Does scale matter? A systematic review of incorporating biological realism when predicting changes in species distributions

Background There is ample evidence that biotic factors, such as biotic interactions and dispersal capacity, can affect species distributions and influence species’ responses to climate change. However, little is known about how these factors affect predictions from species distribution models (SDMs) with respect to spatial grain and extent of the models. Objectives Understanding how spatial scale influences the effects of biological processes in SDMs is important because SDMs are one of the primary tools used by conservation biologists to assess biodiversity impacts of climate change. Data sources and study eligibility criteria We systematically reviewed SDM studies published from 2003–2015 using ISI Web of Science searches to: (1) determine the current state and key knowledge gaps of SDMs that incorporate biotic interactions and dispersal; and (2) understand how choice of spatial scale may alter the influence of biological processes on SDM predictions. Synthesis methods and limitations We used linear mixed effects models to examine how predictions from SDMs changed in response to the effects of spatial scale, dispersal, and biotic interactions. Results There were important biases in studies including an emphasis on terrestrial ecosystems in northern latitudes and little representation of aquatic ecosystems. Our results suggest that neither spatial extent nor grain influence projected climate-induced changes in species ranges when SDMs include dispersal or biotic interactions. Conclusions We identified several knowledge gaps and suggest that SDM studies forecasting the effects of climate change should: 1) address broader ranges of taxa and locations; and 1) report the grain size, extent, and results with and without biological complexity. The spatial scale of analysis in SDMs did not affect estimates of projected range shifts with dispersal and biotic interactions. However, the lack of reporting on results with and without biological complexity precluded many studies from our analysis

Does scale matter? A systematic review of incorporating biological realism when predicting changes in species distributions

Background There is ample evidence that biotic factors, such as biotic interactions and dispersal capacity, can affect species distributions and influence species’ responses to climate change. However, little is known about how these factors affect predictions from species distribution models (SDMs) with respect to spatial grain and extent of the models. Objectives Understanding how spatial scale influences the effects of biological processes in SDMs is important because SDMs are one of the primary tools used by conservation biologists to assess biodiversity impacts of climate change. Data sources and study eligibility criteria We systematically reviewed SDM studies published from 2003–2015 using ISI Web of Science searches to: (1) determine the current state and key knowledge gaps of SDMs that incorporate biotic interactions and dispersal; and (2) understand how choice of spatial scale may alter the influence of biological processes on SDM predictions. Synthesis methods and limitations We used linear mixed effects models to examine how predictions from SDMs changed in response to the effects of spatial scale, dispersal, and biotic interactions. Results There were important biases in studies including an emphasis on terrestrial ecosystems in northern latitudes and little representation of aquatic ecosystems. Our results suggest that neither spatial extent nor grain influence projected climate-induced changes in species ranges when SDMs include dispersal or biotic interactions. Conclusions We identified several knowledge gaps and suggest that SDM studies forecasting the effects of climate change should: 1) address broader ranges of taxa and locations; and 1) report the grain size, extent, and results with and without biological complexity. The spatial scale of analysis in SDMs did not affect estimates of projected range shifts with dispersal and biotic interactions. However, the lack of reporting on results with and without biological complexity precluded many studies from our analysis

Improving acoustic monitoring of biodiversity using deep learning-based source separation algorithms

Passive acoustic monitoring of the environment has been suggested as an effective tool for investigating the dynamics of biodiversity across spatial and temporal scales. Recent development in automatic recorders has allowed environmental acoustic data to be collected in an unattended way for a long duration. However, one of the major challenges for acoustic monitoring is to identify sounds of target taxa in recordings which usually contain undesired signals from non-target sources. In addition, high variation in the characteristics of target sounds, co-occurrence of sounds from multiple target taxa, and a lack of reference data make it even more difficult to separate acoustic signals from different sources. To overcome this issue, we developed an unsupervised source separation algorithm based on a multi-layer (deep) non-negative matrix factorization (NMF). Using reference echolocation calls of 13 bat species, we evaluated the performance of the multi-layer NMF in separating species-specific calls. Results showed that the multi-layer NMF, especially when being pre-trained with reference calls, outperformed the conventional supervised single-layer NMF. We also evaluated the performance of the multi-layer NMF in identifying different types of bat calls in recordings collected in the field. We found comparable performance in call types identification between the multi-layer NMF and human observers. These results suggest that the proposed multi-layer NMF approach can be used to effectively separate acoustic signals of different taxa from long-duration field recordings in an unsupervised manner. The approach can thus improve the applicability of passive acoustic monitoring as a tool to investigate the responses of biodiversity to the changing environment

