Sampling coarse woody debris for multiple attributes in extensive resource inventories

Information on the amount, distribution, and characteristics of coarse woody debris (CWD) in forest ecosystems is in high demand by wildlife biologists, fire specialists, and ecologists. In its important role in wildlife habitat, fuel loading, forest productivity, and carbon sequestration, CWD is an indicator of forest health. Because of this, the USDA Forest Service Pacific Northwest Research Station’s Forest Inventory and Analysis (FIA) program recognized the need to collect data on CWD in their extensive resource inventories. This paper describes a sampling method, measurement protocols, and estimation procedures to collect and compile data on CWD attributes within FIA’s forest inventory. The line-intersect method was used to sample CWD inside the boundaries of the standard inventory field plot. Previously published equations were customized to allow for easy calculation of per-unit-area values, such as biomass and carbon per hectare, log density per hectare, or volume per hectare, for each plot. These estimates are associated with all other information recorded or calculated for an inventory plot. This allows for indepth analysis of CWD data in relation to stand level characteristics. The data on CWD can be used to address current, relevant issues such as criteria no. 5 outlined in the 1994 Montreal process and the 1995 Santiago declaration. This criteria assesses the contribution of forests to the global carbon cycle by measuring such indicators as CWD, live plant biomass, and soil carbon.     

National forest inventories and biodiversity monitoring in Australia

Abstract

Forests currently cover over 20% of the Australian continent and are an important resource, subject to a wide range of economic and environmental pressures. These lands support substantial numbers of forest-dependent species with national forest inventories providing important information on biodiversity. National scale information on these forests has been collected or collated since 1988 under the National Forest Inventory (NFI) programme, but substantial problems with the ‘snap shot’ approach have been recognized, particularly with respect to monitoring change and a consequent move towards a permanent and sample-based continental forest monitoring framework (CFMF) has been proposed. CFMF is proposed to consist of three Tiers: (1) satellite imagery of the continent to identify forest and change in forest cover; (2) systematic high-resolution remotely sensed data and (3) permanent ground points at 20×20 km grid interception points. The CFMF approach is in line with the international trend of national forest inventories in developed countries although the Tier 2 approach offers a useful extension. An alternative inventory approach is provided by the National Carbon Accounting System (NCAS) which models the mass of carbon and nitrogen in seven separate living and dead biomass pools for any point under forest or agriculture land use since 1970. The NCAS approach allows fine spatial and temporal monitoring of changes in these carbon and nitrogen biomass pools, and predictions of changes that result from policy or management decisions. This paper briefly reviews NFI, NCAS and the proposed CFMF, with particular emphasis on issues of use and potential for monitoring biodiversity in this biologically very diverse country.     

Predicting fundamental and realized distributions based on thermal niche: A case study of a freshwater turtle

Species distribution models (SDM) have been broadly used in ecology to address theoretical and practical problems. Currently, there are two main approaches to generate SDMs: (i) correlative, which is based on species occurrences and environmental predictor layers and (ii) process-based models, which are constructed based on species' functional traits and physiological tolerances. The distributions estimated by each approach are based on different components of species niche. Predictions of correlative models approach species realized niches, while predictions of process-based are more akin to species fundamental niche. Here, we integrated the predictions of fundamental and realized distributions of the freshwater turtle Trachemys dorbigni. Fundamental distribution was estimated using data of T. dorbigni's egg incubation temperature, and realized distribution was estimated using species occurrence records. Both types of distributions were estimated using the same regression approaches (logistic regression and support vector machines), both considering macroclimatic and microclimatic temperatures. The realized distribution of T. dorbigni was generally nested in its fundamental distribution reinforcing theoretical assumptions that the species' realized niche is a subset of its fundamental niche. Both modelling algorithms produced similar results but microtemperature generated better results than macrotemperature for the incubation model. Finally, our results reinforce the conclusion that species realized distributions are constrained by other factors other than just thermal tolerances.     

