In a recent paper, Welsh, Lindenmayer and Donnelly (WLD) question the usefulness of models that estimate species occupancy while accounting for detectability. WLD claim that these models are difficult to fit and argue that disregarding detectability can be better than trying to adjust for it. We think that this conclusion and subsequent recommendations are not well founded and may negatively impact the quality of statistical inference in ecology and related management decisions. Here we respond to WLD's claims, evaluating in detail their arguments, using simulations and/or theory to support our points. In particular, WLD argue that both disregarding and accounting for imperfect detection lead to the same estimator performance regardless of sample size when detectability is a function of abundance. We show that this, the key result of their paper, only holds for cases of extreme heterogeneity like the single scenario they considered. Our results illustrate the dangers of disregarding imperfect detection. When ignored, occupancy and detection are confounded: the same naïve occupancy estimates can be obtained for very different true levels of occupancy so the size of the bias is unknowable. Hierarchical occupancy models separate occupancy and detection, and imprecise estimates simply indicate that more data are required for robust inference about the system in question. As for any statistical method, when underlying assumptions of simple hierarchical models are violated, their reliability is reduced. Resorting in those instances where hierarchical occupancy models do no perform well to the naïve occupancy estimator does not provide a satisfactory solution. The aim should instead be to achieve better estimation, by minimizing the effect of these issues during design, data collection and analysis, ensuring that the right amount of data is collected and model assumptions are met, considering model extensions where appropriate.

Variation partitioning analyses combined with spatial predictors (Moran’s eigenvector maps, MEM) are commonly used in ecology to test the fractions of species abundance variation purely explained by environment and space. However, while these pure fractions can be tested using a classical residuals permutation procedure, no specific method has been developed to test the shared space-environment fraction (SSEF). Yet, the SSEF is expected to encompass a major driver of community assembly, that is, an induced spatial dependence effect (ISD; i.e. the reflection of a spatially structured habitat filter on a species distribution). A reliable test of this fraction is therefore crucial to properly test the presence of an ISD on ecological data. To bridge the gap, we propose to test the SSEF through spatially-constrained null models: torus-translations, and Moran spectral randomisations. We investigated the type I error rate and statistical power of our method based on two real environmental dat...

Spatiotemporal variation in natural selection is expected, but difficult to estimate. Pollinator-mediated selection on floral traits provides a good system for understanding and linking variation in selection to differences in ecological context. We studied pollinator-mediated selection in five populations of Dalechampia scandens (Euphorbiaceae) in Costa Rica and Mexico. Using a nonlinear path-analytical approach, we assessed several functional components of selection, and linked variation in pollinator-mediated selection across time and space to variation in pollinator assemblages. After correcting for estimation error, we detected moderate variation in net selection on two of four blossom traits. Both the opportunity for selection and the mean strength of selection decreased with increasing reliability of cross-pollination. Selection for pollinator attraction was consistently positive and stronger on advertisement than reward traits. Selection on traits affecting pollen transfer from the pollinator to the stigmas was strong only when there was a mismatch between pollinator and blossom size under unreliable cross-pollination. These results illustrate how consideration of trait function and ecological context can facilitate both the detection and the causal understanding of spatiotemporal variation in natural selection.

Methods We studied phenotypic selection on the blossom traits in five populations, three in Costa Rica (Palo Verde, Puente la Amistad and Horizontes) and two in Mexico (La Mancha and Puerto Morelos). Both Mexican populations and one Costa Rican population were studied in two consecutive years. The data from the La Mancha population in 2007 were analyzed by Pérez-Barrales et al. (2013). In each population, we marked distinct patches of one to several intertwined individuals. Plants flower for an extended period, and we selected multiple blossoms per patch as they came into flower.

We followed each focal blossom throughout the female phase and for the first day of the bisexual phase. Each day, we recorded the number of pollen grains on the three stigmas with the aid of a LED light and a 10× hand lens, and whether resin had been collected. On the first day of the bisexual phase, when the first male flower was open, we counted pollen on the stigmas one last time, and measured gland-stigma distance (GSD), anther-stigma distance (ASD), gland area (GA), and upper bract area (UBA). All distance traits were measured in mm using digital calipers. For the Costa Rican populations, we also measured the height of the blossom above the ground. After completing the measurements, we marked the blossoms with a small tag tied around the peduncle. We collected the marked blossoms 3-4 weeks later and recorded the number of seeds set (seed set).

Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. The ecoregions of Alaska are a framework for organizing and interpreting environmental data for State, national, and international level inventory, monitoring, and research efforts. The map and descriptions for 20 ecological regions were derived by synthesizing information on the geographic distribution of environmental factors such as climate, physiography, geology, permafrost, soils, and vegetation. A qualitative assessment was used to interpret the distributional patterns and relative importance of these factors from place to place (Gallant and others, 1995). Numeric identifiers assigned to the ecoregions are coordinated with those used on the map of "Ecoregions of the Conterminous United States" (Omernik 1987, U.S. EPA 2010) as a continuation of efforts to map ecoregions for the United States. Additionally, the ecoregions for Alaska and the conterminous United States, along with ecological regions for Canada (Wiken 1986) and Mexico, have been combined for maps at three hierarchical levels for North America (Omernik 1995, Commission for Environmental Cooperation, 1997, 2006). A Roman numeral hierarchical scheme has been adopted for different levels of ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions. At Level III, there are currently 182 ecological regions for North America. Level IV ecoregions have been developed for the conterminous United States, but Level III is the highest level available for Alaska. Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America - toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Commission for Environmental Cooperation, 2006, Ecological regions of North America, Level III, Map scale 1:10,000,000, https://www.epa.gov/eco-research/ecoregions-north-america. Gallant, A.L., Binnian, E.F. Omernik, J.M. and Shasby, M.B., 1995, Ecoregions of Alaska: U.S. Geological Survey Professional Paper 1567. Omernik, J.M., 1987, Ecoregions of the Conterminous United States: Annals of the Association of American Geographers, v. 77, no.1, p. 118-125. Omernik, J.M., 1995, Ecoregions: a Framework for Managing Ecosystems: The George Wright Forum, v. 12, no. 1, p. 35-51. U.S. Environmental Protection Agency, 2010, Level III ecoregions of the continental United States (revision of Omernik, 1987): Corvallis, Oregon, USEPA - National Health and Environmental Effects Research Laboratory, Map M-1, various scales. Wiken, E.B., 1986, Terrestrial Ecozones of Canada: Lands Directorate, Environmental Canada Ecological Land Classification Series 19, 26 p. Comments and questions regarding ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov

Yearly citation counts for the publication titled "Are infectious diseases really killing corals? Alternative interpretations of the experimental and ecological data".

The Missouri Resource Assessment Partnership (MoRAP) of the University of Missouri, in conjunction with the Oklahoma Biological Survey of the University of Oklahoma, produced a vegetation and landcover GIS data layer for the eastern portions of Oklahoma. This effort was accomplished with direction and funding from the Oklahoma Department of Wildlife Conservation and state and federal partners (particularly the Gulf Coast Prairie and Great Plains Landscape Conservation Cooperatives of the U. S. Fish and Wildlife Service). The legend for the layer is based on NatureServe’s Ecological System Classification, with finer thematic units derived from land cover and abiotic modifiers of the System unit. Data for development of a supervised classification of landcover was collected in the field by Oklahoma Biological Survey staff and through photo interpretation of aerial imagery by MoRAP staff. This data was used with decision tree classifier on 3 dates of LandSat imagery, abiotic data, and additional available data to perform a supervised classification to a simplified set of landcover classes. Improved thematic resolution was achieved using image objects at 10 m resolution based on NAIP imagery. The landcover classes, along with EPA Level IV Ecoregion, SSURGO soils, DEM-based, and hydrology variables, were applied to the image objects and were used to interpret ecological context and assign appropriate vegetation type or landcover to the objects. Vector data was then transformed to a 10 m raster product to simplify presentation and use. Project and partner ecologists have produced detailed descriptions of most Ecological Systems that were mapped in this effort. Project ecologists have also developed an Interpretive Booklet that includes a general description of each mapped type, a general range map for each type, and a photograph representing the type (when available). The interpretive booklet also includes detailed product methodology.