Cheatgrass (Bromus tectorum) distribution in the intermountain Western United States and its relationship to fire frequency, seasonality, and ignitions

Cheatgrass (Bromus tectorum) is an invasive grass pervasive across the Intermountain Western US and linked to major increases in fire frequency. Despite widespread ecological impacts associated with cheatgrass, we lack a spatially extensive model of cheatgrass invasion in the Intermountain West. Here, we leverage satellite phenology predictors and thousands of field surveys of cheatgrass abundance to create regional models of cheatgrass distribution and percent cover. We compare cheatgrass presence to fire probability, fire seasonality and ignition source. Regional models of percent cover had low predictive power (34% of variance explained), but distribution models based on a threshold of 15% cover to differentiate high abundance from low abundance had an overall accuracy of 74%. Cheatgrass achieves ≥ 15% cover over 210,000 km2 (31%) of the Intermountain West. These lands were twice as likely to burn as those with low abundance, and four times more likely to burn multiple times between 2000 and 2015. Fire probability increased rapidly at low cheatgrass cover (1–5%) but remained similar at higher cover, suggesting that even small amounts of cheatgrass in an ecosystem can increase fire risk. Abundant cheatgrass was also associated with a 10 days earlier fire seasonality and interacted strongly with anthropogenic ignitions. Fire in cheatgrass was particularly associated with human activity, suggesting that increased awareness of fire danger in invaded areas could reduce risk. This study suggests that cheatgrass is much more spatially extensive and abundant than previously documented and that invasion greatly increases fire frequency, even at low percent cover

Nonlinear effects of group size on collective action and resource outcomes.