From Models to Measurements: Comparing Downed Dead Wood Carbon Stock Estimates in the U.S. Forest Inventory

The inventory and monitoring of coarse woody debris (CWD) carbon (C) stocks is an essential component of any comprehensive National Greenhouse Gas Inventory (NGHGI). Due to the expense and difficulty associated with conducting field inventories of CWD pools, CWD C stocks are often modeled as a function of more commonly measured stand attributes such as live tree C density. In order to assess potential benefits of adopting a field-based inventory of CWD C stocks in lieu of the current model-based approach, a national inventory of downed dead wood C across the U.S. was compared to estimates calculated from models associated with the U.S.’s NGHGI and used in the USDA Forest Service, Forest Inventory and Analysis program. The model-based population estimate of C stocks for CWD (i.e., pieces and slash piles) in the conterminous U.S. was 9 percent (145.1 Tg) greater than the field-based estimate. The relatively small absolute difference was driven by contrasting results for each CWD component. The model-based population estimate of C stocks from CWD pieces was 17 percent (230.3 Tg) greater than the field-based estimate, while the model-based estimate of C stocks from CWD slash piles was 27 percent (85.2 Tg) smaller than the field-based estimate. In general, models overestimated the C density per-unit-area from slash piles early in stand development and underestimated the C density from CWD pieces in young stands. This resulted in significant differences in CWD C stocks by region and ownership. The disparity in estimates across spatial scales illustrates the complexity in estimating CWD C in a NGHGI. Based on the results of this study, it is suggested that the U.S. adopt field-based estimates of CWD C stocks as a component of its NGHGI to both reduce the uncertainty within the inventory and improve the sensitivity to potential management and climate change events.     

Climate change likely to reduce orchid bee abundance even in climatic suitable sites

Studies have tested whether model predictions based on species’ occurrence can predict the spatial pattern of population abundance. The relationship between predicted environmental suitability and population abundance varies in shape, strength and predictive power. However, little attention has been paid to the congruence in predictions of different models fed with occurrence or abundance data, in particular when comparing metrics of climate change impact. Here, we used the ecological niche modeling fit with presence–absence and abundance data of orchid bees to predict the effect of climate change on species and assembly level distribution patterns. In addition, we assessed whether predictions of presence–absence models can be used as a proxy to abundance patterns. We obtained georeferenced abundance data of orchid bees (Hymenoptera: Apidae: Euglossina) in the Brazilian Atlantic Forest. Sampling method consisted in attracting male orchid bees to baits of at least five different aromatic compounds and collecting the individuals with entomological nets or bait traps. We limited abundance data to those obtained by similar standard sampling protocol to avoid bias in abundance estimation. We used boosted regression trees to model ecological niches and project them into six climate models and two Representative Concentration Pathways. We found that models based on species occurrences worked as a proxy for changes in population abundance when the output of the models were continuous; results were very different when outputs were discretized to binary predictions. We found an overall trend of diminishing abundance in the future, but a clear retention of climatically suitable sites too. Furthermore, geographic distance to gained climatic suitable areas can be very short, although it embraces great variation. Changes in species richness and turnover would be concentrated in western and southern Atlantic Forest. Our findings offer support to the ongoing debate of suitability–abundance models and can be used to support spatial conservation prioritization schemes and species triage in Atlantic Forest.     

Integrating expert knowledge and ecological niche models to estimate Mexican primates’ distribution

