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.

A common challenge for studying wildlife populations occurs when different survey methods provide inconsistent or incomplete inference on the trend, dynamics, or viability of a population. A potential solution to the challenge of conflicting or piecemeal data relies on the integration of multiple data types into a unified modeling framework, such as integrated population models (IPMs). IPMs are a powerful approach for species that inhabit spatially and seasonally complex environments. We provide guidance on exploiting the capabilities of IPMs to address inferential discrepancies that stem from spatiotemporal data mismatches. We illustrate this issue with analysis of a migratory species, the American Woodcock (Scolopax minor), in which individual monitoring programs suggest differing population trends. To address this discrepancy, we synthesized several long‐term data sets (1963–2015) within an IPM to estimate continental‐scale population trends, and link dynamic drivers across the full annual cycle and complete extent of the woodcock's geographic range in eastern North America. Our analysis reveals the limiting portions of the life cycle by identifying time periods and regions where vital rates are lowest and most variable, as well as which demographic parameters constitute the main drivers of population change. We conclude by providing recommendations for resolving conflicting population estimates within an integrated modeling approach, and discuss how strategies (e.g., data thinning, expert opinion elicitation) from other disciplines could be incorporated into ecological analyses when attempting to combine multiple, incongruent data types.

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The spatial dimension of sustainability transitions is central to understanding how systems change, yet current literature falls short when it comes to explaining transitions dynamics in context, and the central factors and alternative paths through which particular spaces move towards or away from sustainable development efforts. We conduct an exploratory factor analysis and a post-hoc cluster analysis of sustainability transition dynamics at a spatial level in 99 nations. Based on our analyses we establish a multi-dimensional measurement tool of nation sustainability that considers place, time and directionality. The analysis yielded four key dimensions -governance quality, industrial pollution, socio-environmental conditions and clean wealth creation -upon which we elaborated clusters of nations representing four distinct empirical types of transition pathways, crossroaders, compliers, athletes and laggards. In doing so we provide a holistic view of the phenomenon, which is relevant for both further theorizing and policy development. *Manuscript Click here to view linked References AbstractThe spatial dimension of sustainability transitions is central to understanding how systems change, yet current literature falls short when it comes to explaining transitions dynamics in context, and the central factors and alternative paths through which particular spaces move towards or away from sustainable development efforts. We conduct an exploratory factor analysis and a post-hoc cluster analysis of sustainability transition dynamics at a spatial level in 99 nations. Based on our analyses we establish a multi-dimensional measurement tool of nation sustainability that considers place, time and directionality. The analysis yielded four key dimensions -governance quality, industrial pollution, socio-environmental conditions and clean wealth creation -upon which we elaborated clusters of nations representing four distinct empirical types of transition pathways, crossroaders, compliers, athletes and laggards. In doing so we provide a holistic view of the phenomenon, which is relevant for both further theorizing and policy development.Keywords: Sustainability transitions; Nation-level sustainability measurement; Empirical typology; Sustainability pathways; Factor analysis Highlights  It conducts a quantitative exploration of sustainability transition dynamics at a spatial level  It identifies sets of distinct factors and paths of sustainable development at national level  It conducts an EFA of 22 factors at the national level  It identifies 4 key dimensions relevant to capturing transition dynamics  It conducts a cluster analysis of 99 countries and develops an empirical typology of paths towards SD  It recognizes several policy implications for the four distinct groups of nations  It provides a promising start for the identification of key variables in sustainability research 2 1

Dedicated long‐term monitoring at appropriate spatial and temporal scales is necessary to understand biodiversity losses and develop effective conservation plans. Wildlife monitoring is often achieved by obtaining data at a combination of spatial scales, ranging from local to broad, to understand the status, trends, and drivers of individual species or whole communities and their dynamics. However, limited resources for monitoring necessitates tradeoffs in the scope and scale of data collection. Careful consideration of the spatial and temporal allocation of finite sampling effort is crucial for monitoring programs that span multiple spatial scales. Here we evaluate the ability of five monitoring designs—stratified random, weighted effort, indicator unit, rotating panel, and split panel—to recover parameter values that describe the status (occupancy), trends (change in occupancy), and drivers (spatially varying covariate and an autologistic term) of wildlife communities at two spatial scales. Using an amphibian monitoring program that spans a network of US national parks as a motivating example, we conducted a simulation study for a regional community occupancy sampling program to compare the monitoring designs across varying levels of sampling effort (ranging from 10% to 50%). We found that the stratified random design outperformed the other designs for most parameters of interest at both scales and was thus generally preferable in balancing the estimation of status, trends, and drivers across scales. However, we found that other designs had improved performance in specific situations. For example, the rotating panel design performed best at estimating spatial drivers at a regional level. Thus, our results highlight the nuanced scenarios in which various design strategies may be preferred and offer guidance as to how managers can balance common tradeoffs in large‐scale and long‐term monitoring programs in terms of the specific knowledge gained. Monitoring designs that improve accuracy in parameter estimates are needed to guide conservation policy and management decisions in the face of broad‐scale environmental challenges, but the preferred design is sensitive to the specific objectives of a monitoring program.

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