A practical guide to cross-cultural and multi-sited data collection in the biological and behavioural sciences.

Researchers in the biological and behavioural sciences are increasingly conducting collaborative, multi-sited projects to address how phenomena vary across ecologies. These types of projects, however, pose additional workflow challenges beyond those typically encountered in single-sited projects. Through specific attention to cross-cultural research projects, we highlight four key aspects of multi-sited projects that must be considered during the design phase to ensure success: (1) project and team management; (2) protocol and instrument development; (3) data management and documentation; and (4) equitable and collaborative practices. Our recommendations are supported by examples from our experiences collaborating on the Evolutionary Demography of Religion project, a mixed-methods project collecting data across five countries in collaboration with research partners in each host country. To existing discourse, we contribute new recommendations around team and project management, introduce practical recommendations for exploring the validity of instruments through qualitative techniques during piloting, highlight the importance of good documentation at all steps of the project, and demonstrate how data management workflows can be strengthened through open science practices. While this project was rooted in cross-cultural human behavioural ecology and evolutionary anthropology, lessons learned from this project are applicable to multi-sited research across the biological and behavioural sciences.

Modelling animal social networks: New solutions and future directions.

Research Highlight: Ross, C. T., McElreath, R., & Redhead, D. (2023). Modelling animal network data in R using STRAND. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.14021. One of the most important insights in ecology over the past decade has been that the social connections among animals affect a wide range of ecological and evolutionary processes. However, despite over 20 years of study effort on this topic, generating knowledge from data on social associations and interactions remains fraught with problems. Redhead et al. present an R package-STRAND-that extends the current animal social network analysis toolbox in two ways. First, they provide a simple R interfaces to implement generative network models, which are an alternative to regression approaches that draw inference by simulating the data-generating process. Second, they implement these models in a Bayesian framework, allowing uncertainty in the observation process to be carried through to hypothesis testing. STRAND therefore fills an important gap for hypothesis testing using network data. However, major challenges remain, and while STRAND represents an important advance, generating robust results continues to require careful study design, considerations in terms of statistical methods and a plurality of approaches.

Predator mass mortality events restructure food webs through trophic decoupling.

Predators have a key role in structuring ecosystems1-4. However, predator loss is accelerating globally4-6, and predator mass-mortality events7 (MMEs)-rapid large-scale die-offs-are now emblematic of the Anthropocene epoch6. Owing to their rare and unpredictable nature7, we lack an understanding of how MMEs immediately impact ecosystems. Past predator-removal studies2,3 may be insufficient to understand the ecological consequences of MMEs because, in nature, dead predators decompose in situ and generate a resource pulse8, which could alter ensuing ecosystem dynamics by temporarily enhancing productivity. Here we experimentally induce MMEs in tritrophic, freshwater lake food webs and report ecological dynamics that are distinct from predator losses2,3 or resource pulses9 alone, but that can be predicted from theory8. MMEs led to the proliferation of diverse consumer and producer communities resulting from weakened top-down predator control1-3 and stronger bottom-up effects through predator decomposition8. In contrast to predator removals alone, enhanced primary production after MMEs dampened the consumer community response. As a consequence, MMEs generated biomass dynamics that were most similar to those of undisturbed systems, indicating that they may be cryptic disturbances in nature. These biomass dynamics led to trophic decoupling, whereby the indirect beneficial effects of predators on primary producers are lost and later materialize as direct bottom-up effects that stimulate primary production amid intensified herbivory. These results reveal ecological signatures of MMEs and demonstrate the feasibility of forecasting novel ecological dynamics arising with intensifying global change.

Quantifying uncertainty in inferences of landscape genetic resistance due to choice of individual-based genetic distance metric.

Estimates of gene flow resulting from landscape resistance inferences frequently inform conservation management decision-making processes. Therefore, results must be robust across approaches and reflect real-world gene flow instead of methodological artefacts. Here, we tested the impact of 32 individual-based genetic distance metrics on the robustness and accuracy of landscape resistance modelling results. We analysed three empirical microsatellite datasets and 36 simulated datasets that varied in landscape resistance and genetic spatial autocorrelation. We used ResistanceGA to generate optimised multi-feature resistance surfaces for each of these datasets using 32 different genetic distance metrics. Results of the empirical dataset demonstrated that the choice of genetic distance metric can have strong impacts on inferred optimised resistance surfaces. Simulations showed accurate parametrisation of resistance surfaces across most genetic distance metrics only when a small number of environmental features was impacting gene flow. Landscape scenarios with many features impacting gene flow led to a generally poor recovery of true resistance surfaces. Simulation results also emphasise that choosing a genetic distance metric should not be based on marginal R2 -based model fit. Until more robust methods are available, resistance surfaces can be optimised with different genetic distance metrics and the convergence of results needs to be assessed via pairwise matrix correlations. Based on the results presented here, high correlation coefficients across different genetic distance categories likely indicate accurate inference of true landscape resistance. Most importantly, empirical results should be interpreted with great caution, especially when they appear counter-intuitive in light of the ecology of a species.