In this age of rapid biodiversity loss, we must continue to refine our approaches to describing variation in life on Earth. Combining knowledge and research tools from multiple disciplines is one way to better describe complex natural systems. Understanding plant community diversity requires documenting both pattern and process. We must first know which species exist, and where (i.e., taxonomic and biogeographic patterns), before we can determine why they exist there (i.e., ecological and evolutionary processes). Floristic botanists often use collections-based approaches to elucidate biodiversity patterns, while plant ecologists use hypothesis-driven statistical approaches to describe underlying processes. Because of these different disciplinary histories and research goals, floristic botanists and plant ecologists often remain siloed in their work. Here, using a case study from an urban greenway in Colorado, USA, we illustrate that the collections-based, opportunistic sampling of floristic botanists is highly complementary to the transect- or plot-based sampling of plant ecologists. We found that floristic sampling captured a community species pool four times larger than that captured using ecological transects, with rarefaction and non-parametric species estimation indicating that it would be prohibitive to capture the “true” community species pool if constrained to sampling within transects. We further illustrate that the discrepancy in species pool size between approaches led to a different interpretation of the greenway’s ecological condition in some cases (e.g., transects missed uncommon cultivated species escaping from nearby gardens) but not others (e.g., plant species distributions among functional groups were similar between species pools). Finally, we show that while using transects to estimate plant relative abundances necessarily trades off with a fuller assessment of the species pool, it is an indispensable indicator of ecosystem health, as evidenced by three non-native grasses contributing to 50% of plant cover along the highly modified urban greenway. We suggest that actively fostering collaborations between floristic botanists and ecologists can create new insights into the maintenance of species diversity at the community scale.

Methods We sampled plant communities along the High Line Canal greenway, a 66-mile recreational trail that passes through 11 municipalities in the Denver-Metro area of Colorado. The trail runs alongside a 71-mile earthen canal (owned by Denver Water), which was excavated in the late 1800s to support agriculture and human settlement in what was historically native plains and foothills shrubland vegetation. The greenway thus represents a human-created waterway that is highly managed, yet supports a species pool that contains native flora.

The greenway’s length (with the Canal’s inception located at 39.48362, -105.11293) is demarcated by mile markers that we used to generate a random subset of 45 locations at which the botanical and ecological field crews could synchronize their data collection. As is typical for collections-based floristic surveys, the botanical crew sampled exhaustively from early spring (7 May) through late summer (28 September) to capture early-, middle-, and late-season species. Starting at each of the 45 mile markers, the crew walked in the Canal’s downstream direction, searching the greenway for newly encountered species to collect and accession to the Kathryn Kalmbach Herbarium (KHD) at Denver Botanic Gardens . Most mile marker locations were sampled once during the inventory, but a few were revisited if they occurred in a vegetation type that would not be re-encountered later in the season at the other mile markers (e.g., mile markers zero and one at the inception of the Canal were the only locations in the foothills shrubland Ecoregion).

We used a staggered sampling design in which 5 mile markers spanning the southwestern to northeastern extent of the Canal were sampled every other week from May to September. The floristic survey was carried out over 57 days, comprising 850 search-hours and an estimated distance covered of 42 miles (calculated from our daily starting and stopping waypoints logged with a GPS unit). The botanical crew consisted of two botanists trained in the local flora and one to two additional non-botanists who assisted with specimen collection. All members of the crew searched for species within an ~50 to 75-foot-wide viewshed moving from the bed of the Canal, up the Canal bank, across the greenway trail, and over to the property line that marked the end of Denver Water’s ownership. High-veracity (with identifying structures) herbarium specimens were accessioned for every species encountered during the floristic survey (numbering 1570 specimens, including duplicates; collections data available). Identifications were made using Ackerfield's Flora of Colorado (2015), Wingate's Illustrated Keys to the Grasses of Colorado (1994), and Wingate's Sedges of Colorado (2017).

The ecological sampling was carried out over 10 days, from May 22, 2018 to June 6, 2018, to capture a snapshot of plant communities around peak biomass. This method of deploying a concerted sampling effort over a short time period is common in ecological sampling, because it is often of interest to detect treatment differences that could be obscured by confounding time lags between sample dates (as opposed to the floristic botany goal of exhaustively delineating a species pool over time). At each of the 45 miles markers, we laid a 12 m × 2 m transect, the length of which captured habitat variation across the greenway corridor. We used the line-point intercept method to make field observations of plant species presence every 0.25 m along the 12 m transect (as well as bare ground, plant litter, and rocks, which we do not report herein). In the associated data set, the “first hit” was used to generate the reported percent cover estimates (number of hits per species per total number of hits), while the “second hit” was used to add species to our presence list. We also searched each of the two, one-meter-wide belt transects for additional species that were not encountered along the line-point transect. Voucher specimens were collected for the species encountered during the observational ecological sampling (collected outside the transects so as not to influence long-term sampling). However, given the short time period of the ecological sampling, not all specimens had flowers or fruits, and therefore were not of sufficient quality to be curated. All specimens were kept during the field season and subsequent analyses to facilitate identifications, but only higher quality specimens were accessioned to the herbarium. Please note that one example specimen exists for potentially hundreds of field observations (i.e., each time a species was encountered along the transects).