Ecological niche modeling is used to estimate species distributions based on occurrence records and environmental variables, but it seldom includes explicit biotic or historical factors that are important in determining the distribution of species. Expert knowledge can provide additional valuable information regarding ecological or historical attributes of species, but the influence of integrating this information in the modeling process has been poorly explored. Here, we integrated expert knowledge in different stages of the niche modeling process to improve the representation of the actual geographic distributions of Mexican primates (Ateles geoffroyi, Alouatta pigra, and A. palliata mexicana). We designed an elicitation process to acquire information from experts and such information was integrated by an iterative process that consisted of reviews of input data by experts, production of ecological niche models (ENMs), and evaluation of model outputs to provide feedback. We built ENMs using the maximum entropy algorithm along with a dataset of occurrence records gathered from a public source and records provided by the experts. Models without expert knowledge were also built for comparison, and both models, with and without expert knowledge, were evaluated using four validation metrics that provide a measure of accuracy for presence-absence predictions (specificity, sensitivity, kappa, true skill statistic). Integrating expert knowledge to build ENMs produced better results for potential distributions than models without expert knowledge, but a much greater improvement in the transition from potential to realized geographic distributions by reducing overprediction, resulting in better representations of the actual geographic distributions of species. Furthermore, with the combination of niche models and expert knowledge we were able to identify an area of sympatry between A. palliata mexicana and A. pigra. We argue that the inclusion of expert knowledge at different stages in the construction of niche models in an explicit and systematic fashion is a recommended practice as it produces overall positive results for representing realized species distributions.     

The use of forest inventory data for a National Protected Area Strategy in Guyana

Forest inventories are largely neglected in the debate of national parks selection in Guyana (and probably elsewhere). Because taxonomic data are often scant and biased towards are as of high collecting effort, large scale forest inventory data can be a useful tool adding to a knowledge database for forests. In this paper the use of forest inventories to select national parks in Guyana is assessed. With the data of a large scale inventory five forest regions could be distinguished and two were added on the base of existing other information. Forest composition in Guyana is largely determined by geology at a national level and soil type at regional level. Species diversity is higher in the south of Guyana, possibly due to higher disturbance and is also higher on the better soils. It is concluded that a selection of national parks in Guyana should include a sample of all seven regions, including as much soil variation as possible. Because of land use conflicts in central Guyana, this area is in need of quick attention of Guyana's policy makers.     

Using Publicly Available Forest Inventory Data in Climate-Based Models of Tree Species Distribution: Examining Effects of True Versus Altered Location Coordinates

Species distribution models (SDMs) were built with US Forest Inventory and Analysis (FIA) publicly available plot coordinates, which are altered for plot security purposes, and compared with SDMs built with true plot coordinates. Six species endemic to the western US, including four junipers (Juniperus deppeana var. deppeana, J. monosperma, J. occidentalis, J. osteosperma) and two piñons (Pinus edulis, P. monophylla), were analyzed. The presence–absence models based on current climatic variables were generated over a series of species-specific modeling extents using Random Forests and applied to forecast climatic conditions. The distributions of predictor variables sampled with public coordinates were compared to those sampled with true coordinates using t tests with a Bonferroni adjustment for multiple comparisons. Public- and true-based models were compared using metrics of classification accuracy. The modeled current and forecast distributions were compared in terms of their overall areal agreement and their geographic mean centroids. Comparison of the underlying distributions of predictor variables sampled with true versus public coordinates did not indicate a significant difference for any species at any extent. Both the public- and true-based models had comparable classification accuracies across extent for each species, with the exception of one species, J. occidentalis. True-based models produced geographic distributions with smaller areas under current and future scenarios. The greatest areal difference occurred in the species with the lowest modeled accuracies (J. occidentalis), and had a forecast distribution which diverged severely. The other species had forecast distributions with similar magnitudes of modeled distribution shifts.     

Spatially autocorrelated sampling falsely inflates measures of accuracy for presence‐only niche models