For decades, scholars have been trying to determine whether small or large groups are more likely to cooperate for collective action and successfully manage common-pool resources. Using data gathered from the Wolong Nature Reserve since 1995, we examined the effects of group size (i.e., number of households monitoring a single forest parcel) on both collective action (forest monitoring) and resource outcomes (changes in forest cover) while controlling for potential confounding factors. Our results demonstrate that group size has nonlinear effects on both collective action and resource outcomes, with intermediate group size contributing the most monitoring effort and leading to the biggest forest cover gain. We also show how opposing effects of group size directly and indirectly affect collective action and resource outcomes, leading to the overall nonlinear relationship. Our findings suggest why previous studies have observed differing and even contradictory group-size effects, and thus help guide further research and governance of the commons. The findings also suggest that it should be possible to improve collective action and resource outcomes by altering factors that lead to the nonlinear group-size effect, including punishing free riding, enhancing overall and within-group enforcement, improving social capital across groups and among group members, and allowing self-selection during the group formation process so members with good social relationships can form groups autonomously. casual inference | commons governance | ecosystem services | biodiversity conservation | sustainability G roups are basic units for collective action and may achieve outcomes that individual efforts cannot (1). However, the threat of free riding implies that the optimal amount of collective action does not always occur, and has led to a substantial literature trying to understand what factors facilitate or block the emergence of collective action. Because collective action is needed to manage many common-pool resources, understanding the mechanisms that shape collective action and resource outcomes is a critical challenge for sustainability (2, 3). From Pareto in 1906 (4) and especially since the influential work by Olson in 1965 (5), group size has been hypothesized as a crucial factor affecting collective action and resource outcomes. (We note that Olson used an unusual definition of "group size": the potential number of group members. Here we follow conventional practice and consider the actual number of participants.) However, the debate on group-size effect continues with some researchers arguing that it is linear and negative (5-7), others arguing for linear and positive (8-11), and still others insisting it is curvilinear (12-14), ambiguous (1, 15-17), or nonsignificant (18-20). Even in the most recent work (8, 15, 19,(21)(22) Previous literature indicates that there are two hypothetical opposing forces through which group size affects collective action and resource outcomes A few previous studies qualitatively described the curvilinear or nonlinear effects of group size (12, 26, 29), and some claimed a nonlinear relationship by simply plotting collective action against group size without controlling other factors (13, 14). However, none has provided a quantitative analysis of field evidence while controlling potential confounding factors, as suggested by Ostrom (7). Furthermore, there is little empirical examination of the mechanisms of nonlinear group-size effects, which is essential to guide commons governance. To fill these knowledge gaps, we used empirical data from our long-term studies (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44) in Wolong Nature Reserve, Sichuan Province, China (N 30°45′ -31°25′, E 102°52′ -103°24′) To understand the group-size effects and the underpinning mechanisms, we combined data on characteristics of households, household groups, and monitored parcels (SI Appendix, Section 1). We acknowledge that conflicts with regard to monitoring might occur within a household, but because the policy is designed to treat households-not individuals-as monitoring units, the common practice of treating households as the unit of analysis is appropriate here. We measured household monitoring efforts by the total amount of labor input (one unit of labor input is defined as one laborer working for 1 d) (SI Appendix, Section 2.1) through surveys. We measured resource outcomes as changes in forest cover derived from previously published forest-cover maps (SI Appendix, Section 1.1.1). We also measured factors that might explain the mechanisms, including free riders (i.e., households that did not participate in monitoring), the level of within-group enforcement (i.e., strong enforcement if there are punishment measures for free-riding members within the group; otherwise, weak enforcement), and within-group division (i.e., whether groups divide into subgroups to conduct monitoring in turns) (SI Appendix, Section 2). Some other contextual factors shown in previous studies to affect group size, collective action, or resource outcomes were used as control variables (SI Appendix, Section 2.3). Results Our results show that group size has a nonlinear effect on the monitoring efforts per household, with an intermediate group size contributing the most Our results demonstrate that group size also has a nonlinear effect on changes in forest cover, with an intermediate group size leading to the biggest gain ( We accounted for as many as possible alternative explanations of the observed nonlinear group-size effects based on systematic quantitative and qualitative analyses. No factor other than group size seems to account for the observed nonlinear effects. First, correlation tests (SI Appendix, Table S2) show that except for the two criteria used for household group assignment (see details in SI Appendix, Section 1.2) by the administrative bureau (i.e., distance between each household and its assigned parcel and received NFCP payment), no other factors were significantly associated with group size and thus are implausible as possible alternative explanations for the group-size effects. We used two additional approaches to ensure that the observed nonlinear effects were not caused by the two criteria used for household group assignments (SI Appendix, Section 2.4.3). We examined the associations between the two criteria used for household group assignment and household monitoring efforts, and we A B Yang et al. PNAS Early Edition | 3 of 6 SUSTAINABILITY SCIENCE estimated two-step Tobit models of monitoring effort. Using either approach, all hypothesized alternatives to group size were linearly associated with household monitoring efforts, and thus could not