We chose the line-point method as the most appropriate for our system, with its narrow and steep canal bank that could not accommodate other plot designs. Additionally, our aim with the ecological transects was to estimate not only species presence, but also composition. For questions about composition, the line-point method is highly repeatable across individuals and rapidly deployed, thereby maximizing sampling replicates across many locations in a single season. Such transects will capture fewer species than other methods (e.g., Modified-Whitaker plots); however, any bounded sampling approach will cover considerably less area than can be achieved with opportunistic sampling based in the floristic tradition of using the habitat itself as the sampling unit. Importantly, we note that it was not our goal to equalize the temporal or spatial scales of the two sampling approaches (which in our experience is not often done in practice), but rather to sample in a manner that is broadly consistent with collections-based versus ecological disciplines. The particulars of our comparison, such as the sampling window and the use of transects rather than any number of plot types, contextualize the results.

AimMany tree species distribution models use black-box machine learning techniques that often neglect interpretative aspects and instead focus mainly on maximising predictive accuracy. In this study, we outline an interpretative modelling framework to gain better ecological insights while mapping abundance patterns of six North American species. LocationContinental United States and Canada MethodsWe develop an innovative procedure using regression trees by stabilising variance and mapping dominant rules which we term ‘optimized regression tree bagging for interpretation and mapping’ (ORTBIM). We apply this technique to understand ecological features influencing the abundance patterns of three eastern (Pinus strobus, Acer saccharum, and Quercus montana), and three western (Picea engelmannii, Pinus ponderosa, and Pseudotsuga menziesii) tree species in North America. For these species, we assess and map the dominant climate-terrain interactions that partly determine abundance patterns in t..., The data is from the Forest Inventory Analysis of the USDA Forest Service. We also use data from the AdaptWest climate and USGS elevational data., R - https://cran.r-project.org/Â

1. Light detection and ranging (LiDAR) technology provides ecologists with high-resolution data on three-dimensional vegetation structure. Large LiDAR datasets challenge predictive ecologists, who commonly simplify point clouds into structural attributes (namely LiDAR-based metrics such as canopy height), which are used as predictors in ecological models, potentially with loss of relevant information. 2. We illustrate an efficient alternative approach to reduce the dimensionality of LiDAR data that aims at minimal data filtering with no a priori assumptions on the ecology of the target species. We first fit the ecological model exploiting the full variability of the LiDAR point cloud, then we explain the results using post-modelling LiDAR-data classification for ecological interpretation only. This is the classical logic of explorative, hypothesis generating and predictive statistics, rather than testing specific vegetation-structural hypotheses. 3. First, we reduce the dimensionality of the LiDAR point cloud by Principal Component Analysis (PCA) to fewer predictors. Secondly, we show that LiDAR-PCs are capable to outperforming commonly used environmental predictors in ecological modelling, including LiDAR-based metrics. We exemplify this by modelling red deer (Cervus elaphus) and roe deer (Capreolus capreolus) resource selection in the Bavarian Forest National Park, Germany. After fitting the ecological model, we provide an interpretation of the information included in LiDAR-PCs, which allows users to draw conclusions whenever using them as predictors. We make use of the PCA rotation matrix and post-modelling data classification, and document deer selection for understory vegetation at unprecedented fine scale. 4. Our approach is the first attempt in animal ecology to avoid the use of LiDAR-based metrics as model predictors, but rather generate principal components able to capture most of the LiDAR point cloud variability. Our study demonstrates that LiDAR-PCs can boost ecological models. We envision a potential use of LiDAR-PCs in several applications, particularly species distribution and habitat suitability models. We demonstrate an application of our approach by building suitability maps for both deer species, which can be used by practitioners to visualize model spatial predictions and understand the type of forest structures selected by deer.