One metric or many? Refining the analytical framework of landscape resistance estimation in individual-based landscape genetic analyses.

One of the allures of landscape genetics is the ability to leverage pairwise genetic distance metrics to infer how landscape features promote or constrain gene flow (i.e. landscape resistance surfaces). Critically, properly parameterized landscape resistance surfaces are foundational to applied conservation and management decisions. As such, there has been considerable effort expended assessing methods and metrics to estimate landscape resistance from genetic data (Balkenhol et al., Ecography, 32, 2009, 818; Peterman et al., Landsc. Ecol., 34, 2019, 2197; Shirk et al., Mol. Ecol. Resour., 17, 2017, 1308; Shirk et al., Mol. Ecol. Resour., 18, 2018, 55). Nonetheless, a primary challenge to assessing the effects of landscapes on gene flow is in the estimation of landscape resistance values, and this problem becomes increasingly challenging as more landscape features or land cover classes are considered. It quickly becomes infeasible to adequately assess the potential parameter space through manual or systematic assignment of resistance values. The development of ResistanceGA (Peterman, Methods Ecol. Evol., 9, 2018, 1638) provided a framework for using genetic algorithms to optimize landscape resistance values and identify the best statistical relationship between pairwise effective distances and genetic distances. ResistanceGA has seen extensive use in both population- and individual-based landscape genetic analyses. However, there has been relatively limited assessment of ResistanceGA's ability to identify the landscape features affecting gene flow (but see Peterman et al., Landsc. Ecol., 34, 2019, 2197; Winiarski et al., Mol. Ecol. Resour., 20, 2020, 1583) or the sensitivity of ResistanceGA results to the choice of genetic distance metric used. In the current issue of Molecular Ecology Resources, Beninde et al. (2023) aim to address these knowledge gaps by examining the impact of individual-based genetic distance measures on landscape genetic inference.

Carbon ecological security assessment based on the decoupling relationship between carbon balance pressure and ecological quality in Xuzhou City, China.

Building a carbon ecological security (CES) framework helps to scientifically evaluate and manage the regional carbon cycle and eco-environment and support regional ecological security patterns. This paper adopted the pressure-state-response-immune (PSRI) model and the carbon balance index method to evaluate the ecological quality and carbon balance pressure. Then, based on the decoupling model and the improved four-quadrant model, the CES framework was constructed to evaluate the changing trend of the CES of Xuzhou City from 2005 to 2020. The results showed that the carbon balance pressure of Xuzhou City showed a pattern of "low-high-low" from east to west, and most areas tended to have a carbon balance and surplus in 2020. The ecological quality showed an overall upward trend during the study period. Protection and restoration drove the response and immune index growth from 2010 to 2020. In the Thirteenth Five-Year Plan stage, the nine districts of Xuzhou City were in a stable decoupling state, and the overall decoupling process was ideal. The CES of districts showed individual differences in the general upward trend. The carbon balance pressure of Gulou and Quanshan Districts was the main factor restricting the districts' CES. Therefore, based on the empirical results, this research proposes relevant suggestions to enhance carbon ecological security to achieve regional green and low-carbon development.

Landscape ecological risk assessment of an ecological area in the Kubuqi desert based on Landsat remote sensing data.

