. Macrosystems ecology – the study of ecological phenomena at broad scales, including interactions across scales – increasingly employs data integration techniques to expand the spatiotemporal scope of research and inferences, increase the precision of parameter estimates, and account for multiple sources of uncertainty in estimates of multiscale processes. We highlight four common analytical challenges to data integration in macrosystems ecology research: data scale mismatches, unbalanced data, sampling biases, and model development and assessment. We explain each problem, discuss current approaches to address the issue, and describe potential areas of research to overcome these hurdles. Use of data integration techniques has increased rapidly in recent years, and given the inferential value of such approaches, we expect continued development and wider application across ecological disciplines, especially in macrosystems ecology.30-3

Academic Ecosystems Must Evolve to Support a Sustainable Postdoc Workforce

The postdoctoral workforce comprises a growing proportion of the science, technology, engineering and mathematics (STEM) community, and plays a vital role in advancing science. Postdoc professional development, however, remains rooted in outdated realities. We propose enhancements to postdoc-centred policies and practices to better align this career stage with contemporary job markets and work life. By facilitating productivity, wellness and career advancement, the proposed changes will benefit all stakeholders in postdoc success—including research teams, institutions, professional societies and the scientific community as a whole. To catalyse reform, we outline recommendations for (1) skills-based training tailored to the current career landscape, and (2) supportive policies and tools outlined in postdoc handbooks. We also invite the ecology and evolution community to lead further progressive reform