1. The anthropocene presents significant challenges for global biodiversity, public health, and long-term ecosystem stability. The wealth of publicly available near-real-time ecology and climate data can be used to monitor these challenges and allow practitioners to develop mitigation strategies.
2. There is untapped potential to apply Large Language Models (LLMs) to quantitative ecological and environmental datasets, enabling researchers and practitioners to use natural language queries to transform ecological observations into actionable insights for both conservation action and external communication of results to diverse audiences. Advances in artificial intelligence (AI), and particularly in LLMS, offer emerging opportunities to address these challenges. LLMs are increasingly proficient at identifying patterns and semantic relationships within textual data, and are highly customisable. Accessible AI tools can also facilitate communication across research and policy sectors.
3. Here, we present a roadmap for designing and implementing multi-modal LLMs to answer ecological research questions. In order to build ‘virtual statistician’ systems capable of fast-tracking data interpretation, we advocate for strategic planning, data stewardship practices, careful prompt-engineering, and model evaluation as key steps in the LLM development process.
4. We showcase a case study that applies the open-source LangChain framework to analyse citizen science data using the eBird database to produce a chatbot allowing the user to ask quantitative questions about near-real-time bird observations. Using our LLM roadmap, we highlight the importance of iterative and strategic prompt engineering and agent selection, in addition to iteratively evaluating model output. As LLM software continues to evolve, their integration into ecological and environmental research can empower ecologists with purpose-built tools that bridge the gap between data collection and actionable solutions.

When ecosystems are under severe pressure or environments change, trophic position and intraspecific niche width may decrease or narrow, signalling that conservation action is required. In New Zealand, alpine and sub-alpine ecosystems have been extensively modified through farming since 19th century European settlement, with consequences for indigenous species such as the kea (Nestor notabilis). We investigated feather stable isotope values in the kea and predicted a lower trophic position in modern kea populations, to reflect reduced lowland habitat and a mixed diet with more plant material. We predicted that size and sex would influence trophic values in this sexually dimorphic species, with larger birds more likely to have a high protein diet. We examined potential dietary changes in 68 museum collected kea from 1880s to 2000s, first recording accession details including provenance and sex, and measuring culmen length. We used bulk carbon and nitrogen stable isotopes analyses (BSIA) of feathers and a further feather subset using compound-specific stable isotopes analyses of amino acids (CSIA-AA) to obtain isotopic values and estimate trophic position. BSIA showed δ15N values in kea feathers declined through time, and could indicate that early century kea were highly omnivorous, with δ15N values on average higher than in modern kea. Variance in δ15N values was greater after 1950, driven by a few individuals. Few differences between males and females were evident, although females in the south region had lower δ15N values. There was a tendency for large male birds to have higher trophic values, perhaps reflecting dominant male bird behaviour noted in historical records. Nonetheless, CSIA-AA performed on a subset of the data suggested that variation in BSIA is likely due to baseline changes rather than relative trophic position which may be more homogenous than these data indicate. Although there was more variability in modern kea, we suggest caution in interpretation. Stable isotope data, particularly CSIA-AA, from museum specimens can reveal potential change in ecological networks, as well as sexually dimorphic feeding patterns within species. The data can reveal temporal and regional variation in species trophic position and changes in ecosystem integrity to inform conservation decision-making. Methods We examined kea specimens and skins from 5 natural history collections, at Auckland Museum, New Zealand; Te Papa National Museum, NZ; Canterbury Museum, NZ; and Otago Museum, NZ; and Naturhistorisches Museum Wien (the Vienna Natural History Museum), Austria. We recorded all accession details for kea where these were available, including collection location, year of collection, and sex if previously determined. Stable isotope analysis was conducted from feathers removed from the upper middle back between the shoulder blades on each bird skin, and were washed in 2:1 chloroform:methanol solution for 24 h, agitated, rinsed and air dried in a fume cupboard for 48 h, before the top 1cm of the feather vane was finely clipped and weighed into tin capsules for bulk nitrogen and carbon isotope analysis. Further methods are reported in the paper.