Conducting ecological risk assessment of fragile ecological landscapes is a prerequisite for building an ecological security pattern and a necessary consideration for sustainable development. Engebei ecological demonstration zone is a typical ecologically fragile area located in the Kubuqi Desert. To explore the ecological status of Engebei, an ecological risk assessment model is used to assess its ecological risk, and the spatial correlation analysis is conducted based on the Moran index. The optimal grain size is obtained through grain size effect analysis, which is the foundation of landscape pattern analysis. The landscape ecological risk assessment model is constructed by the landscape indexes. Based on the division of small ecological risk zones, a spatial correlation analysis of ecological risks is conducted on Engebei. Results manifest that: (1) Overall, from 2005 to 2021, its spatial distribution features of landscape ecological risk level are relatively-high and high in the middle, and gradually reduce in the north-south direction, as shown below: the relatively-low ecological risk areas are widely spread, and the overall risk index decrease from 0.1944 to 0.1940; the area of low and high-level ecological risk areas show a decreasing trend, which decrease by 5.0102 km2 and 1.3132 km2 respectively; the area of relatively-low, middle, and relatively-high-level ecological risk areas increase by 0.2655 km2, 3.7803 km2, and 2.4852 km2, respectively. (2) The ecological risk value is correlated positively with spatial distribution, and the spatial aggregation forms are primarily low-low and high-high. (3) The ecological risk values in Engebei have a significant spatial correlation, and the spatial distribution shows a clustering effect, which is consistent with the spatial distribution. The study has certain reference value for the development and comprehensive regulation of ecological construction in Engebei, even in other ecologically fragile areas.

Spatiotemporal patterns and prediction of landscape ecological security in Xishuangbanna from 1996-2030.

In recent years, the landscape ecological security of Xishuangbanna in southwest China has become an essential factor affecting the cross-border ecological security in South Asia and Southeast Asia. Based on the change of land use in Xishuangbanna, with the help of "3S" technology, landscape ecology theory, and gray prediction model, the spatial and developmental trends of landscape ecological security in Xishuangbanna from 1996-2030 could be determined. In more than 20 years, the woodland landscape area in Xishuangbanna decreased, and the fragmentation of construction land has increased overall. In 1996, the overall landscape ecological safety was good, with 63.5% of the total area of grade I and II. In 2003, the proportion of the grade I and grade II areas decreased, with landscape ecological security problems appearing. In 2010, the overall landscape ecological security area reached 74.5%, the largest proportion in more than 20 years. The grade V area accounted for only 9% and was mainly distributed on the border of Menghai County and central Jinghong City. In 2017, The grade IV and V areas was further increased, and the ecological security problem intensified. The prediction results showed that from 2023 to 2030, the regions of grades I and II increased, but the proportion of level V regions increased. Furthermore, the grade IV transformed to grade V rapidly, reaching its highest value in more than 20 years. From 1996 to 2030, the landscape ecological security space significantly evolved, showing an evident "east-south" trend in movement and eventually shifting to the southeast.

Empirical evidence of widespread exaggeration bias and selective reporting in ecology.

In many scientific disciplines, common research practices have led to unreliable and exaggerated evidence about scientific phenomena. Here we describe some of these practices and quantify their pervasiveness in recent ecology publications in five popular journals. In an analysis of over 350 studies published between 2018 and 2020, we detect empirical evidence of exaggeration bias and selective reporting of statistically significant results. This evidence implies that the published effect sizes in ecology journals exaggerate the importance of the ecological relationships that they aim to quantify. An exaggerated evidence base hinders the ability of empirical ecology to reliably contribute to science, policy, and management. To increase the credibility of ecology research, we describe a set of actions that ecologists should take, including changes to scientific norms about what high-quality ecology looks like and expectations about what high-quality studies can deliver.

Joint species distribution models with imperfect detection for high-dimensional spatial data.

Determining the spatial distributions of species and communities is a key task in ecology and conservation efforts. Joint species distribution models are a fundamental tool in community ecology that use multi-species detection-nondetection data to estimate species distributions and biodiversity metrics. The analysis of such data is complicated by residual correlations between species, imperfect detection, and spatial autocorrelation. While many methods exist to accommodate each of these complexities, there are few examples in the literature that address and explore all three complexities simultaneously. Here we developed a spatial factor multi-species occupancy model to explicitly account for species correlations, imperfect detection, and spatial autocorrelation. The proposed model uses a spatial factor dimension reduction approach and Nearest Neighbor Gaussian Processes to ensure computational efficiency for data sets with both a large number of species (e.g., >100) and spatial locations (e.g., 100,000). We compared the proposed model performance to five alternative models, each addressing a subset of the three complexities. We implemented the proposed and alternative models in the spOccupancy software, designed to facilitate application via an accessible, well documented, and open-source R package. Using simulations, we found that ignoring the three complexities when present leads to inferior model predictive performance, and the impacts of failing to account for one or more complexities will depend on the objectives of a given study. Using a case study on 98 bird species across the continental US, the spatial factor multi-species occupancy model had the highest predictive performance among the alternative models. Our proposed framework, together with its implementation in spOccupancy, serves as a user-friendly tool to understand spatial variation in species distributions and biodiversity while addressing common complexities in multi-species detection-nondetection data.