lead to the observed nonlinear effects. Our path analysis Discussion The coexistence of two opposing forces may also explain why previous studies found different group-size effects. If, as we argue, the net effect of group size is determined by the dynamics (e.g., strength and variation with group size) of the two opposing forces, the optimum point of the net effect (or the necessary range of group size to observe a nonlinear effect) would be dependent on the context (14). The range of group size in our study area may appear to be small. However, the nonlinear pattern we observed means that such a range is large enough to exhibit the nonlinear effect in our context. One of the reasons we find such effects with only moderate variation in group size may be because our study area is a flagship nature reserve for giant pandas. As a result, the local administrative bureau has relatively abundant resources to allocate payments for household groups to monitor parcels and evaluate their performance biannually Unit of analysis is the forest parcel. Dependent variable is the percent of forest-cover change from 2001 to 2007. Additional controls include parcel size, parcel size per household, elevation, distance between each parcel and the nearest household, and distance between each parcel and the main road (SI Appendix, Randomized experiments are sometimes seen as the "gold standard" for research on causal mechanisms. However, there have been no randomized experiments at our site, nor are there likely to be because of its status as a showcase for conservation efforts. In addition, in the real world, there is no randomized or even quasirandomized field experiment in this field of study. The best that can be done in many real-world resource management situations is to be careful with regard to inference. Our analyses show that significant advances in understanding can be made through careful analyses of nonexperimental data by drawing on historical data. Such efforts of ongoing programs provide a useful complement to field experiments in building a cumulative literature and forwarding the important work on collective action and resource management. Our findings also suggest that by regulating factors interacting with group size, it should be possible to improve collective action and resource outcomes. For example, all groups of various sizes can stimulate group members to contribute and protect common-pool resources by punishing free riding and enhancing overall and within-group enforcement. Overall enforcement can be enhanced not only through intensifying costly monitoring efforts but also via improving social capital across groups. The within-group enforcement and outcomes may also be enhanced by improving social capital among group members or allowing self-selection during the group formation process so members with good social relationships can form groups autonomously. Unprecedented deterioration of global commons requires better understandings of the mechanisms shaping collective action and resource outcomes. Because of the complexity of coupled human and natural systems (46), improving such understandings is challenging and requires efforts to integrate data and methods from multiple disciplines. The struggle to understand the groupsize effects is one example showing the importance of such efforts. Our findings help disentangle the puzzle of group-size effects and guide solutions to pressing problems of coupled human and natural systems (47), as well as the design of commons governance policies. Materials and Methods We acquired the map of household monitoring parcels and associated documentation (e.g., the number of households that monitor each forest parcel) from the administrative bureau of Wolong Nature Reserve. To estimate forest-cover change, we used previously published forest-cover maps derived from Landsat imagery in 2001 and 2007 (48, 49). These maps included two main land-cover classes (i.e., forest and nonforest) with overall accuracies between 80% and 88% using independent ground-truth data. Topographic data, such as elevation, slope, and the Compound Topographic Index, a relative measure of wetness (50), were obtained from a digital elevation model at a spatial resolution of 90 m/pixel (51). We measured all household locations (∼2,200 households) inside and surrounding the Reserve using Global Positioning System receivers. We calculated geographic metrics of forest parcels and households using the software of ArcGIS 10.1 (ESRI). These metrics include parcel size, parcel size per household, average elevation, average slope, average wetness, distance between each parcel and the nearest household, distance between each parcel and the main road, distance between each household and its monitored parcel, distance between each household and the main road, initial forest cover in 2001, and the percent of forest-cover change from 2001 to 2007. To understand the NFCP planning, implementation, evaluation, and decision-making processes, and to prepare for the household interview, we invited eight Reserve administrative staff for focus group interviews and five officials who were or are in charge of the NFCP for personal interviews. We used best available household survey data containing NFCP implementation information in 2007 and 2009 from our long-term study in the Reserve, which has been tracking ∼220 randomly sampled households across the years since 1998 (52). The panel survey elicited basic information, such as demographic status, socioeconomic conditions, and energy use (53). In the 2007 and 2009 surveys, besides basic information from panel surveys, we also asked questions regarding NFCP implementation [e.g., NFCP payments, monitoring frequency, time spent for each monitoring, monitoring strategy (e.g., within-group division), and within-group enforcement]. A total of 156 randomly sampled NFCP participating households in 2007, covering the full range of group size (i.e., 1 to 16), were used to examine how group size affects collective action (i.e., household forest monitoring). The 113 households who monitored NFCP parcels with group size larger than one (i.e., 2 to 16) in 2009 were used to examine the mechanisms of nonlinear groupsize effects. We first used a Tobit model to examine the effect of group size on monitoring efforts at the household level. We then used a spatial autoregressive model to examine the effect of group size on forest-cover change at the parcel level. Finally, we conducted the path analysis to test the two hypothetical, opposing forces on the mechanisms of nonlinear group-size effects. Detailed descriptions of data collection, processing, and model specification and construction are provided in SI Appendix. ACKNOWLEDGMENTS