Species Distribution Models often include spatial effects which may improve prediction at unsampled locations and reduce Type I errors when identifying environmental drivers. In some cases ecologists try to ecologically interpret the spatial patterns displayed by the spatial effect. However, spatial autocorrelation may be driven by many different unaccounted drivers, which complicates the ecological interpretation of fitted spatial effects. This study aims to provide a practical demonstration that spatial effects are able to smooth the effect of multiple unaccounted drivers. To do so we use a simulation study that fit model-based spatial models using both geostatistics and 2D smoothing splines. Results show that fitted spatial effects resemble the sum of the unaccounted covariate surface(s) in each model.

This research project examined how the state of the environment is understood through an ecological and social perspective. Emphasis is placed on ecological measures as well as perceptions, with specific attention to intergroup differences and the influences of an interpretive program. Two studies were conducted at the Niagara Glen Nature Reserve, a protected area in the Niagara Region of Canada. The first study consisted of an ecological assessment and a survey administered to experts and visitors. The second study involved administering the survey to individuals receiving two different educational interventions, thereby exploring the influence of an environmental interpretive program on how people perceive the environment. Raw data, summary data, and procedure details included here.

In 2015, the United States Fish and Wildlife Service (USFWS), Detroit River International Wildlife Refuge, contracted with Michigan Natural Features Inventory (MNFI), Michigan State University Extension (MSUE), to survey and identify remnant natural communities at Humbug Marsh and several other local conservation areas to produce information that Refuge staff will use to help develop a management plan and an accurate interpretive message to the public for Humbug Marsh. The focus of the surveys was not only to identify existing natural communities, but also to interpret landform and soils, hydrology, past land use, and current natural and anthropogenic processes and disturbances at each site.

After reviewing field data, aerial imagery, and other information, we identified and surveyed Humbug Marsh and remnant forested communities on Grosse Ile and at Oakwoods Metropark. Field assessment of natural communities included compiling a thorough list of dominant and representative vascular plant species, describing site-specific structural attributes and ecological processes, analyzing soils and apparent hydrological features (e.g., seasonally inundated depressions), noting current and historical anthropogenic disturbances, evaluating potential threats, taking representative digital photos and GPS points at significant locations, evaluating each site’s natural community delineation and classification, and noting significant management needs and restoration opportunities.

Based on field surveys and other data sources such as the original General Land Office (GLO) survey notes, historical plat maps, historical literature, aerial photographs, studies on regional landscape ecosystems and ecoregions, USDA NRCS digital soil maps, and the International Vegetation Classification, we identified the highest quality field survey sites as examples of wet-mesic flatwoods, a state imperiled forested natural community of low-relief lake plain characterized by mixed hardwood dominance on relatively impermeable, seasonally wet to dry soils. The study area at Humbug Marsh was interpreted primarily as old agricultural field in early stages of succession, with remnant areas of historically cleared and grazed, mostly mesic oak – hickory forest. None of the study sites supported significant concentrations of conservative vascular plant species or species typical of oak savanna or prairie habitats.

If forest is the future desired condition of Humbug Marsh, land management practices such as the planting of trees, selective harvest and canopy gap creation, reduction of the white-tailed deer population, and control of invasive woody and herbaceous species (e.g., common buckthorn and garlic mustard) should be considered in an experimental context. An alternative conservation management option is to maintain and enhance early successional habitats at Humbug Marsh for declining wildlife species and shade-intolerant plants. Although there is scant evidence of restorable savanna or prairie communities at Humbug Marsh, Refuge staff could consider creating a demonstration planting of plant species native to local savanna and prairie remnants to educate visitors about these critically imperiled ecosystems. Given the sizable human population and presence of several colleges and universities in metropolitan Detroit, Humbug Marsh is ideally situated for academic research, which should be conducted in conjunction with land management activities to assess progress in a statistically rigorous manner and in turn help refine conservation goals and land management techniques.

Network of 44 papers and 61 citation links related to "PERCEIVING AND INTERPRETING ENVIRONMENTAL CHANGE: AN EXAMINATION OF COLLEGE ADMINISTRATORS' INTERPRETATION OF CHANGING DEMOGRAPHICS.".

This data collection includes habitat characterizations, marine mammal distributions, bird and reptile distributions, nest distributions, and other data and information organized using a geographic information system (GIS). Specialized software for reading and interpreting GIS files is required to fully utilize this data collection.