Aim Environmental niche models that utilize presence‐only data have been increasingly employed to model species distributions and test ecological and evolutionary predictions. The ideal method for evaluating the accuracy of a niche model is to train a model with one dataset and then test model predictions against an independent dataset. However, a truly independent dataset is often not available, and instead random subsets of the total data are used for ‘training’ and ‘testing’ purposes. The goal of this study was to determine how spatially autocorrelated sampling affects measures of niche model accuracy when using subsets of a larger dataset for accuracy evaluation. Location The distribution of Centaurea maculosa (spotted knapweed; Asteraceae) was modelled in six states in the western United States: California, Oregon, Washington, Idaho, Wyoming and Montana. Methods Two types of niche modelling algorithms – the genetic algorithm for rule‐set prediction (GARP) and maximum entropy modelling (as implemented with Maxent) – were used to model the potential distribution of C. maculosa across the region. The effect of spatially autocorrelated sampling was examined by applying a spatial filter to the presence‐only data (to reduce autocorrelation) and then comparing predictions made using the spatial filter with those using a random subset of the data, equal in sample size to the filtered data. Results The accuracy of predictions from both algorithms was sensitive to the spatial autocorrelation of sampling effort in the occurrence data. Spatial filtering led to lower values of the area under the receiver operating characteristic curve plot but higher similarity statistic (I) values when compared with predictions from models built with random subsets of the total data, meaning that spatial autocorrelation of sampling effort between training and test data led to inflated measures of accuracy. Main conclusions The findings indicate that care should be taken when interpreting the results from presence‐only niche models when training and test data have been randomly partitioned but occurrence data were non‐randomly sampled (in a spatially autocorrelated manner). The higher accuracies obtained without the spatial filter are a result of spatial autocorrelation of sampling effort between training and test data inflating measures of prediction accuracy. If independently surveyed data for testing predictions are unavailable, then it may be necessary to explicitly account for the spatial autocorrelation of sampling effort between randomly partitioned training and test subsets when evaluating niche model predictions.     

The effect of positional error on fine scale species distribution models increases for specialist species

Species occurrences inherently include positional error. Such error can be problematic for species distribution models (SDMs), especially those based on fine-resolution environmental data. It has been suggested that there could be a link between the influence of positional error and the width of the species ecological niche. Although positional errors in species occurrence data may imply serious limitations, especially for modelling species with narrow ecological niche, it has never been thoroughly explored. We used a virtual species approach to assess the effects of the positional error on fine-scale SDMs for species with environmental niches of different widths. We simulated three virtual species with varying niche breadth, from specialist to generalist. The true distribution of these virtual species was then altered by introducing different levels of positional error (from 5 to 500 m). We built generalized linear models and MaxEnt models using the distribution of the three virtual species (unaltered and altered) and a combination of environmental data at 5 m resolution. The models’ performance and niche overlap were compared to assess the effect of positional error with varying niche breadth in the geographical and environmental space. The positional error negatively impacted performance and niche overlap metrics. The amplitude of the influence of positional error depended on the species niche, with models for specialist species being more affected than those for generalist species. The positional error had the same effect on both modelling techniques. Finally, increasing sample size did not mitigate the negative influence of positional error. We showed that fine-scale SDMs are considerably affected by positional error, even when such error is low. Therefore, where new surveys are undertaken, we recommend paying attention to data collection techniques to minimize the positional error in occurrence data and thus to avoid its negative effect on SDMs, especially when studying specialist species.     

Prediction of biodiversity hotspots in the Anthropocene: The case of veteran oaks

Over the past centuries, humans have transformed large parts of the biosphere, and there is a growing need to understand and predict the distribution of biodiversity hotspots influenced by the presence of humans. Our basic hypothesis is that human influence in the Anthropocene is ubiquitous, and we predict that biodiversity hot spot modeling can be improved by addressing three challenges raised by the increasing ecological influence of humans: (i) anthropogenically modified responses to individual ecological factors, (ii) fundamentally different processes and predictors in landscape types shaped by different land use histories and (iii) a multitude and complexity of natural and anthropogenic processes that may require many predictors and even multiple models in different landscape types. We modeled the occurrence of veteran oaks in Norway, and found, in accordance with our basic hypothesis and predictions, that humans influence the distribution of veteran oaks throughout its range, but in different ways in forests and open landscapes. In forests, geographical and topographic variables related to the oak niche are still important, but the occurrence of veteran oaks is shifted toward steeper slopes, where logging is difficult. In open landscapes, land cover variables are more important, and veteran oaks are more common toward the north than expected from the fundamental oak niche. In both landscape types, multiple predictor variables representing ecological and human‐influenced processes were needed to build a good model, and several models performed almost equally well. Models accounting for the different anthropogenic influences on landscape structure and processes consistently performed better than models based exclusively on natural biogeographical and ecological predictors. Thus, our results for veteran oaks clearly illustrate the challenges to distribution modeling raised by the ubiquitous influence of humans, even in a moderately populated region, but also show that predictions can be improved by explicitly addressing these anthropogenic complexities.     