Will Remote Sensing Shape the Next Generation of Species Distribution Models?

Two prominent limitations of species distribution models (SDMs) are spatial biases in existing occurrence data and a lack of spatially explicit predictor variables to fully capture habitat characteristics of species. Can existing and emerging remote sensing technologies meet these challenges and improve future SDMs? We believe so. Novel products derived from multispectral and hyperspectral sensors, as well as future Light Detection and Ranging (LiDAR) and RADAR missions, may play a key role in improving model performance. In this perspective piece, we demonstrate how modern sensors onboard satellites, planes and unmanned aerial vehicles are revolutionizing the way we can detect and monitor both plant and animal species in terrestrial and aquatic ecosystems as well as allowing the emergence of novel predictor variables appropriate for species distribution modeling. We hope this interdisciplinary perspective will motivate ecologists, remote sensing experts and modelers to work together for developing a more refined SDM framework in the near future

A second horizon scan of biogeography:golden ages, Midas touches, and the Red Queen

Are we entering a new ‘Golden Age’ of biogeography, with continued development of infrastructure and ideas? We highlight recent developments, and the challenges and opportunities they bring, in light of the snapshot provided by the 7th biennial meeting of the International Biogeography Society (IBS 2015). We summarize themes in and across 15 symposia using narrative analysis and word clouds, which we complement with recent publication trends and ‘research fronts’. We find that biogeography is still strongly defined by core sub-disciplines that reflect its origins in botanical, zoological (particularly bird and mammal), and geographic (e.g., island, montane) studies of the 1800s. That core is being enriched by large datasets (e.g. of environmental variables, ‘omics’, species’ occurrences, traits) and new techniques (e.g., advances in genetics, remote sensing, modeling) that promote studies with increasing detail and at increasing scales; disciplinary breadth is being diversified (e.g., by developments in paleobiogeography and microbiology) and integrated through the transfer of approaches and sharing of theory (e.g., spatial modeling and phylogenetics in evolutionary–ecological contexts). Yet some subdisciplines remain on the fringe (e.g., marine biogeography, deep-time paleobiogeography), new horizons and new theory may be overshadowed by popular techniques (e.g., species distribution modelling), and hypotheses, data, and analyses may each be wanting. Trends in publication suggest a shift away from traditional biogeography journals to multidisciplinary or open access journals. Thus, there are currently many stewardship of, the planet (e.g., Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services). As in the past, biogeographers doubtless will continue to be engaged by new data and methods in exploring the nexus between biology and geography for decades into the future. But golden ages come and go, and they need not touch every domain in a discipline nor affect subdisciplines at the same time; moreover, what appears to be a Golden Age may sometimes have an undesirable ‘Midas touch’. Contexts within and outwith biogeography—e.g., methods, knowledge, climate, biodiversity, politics—are continually changing, and at times it can be challenging to establish or maintain relevance. In so many races with the Red Queen, we suggest that biogeography will enjoy greatest success if we also increasingly engage with the epistemology of our disciplinePeer reviewe

Synthesis of human-nature feedbacks

In today's globalized world, humans and nature are inextricably linked. The coupled human and natural systems (CHANS) framework provides a lens with which to understand such complex interactions. One of the central components of the CHANS framework involves examining feedbacks among human and natural systems, which form when effects from one system on another system feed back to affect the first system. Despite developments in understanding feedbacks in single disciplines, interdisciplinary research on CHANS feedbacks to date is scant and often site-specific, a shortcoming that prevents complex coupled systems from being fully understood. The special feature "Exploring Feedbacks in Coupled Human and Natural Systems (CHANS)" makes strides to fill this critical gap. Here, as an introduction to the special feature, we provide an overview of CHANS feedbacks. In addition, we synthesize key CHANS feedbacks that emerged in the papers of this special feature across agricultural, forest, and urban landscapes. We also examine emerging themes explored across the papers, including multilevel feedbacks, time lags, and surprises as a result of feedbacks. We conclude with recommendations for future research that can build upon the foundation provided in the special feature

Taiwan Biodiversity Information Facility (TaiBIF)’s Role in Facilitating Open Biodiversity Data in Taiwan

Taiwan Biodiversity Information Facility (TaiBIF) was established in 2001 as one of the first Global Biodiversity Information Facility (GBIF) nodes. Funded by Taiwan's Ministry of Science and Technology and Forestry Bureau, TaiBIF’s missions are to:Develop infrastructures for biodiversity-related data,Promote the open data concept,Facilitate the publication and integration of biodiversity information,Support biodiversity-related research and policy-making, andConnect Taiwan and international biodiversity information.As part of the work to fulfill its missions, TaiBIF develops and maintains infrastructures such as a data portal, Taiwan Catalogue of Life (TaiCoL), and a tool for scientific name-checking - NomenMatch, for public and researchers to use as needed. Additionally, as the only GBIF data hosting centre currently available in Asia, TaiBIF offers all services mentioned in the GBIF’s data hosting criteria. Briefly, this includes a regularly maintained Integrated Publishing Toolkit (IPT) and help desk for supporting users in dataset and data paper publishing process. In the continuous effort to promote open biodiversity data, TaiBIF organises workshops annually for students, researchers, and any governmental or non-governmental organisations, providing hands-on training focusing on data standardising and the publishing process. As of June 2022, Taiwan has exposed more than 13 million occurrence records and 83 datasets through GBIF's data platform, contributed mostly by 19 data publishing units, making it the second-largest data contributor in Asia (after India). These data have been used in many peer-reviewed studies on various topics and for guiding conservation policies. TaiBIF has maintained close interactions and cooperation with GBIF, regularly assisting in the Biodiversity Information Fund for Asia (BIFA) projects, regional workshops and communications between members from Asian countries. TaiBIF’s current active tasks include allying with the Taiwan Biodiversity Information Alliance (TBIA), a national biodiversity network aimed at the integration of local biodiversity data from various governmental agencies and museums. Furthermore, a volunteer training system is being developed to support workloads at the TaiBIF office. Recognising the importance of data coverage, a data review study of open biodiversity data status in Taiwan is ongoing