Aim: Understanding how ecological communities are assembled remains a grand challenge in ecology with direct implications for charting the future of biodiversity. Trait-based methods have emerged as the leading approach for quantifying functional community structure (convergence, divergence) but their potential for inferring assembly processes rests on accurately measuring functional dissimilarity among community members. Here, we argue that trait resolution (from finest-resolution continuous measurements to coarsest-resolution binary categories) remains a critically overlooked methodological variable, even though categorical classification is known to mask functional variability and inflate functional redundancy among species or individuals.

Innovation: We present the first detailed predictions of trait resolution biases and demonstrate, with simulations, how the distortion of signal strength by increasingly coarse-resolution traits can fundamentally alter functional structure patterns and the interpretation of causative ecological processes (e.g., abiotic filters, biotic interactions). We show that coarser trait data impart different impacts on the signals of divergence and convergence, implying that the role of biotic interactions may be underestimated when using coarser traits. Furthermore, in some systems, coarser traits may overestimate the strength of trait convergence, leading to erroneous support for abiotic processes as the primary drivers of community assembly or change.

Main conclusions: Inferences of assembly processes must account for trait resolution to ensure robust conclusions, especially for broad-scale studies of comparative community assembly and biodiversity change. Despite recent improvements in the collection and availability of trait data, great disparities continue to exist among taxa in the number and availability of continuous traits, which are more difficult to acquire for large numbers of species than coarse categorial assignments. Based on our simulations, we urge the consideration of trait resolution in the design and interpretation of community assembly studies and suggest a suite of practical solutions to address the pitfalls of trait resolution biases.

Global Biotic Interactions: Interpreted Data Products

Global Biotic Interactions (GloBI, https://globalbioticinteractions.org, [1]) aims to facilitate access to existing species interaction records (e.g., predator-prey, plant-pollinator, virus-host). This data publication provides interpreted species interaction data products. These products are the result of a process in which versioned, existing species interaction datasets ([2]) are linked to the so-called GloBI Taxon Graph ([3]) and transformed into various aggregate formats (e.g., tsv, csv, neo4j, rdf/nquad, darwin core-ish archives). In addition, the applied name maps are included to make the applied taxonomic linking explicit.

Citation
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GloBI is made possible by researchers, collections, projects and institutions openly sharing their datasets. When using this data, please make sure to attribute these *original data contributors*, including citing the specific datasets in derivative work. Each species interaction record indexed by GloBI contains a reference and dataset citation. Also, a full lists of all references can be found in citations.csv/citations.tsv files in this publication. If you have ideas on how to make it easier to cite original datasets, please open/join a discussion via https://globalbioticinteractions.org or related projects.

To credit GloBI for more easily finding interaction data, please use the following citation to reference GloBI:

Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005.

Bias and Errors
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As with any analysis and processing workflow, care should be taken to understand the bias and error propagation of data sources and related data transformation processes. The datasets indexed by GloBI are biased geospatially, temporally and taxonomically ([5], [6]). Also, mapping of verbatim names from datasets to known name concept may contains errors due to synonym mismatches, outdated names lists, typos or conflicting name authorities. Finally, bugs may introduce bias and errors in the resulting integrated data product.

To help better understand where bias and errors are introduced, only versioned data and code are used as an input: the datasets ([2]), name maps ([3]) and integration software ([6]) are versioned so that the integration processes can be reproduced if needed. This way, steps take to compile an integrated data record can be traced and the sources of bias and errors can be more easily found.

This version was preceded by [7].

Contents
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README:
this file

citations.csv.gz:
contains data citations in a in a gzipped comma-separated values format.

citations.tsv.gz:
contains data citations in a gzipped tab-separated values format.

datasets.csv.gz:
contains list of indexed datasets in a gzipped comma-separated values format.

datasets.tsv.gz:
contains list of indexed datasets in a gzipped tab-separated values format.

verbatim-interactions.csv.gz
contains species interactions tabulated as pair-wise interaction in a gzipped comma-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources.

verbatim-interactions.tsv.gz
contains species interactions tabulated as pair-wise interaction in a gzipped tab-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources.

interactions.csv.gz:
contains species interactions tabulated as pair-wise interactions in a gzipped comma-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources.

interactions.tsv.gz:
contains species interactions tabulated as pair-wise interactions in a gzipped tab-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources.

refuted-interactions.csv.gz:
contains refuted species interactions tabulated as pair-wise interactions in a gzipped comma-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources.