Akaike information criterion should not be a “test” of geographical prediction accuracy in ecological niche modelling

Model complexity in ecological niche modelling has been recently considered as an important issue that might affect model performance. New methodological developments have implemented the Akaike information criterion (AIC) to capture model complexity in the Maxent algorithm model. AIC is calculated based on the number of parameters and likelihoods of continuous raw outputs. ENMeval R package allows users to perform a species-specific tuning of Maxent settings running models with different combinations of regularization multiplier and feature classes and finally, all these models are compared using AIC corrected for small sample size. This approach is focused to find the “best” model parametrization and it is thought to maximize the model complexity and therefore, its predictability. We found that most niche modelling studies examined by us (68%) tend to consider AIC as a criterion of predictive accuracy in geographical distribution. In other words, AIC is used as a criterion to choose those models with the highest capacity to discriminate between presences and absences. However, the link between AIC and geographical predictive accuracy has not been tested so far. Here, we evaluated this relationship using a set of simulated (virtual) species. We created a set of nine virtual species with different ecological and geographical traits (e.g., niche position, niche breadth, range size) and generated different sets of true presences and absences data across geography. We built a set of models using Maxent algorithm with different regularization values and features schemes and calculated AIC values for each model. For each model, we obtained binary predictions using different threshold criteria and validated using independent presence and absences data. We correlated AIC values against standard validation metrics (e.g., Kappa, TSS) and the number of pixels correctly predicted as presences and absences. We did not find a correlation between AIC values and predictive accuracy from validation metrics. In general, those models with the lowest AIC values tend to generate geographical predictions with high commission and omission errors. The results were consistent across all species simulated. Finally, we suggest that AIC should not be used if users are interested in prediction more than explanation in ecological niche modelling.     

The inventory of bryophytes at sites: completeness and survey effort

Abstract

Knowledge of the species present within a site is often used to inform decisions that have significant implications for biodiversity conservation. This study surveyed eight woodland sites in north-west England for bryophytes. Searches for species within each site continued until all areas had been approached to within a minimum of 50 m and at least 60 minutes had elapsed since the discovery of a new species. Survey data were used to build predictive models that provided an estimate of the total number of species present at each site and the time required to compile a complete inventory. The ‘50 m 60 minutes stopping rule’ consistently produced comprehensive inventories for sites, judging by the numbers of species found and model predictions of the total number of species present. The study suggests that a minor alteration to conventional survey practice and a small amount of data analysis can provide useful assessments of the completeness of bryophyte inventories for sites.     

Plot size matters: Toward comparable species richness estimates across plot‐based inventories

To understand the state and trends in biodiversity beyond the scope of monitoring programs, biodiversity indicators must be comparable across inventories. Species richness (SR) is one of the most widely used biodiversity indicators. However, as SR increases with the size of the area sampled, inventories using different plot sizes are hardly comparable. This study aims at producing a methodological framework that enables SR comparisons across plot‐based inventories with differing plot sizes. We used National Forest Inventory (NFI) data from Norway, Slovakia, Spain, and Switzerland to build sample‐based rarefaction curves by randomly incrementally aggregating plots, representing the relationship between SR and sampled area. As aggregated plots can be far apart and subject to different environmental conditions, we estimated the amount of environmental heterogeneity (EH) introduced in the aggregation process. By correcting for this EH, we produced adjusted rarefaction curves mimicking the sampling of environmentally homogeneous forest stands, thus reducing the effect of plot size and enabling reliable SR comparisons between inventories. Models were built using the Conway–Maxell–Poisson distribution to account for the underdispersed SR data. Our method successfully corrected for the EH introduced during the aggregation process in all countries, with better performances in Norway and Switzerland. We further found that SR comparisons across countries based on the country‐specific NFI plot sizes are misleading, and that our approach offers an opportunity to harmonize pan‐European SR monitoring. Our method provides reliable and comparable SR estimates for inventories that use different plot sizes. Our approach can be applied to any plot‐based inventory and count data other than SR, thus allowing a more comprehensive assessment of biodiversity across various scales and ecosystems.     