The geophysical toolbox applied to forest ecosystems - A review.

Studying the forest subsurface is a challenge because of its heterogeneous nature and difficult access. Traditional approaches used by ecologists to characterize the subsurface have a low spatial representativity. This review article illustrates how geophysical techniques can and have been used to get new insights into forest ecology. Near-surface geophysics offers a wide range of methods to characterize the spatial and temporal variability of subsurface properties in a non-destructive and integrative way, each with its own advantages and disadvantages. These techniques can be used alone or combined to take advantage of their complementarity. Our review led us to define three topics how near-surface geophysics can support forest ecology studies: 1) detection of root systems, 2) monitoring of water quantity and dynamics, and 3) characterisation of spatial heterogeneity in subsurface properties at the stand level. The number of forest ecology studies using near-surface geophysics is increasing and this multidisciplinary approach opens new opportunities and perspectives for improving quantitative assessment of biophysical properties and exploring forest response to the environment and adaptation to climate change.

Community form, function and phylogenetic diversity respond differently across microhabitat and recovery gradients.

Research Highlight: Hoenle, P. O., Staab, M., Donoso, D. A., Argoti, A., & Blüthgen, N. (2023). Stratification and recovery time jointly shape ant functional reassembly in a neotropical forest. Journal of Animal Ecology, https://doi.org/10.1111/1365-2656.13896. Space, time and abiotic variation are primary axes across investigations of community ecology and disturbed ecosystems offer tractable systems for assessing their relative impact. While recovering forests can act as isolated case studies in understanding community assembly, it is not well understood how individual microhabitats respond to recovery and ultimately shape community attributes. Hoenle et al. (2023) leverage the ubiquity and microhabitat-specific diversity of ants across a gradient from active agricultural sites to old-growth forest and assess how recovery and stratification together shape communities. The authors find distinct stratification across phylogenetic, functional and trait diversity as forest recovery time increases, while also recovering unique recovery trajectories contingent on trait sampling. While stratified, phylogenetic and functional diversity did not increase along this recovery gradient. Ten out of 13 sampled traits were jointly influenced by both stratification and recovery time. In contrast to intuitive predictions, a majority of trait means converged throughout the recovery period. Results highlight the multifaceted nature of recovery-based community assembly and the capacity of multidimensional sampling to uncover surprising patterns in ecologically diverse lineages.

Unveiling the transition from niche to dispersal assembly in ecology.

A central goal in ecology is to understand what maintains species diversity in local communities. Classic ecological theory1,2 posits that niches dictate the maximum number of species that can coexist in a community and that the richness of observed species will be below this maximum only where immigration is very low. A new alternative theory3,4 is that niches, instead, dictate the minimum number of coexisting species and that the richness of observed species will usually be well above this because of ongoing immigration. We conducted an experimental test to discriminate between these two unified theories using a manipulative field experiment with tropical intertidal communities. We found, consistent with the new theory, that the relationship of species richness to immigration rate stabilized at a low value at low immigration rates and did not saturate at high immigration rates. Our results suggest that tropical intertidal communities have low niche diversity and are typically in a dispersal-assembled regime where immigration is high enough to overfill the niches. Observational data from other studies3,5 suggest that these conclusions may generalize to other ecological systems. Our new experimental approach can be adapted for other systems and be used as a 'niche detector' and a tool for assessing when communities are niche versus dispersal assembled.

Toward a cohesive understanding of ecological complexity.

Ecological systems are quintessentially complex systems. Understanding and being able to predict phenomena typical of complex systems is, therefore, critical to progress in ecology and conservation amidst escalating global environmental change. However, myriad definitions of complexity and excessive reliance on conventional scientific approaches hamper conceptual advances and synthesis. Ecological complexity may be better understood by following the solid theoretical basis of complex system science (CSS). We review features of ecological systems described within CSS and conduct bibliometric and text mining analyses to characterize articles that refer to ecological complexity. Our analyses demonstrate that the study of complexity in ecology is a highly heterogeneous, global endeavor that is only weakly related to CSS. Current research trends are typically organized around basic theory, scaling, and macroecology. We leverage our review and the generalities identified in our analyses to suggest a more coherent and cohesive way forward in the study of complexity in ecology.