refuted-interactions.tsv.gz:
contains refuted species interactions tabulated as pair-wise interactions in a gzipped tab-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources.

refuted-verbatim-interactions.csv.gz:
contains refuted species interactions tabulated as pair-wise interactions in a gzipped comma-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources.

refuted-verbatim-interactions.tsv.gz:
contains refuted species interactions tabulated as pair-wise interactions in a gzipped tab-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources.

interactions.nq.gz:
contains species interactions expressed in the resource description framework in a gzipped rdf/quads format.

dwca-by-study.zip:
contains species interactions data as a Darwin Core Archive aggregated by study using a custom, occurrence level, association extension.

dwca.zip:
contains species interactions data as a Darwin Core Archive using a custom, occurrence level, association extension.

neo4j-graphdb.zip:
contains a neo4j v3.5.32 graph database snapshot containing a graph representation of the species interaction data.

taxonCache.tsv.gz:
contains hierarchies and identifiers associated with names from naming schemes in a gzipped tab-separated values format.

taxonMap.tsv.gz:
describes how names in existing datasets were mapped into existing naming schemes in a gzipped tab-separated values format.

References
-----

[1] Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. doi: 10.1016/j.ecoinf.2014.08.005.

[2] Poelen, J. H. (2020) Global Biotic Interactions: Elton Dataset Cache. Zenodo. doi: 10.5281/ZENODO.3950557.

[3] Poelen, J. H. (2021). Global Biotic Interactions: Taxon Graph (Version 0.3.28) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4451472

[4] Hortal, J. et al. (2015) Seven Shortfalls that Beset Large-Scale Knowledge of Biodiversity. Annual Review of Ecology, Evolution, and Systematics, 46(1), pp.523–549. doi: 10.1146/annurev-ecolsys-112414-054400.

[5] Cains, M. et al. (2017) Ivmooc 2017 - Gap Analysis Of Globi: Identifying Research And Data Sharing Opportunities For Species Interactions. Zenodo. Zenodo. doi: 10.5281/ZENODO.814978.

[6] Poelen, J. et al. (2022) globalbioticinteractions/globalbioticinteractions v0.24.6. Zenodo. doi: 10.5281/ZENODO.7327955.

[7] GloBI Community. (2023). Global Biotic Interactions: Interpreted Data Products hash://md5/89797a5a325ac5c50990581689718edf hash://sha256/946178b36c3ea2f2daa105ad244cf5d6cd236ec8c99956616557cf4e6666545b (0.6) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8284068

Content References
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The scientific community has entered an era of big data. However, with big data comes big responsibilities, and best practices for how data are contributed to databases have not kept pace with the collection, aggregation, and analysis of big data. Here, we rigorously assess the quantity of data for specific leaf area (SLA) available within the largest and most frequently used global plant trait database, the TRY Plant Trait Database, exploring how much of the data were applicable (i.e., original, representative, logical, and comparable) and traceable (i.e., published, cited, and consistent). Over three-quarters of the SLA data in TRY either lacked applicability or traceability, leaving only 22.9% of the original data usable compared to the 64.9% typically deemed usable by standard data cleaning protocols. The remaining usable data differed markedly from the original for many species, which led to altered interpretation of ecological analyses. Though the data we consider here make up onl..., SLA data was downlaoded from TRY (traits 3115, 3116, and 3117) for all conifer (Araucariaceae, Cupressaceae, Pinaceae, Podocarpaceae, Sciadopityaceae, and Taxaceae), Plantago, Poa, and Quercus species. The data has not been processed in any way, but additional columns have been added to the datset that provide the viewer with information about where each data point came from, how it was cited, how it was measured, whether it was uploaded correctly, whether it had already been uploaded to TRY, and whether it was uploaded by the individual who collected the data., , There are two additional documents associated with this publication. One is a word document that includes a description of each of the 120 datasets that contained SLA data for the four plant groups within the study (conifers, Plantago, Poa, and Quercus). The second is an excel document that contains the SLA data that was downloaded from TRY and all associated metadata.

Missing data codes: NA and N/A

Yearly citation counts for the publication titled "PERCEIVING AND INTERPRETING ENVIRONMENTAL CHANGE: AN EXAMINATION OF COLLEGE ADMINISTRATORS' INTERPRETATION OF CHANGING DEMOGRAPHICS.".