Prediction of bird community composition based on point-occurrence data and inferential algorithms: a valuable tool in biodiversity assessments

Local biological communities are made up of species, each of which has its own particular relationship with the environment. To the extent that these autecological niches limit species’ distributions, and by extension community composition, models of species’ ecological niches can predict species composition at particular sites, or at least provide a null hypothesis of potential species composition in the absence of effects of species interactions. We developed distributional predictions (ecological niche models) for 89 species occurring in dry tropical forest in the Balsas Basin of south‐western Mexico using an interpolation technique, and predicted the species likely to occur at 8 sites across the region. Onsite field inventory data were then used to test the community predictions, all of which were statistically significant. These results suggest that inventory efforts can be made more efficient by development beforehand of hypotheses that focus onsite collecting and inventory.     

Assessment of appropriate species-specific time intervals to integrate GPS telemetry data in ecological niche models

Ecological niche models use presence-only data, which is often affected by lack of true absences leading to sampling bias. Over the last decade, there has been an uptick in the integration of occurrence data from global positioning systems telemetry data in ecological niche models and/or species distribution models. These data types can be affected by serial autocorrelation at high relocation frequencies yet have been used in ecological niche models using geographic filters and subsampling techniques. Yet, no study to date has attempted to discern a method to identify the appropriate time interval for a particular species if integrating GPS telemetry occurrence data in a MaxEnt framework. We demonstrate a rigorous spatial technique using a robust contemporary dataset from ocelots (Leopardus pardalis) to assess the appropriate time intervals to use in a species-specific ecological niche model. We assessed a range of daily time intervals (every 0.5, 1–4, 6, 8, and 12 h) commonly used in teresstrial mammalian carnivore studies. We observed the predictive performance of shorter time intervals every 2 h was comparable to much longer intervals every 12 h. These shorter intervals under/overestimated the least amount of data compared to 12 h. This study demonstrates that by accounting for serial autocorrelation and conducting rigorous spatial analyses, scientists can identify the appropriate time interval to integrate GPS telemetry data use in ecological niche models in MaxEnt. These results can also be transferable across highly mobile terrestrial taxa at different spatial scales, which can help inform species management or conservation strategies.     

Coexistence of Abies alba (Mill.) - Fagus sylvatica (L.) and climate change impact in the Iberian Peninsula: A climatic-niche perspective approach

Abies alba Mill. (European silver fir) and Fagus sylvatica L. (beech) are Eurosiberian species dispersed over the Iberian Peninsula. Climate change predictions indicate a rise in temperature and a decrease in precipitation in this region, threatening the future existence of these species. In the present study we analyzed the future topo-climatic suitability of Abies alba and Fagus sylvatica and the mixed forests of these two species, using the General Linear Models technique and data from the third National Forest Inventory (Ministerio de Agricultura PyA, 2007). We considered two modeling approaches based on niche theory: modeling community (Abieti-Fagetum) and overlapping individual species models. General trends showed an overall decrease in both species’ topo-climatic suitability and indicated that the Pyrenees will play a crucial role as a climatic refuge. The modeling approaches markedly differed, however, in their current and future spatial agreement. Despite good accuracy results, community modeling through co-occurrence does not encompass the environmental space of individual species prejudicing future assessments in new environmental situations, suggesting a need for future studies in community modeling.     

Utilizing the density of inventory samples to define a hybrid lattice for species distribution models: DISTRIB‐II for 135 eastern U.S. trees

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