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Erica Fleishman - One of the best experts on this subject based on the ideXlab platform.
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using Indicator Species to predict Species richness of multiple taxonomic groups
Conservation Biology, 2005Co-Authors: Erica Fleishman, James Robertson Thomson, Ralph Charles Mac Nally, Dennis D Murphy, John P FayAbstract:: Values of Species richness are used widely to establish conservation and management priorities. Because inventory data, money, and time are limited, use of surrogates such as “Indicator” Species to estimate Species richness has become common. Identifying sets of Indicator Species that might reliably predict Species richness, especially across taxonomic groups, remains a considerable challenge. We used genetic algorithms and a Bayesian approach to explain individual and combined Species richness of two taxonomic groups as a function of occurrence patterns of Indicator Species drawn from either both groups or one group. Genetic algorithms iteratively screen large numbers of potential models and predictor variables in a process that emulates natural selection. The best-fitting models of bird Species richness and butterfly Species richness explained approximately 80% of deviances and included only Indicator Species from the same taxonomic group. Using Species from both taxonomic groups as potential predictors did not improve model fit but slightly improved the parsimony (fewer predictors) of the model of bird Species richness. The best model of combined Species richness included five butterflies and one bird and explained 83% of deviance, whereas a model of combined Species richness based on six butterflies as Indicators explained 82% of deviance. A model of combined Species richness based on birds alone explained 72% of deviance. We found that a small, common set of Species could be used to predict separately the Species richness of multiple taxonomic groups. We built models explaining approximately 70% of the deviance in Species richness of birds and butterflies based on a common set of three bird Species and three butterfly Species. We also identified a set of six Species of butterflies that predicted ≥66% of both bird Species richness and butterfly Species richness. Our approach is applicable to any assemblage or ecosystem, and may be useful both for estimating Species richness and for gaining insight into mechanisms that influence diversity patterns. Resumen: Los valores de riqueza de eSpecies son ampliamente utilizados para definir prioridades de conservacion y manejo. Debido a que los datos de inventarios, el dinero y el tiempo son limitados, se ha vuelto comun el uso de sustitutos, como las eSpecies “indicadoras,” para estimar la riqueza de eSpecies. La identificacion de conjuntos de eSpecies indicadoras que pronostiquen la riqueza de eSpecies confiablemente, especialmente en varios grupos taxonomicos, es un reto importante. Utilizamos algoritmos geneticos y un metodo Bayesiano para explicar las riquezas de eSpecies individuales y combinadas de dos grupos taxonomico como una funcion de patrones de ocurrencia de eSpecies indicadoras extraidas de ambos grupos o de uno. Los algoritmos geneticos reiterativamente filtran grandes numeros de modelos potenciales y variables predictoras en un proceso que emula a la seleccion natural. Los modelos que mejor se ajustaron a la riqueza de eSpecies de aves y de mariposas explicaron aproximadamente 80% de las anormalidades e incluyeron solo a eSpecies indicadoras del mismo grupo taxonomico. Utilizando a eSpecies de ambos grupos taxonomicos como predictores potenciales no mejoro el ajuste del modelo pero mejoro ligeramente la parsimonia (menos predictores) del modelo de riqueza de eSpecies de aves. El mejor modelo de la riqueza de eSpecies combinada incluyo a cinco eSpecies de mariposas y una de ave y explico 83% de la anormalidad, mientras que un modelo de riqueza de eSpecies combinadas basada en seis eSpecies de mariposas explico 82% de la anormalidad. Un modelo de riqueza de eSpecies combinadas basado solo en aves explico 72% de la anormalidad. Encontramos que un conjunto pequeno, comun, podria ser utilizado para pronosticar, por separado, la riqueza de eSpecies de multiples grupos taxonomicos. Construimos modelos que explicaron aproximadamente 70% de la anormalidad en la riqueza de eSpecies de aves y mariposas con base en un conjunto comun de tres eSpecies de aves y tres de mariposas. Tambien identificamos un conjunto de seis eSpecies de mariposas que predijeron ≥ 66% de la riqueza de eSpecies tanto de aves como de mariposas. Nuestro metodo es aplicable a cualquier ensamble o ecosistema, y puede ser util tanto para estimar la riqueza de eSpecies como para incrementar el entendimiento de los mecanismos que influyen sobre los patrones de diversidad.
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Influence of the temporal resolution of data on the success of Indicator Species models of Species richness across multiple taxonomic groups
Biological Conservation, 2005Co-Authors: James Robertson Thomson, Erica Fleishman, Ralph Charles Mac Nally, David S. DobkinAbstract:Abstract Indicator Species models may be a cost-effective approach to estimating Species richness across large areas. Obtaining reliable distributional data for Indicator Species (and therefore reliable estimates of Species richness) often requires longitudinal data, that is, surveys for Indicator Species repeated for several years or time steps. Maximum information must be extracted from such data. We used genetic algorithms and a Bayesian approach to compare the influence of presence/absence data and reporting rate data (the proportion of survey years in which a Species was present) on models of Species richness based on Indicator Species. Using data on birds and butterflies from the Great Basin (Nevada, USA), we evaluated models of Species richness for one taxonomic group based on Indicator Species drawn from the same taxonomic group and from a different group. We also evaluated models of combined Species richness of both taxonomic groups based on Indicator Species drawn from either group. We identified suites of Species whose occurrence patterns explained as much as 70% of deviance in Species richness of a different taxonomic group. Validation tests revealed strong correlations between observed and predicted Species richness, with 83–100% of the observed values falling within the 95% credible intervals of the predictions. Whether reporting rate data improved the explanatory and predictive ability of cross-taxonomic models depended on the taxonomic group of the Indicator Species. The discrepancy in predictive ability was smaller for same-taxon models. Our methods provide a manager with the means to maximize the information obtained from longitudinal survey data.
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A successful predictive model of Species richness based on Indicator Species
Conservation Biology, 2004Co-Authors: Ralph Charles Mac Nally, Erica FleishmanAbstract:Because complete Species inventories are expensive and time-consuming, scientists and land man- agers seek techniques to alleviate logistic constraints on measuring Species richness, especially over large spatial scales. We developed a method to identify Indicators of Species richness that is applicable to any taxonomic group or ecosystem. In an initial case study, we found that a model based on the occurrence of five Indicator Species explained 88% of the deviance of Species richness of 56 butterflies in a mountain range in western North America. We validated model predictions and spatial transferability of the model using independent, newly collected data from another, nearby mountain range. Predicted and observed values of butterfly Species richness were highly correlated with 93% of the observed values falling within the 95% credible intervals of the predictions. We used a Bayesian approach to update the initial model with both the model-building and model-validation data sets. In the updated model, the effectiveness of three of the five Indicator Species was similar, whereas the effectiveness of two Species was reduced. The latter Species had more erratic distributions in the validation data set than in the original model-building data set. This objective method for identifying indi- cators of Species richness could substantially enhance our ability to conduct large-scale ecological assessments of any group of animals or plants in any geographic region and to make effective conservation decisions.
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using Indicator Species to model Species richness model development and predictions
Ecological Applications, 2002Co-Authors: Ralph Charles Mac Nally, Erica FleishmanAbstract:Reliable Indicators of Species richness (e.g., particular Species), if they can be found, offer potentially significant benefits for management planning. Few efficient and statistically valid methods for identifying potential Indicators of Species richness currently exist. We used Bayesian-based Poisson modeling to explore whether Species richness of butterflies in the Great Basin could be modeled as a function of the occurrence (presence or absence) of certain Species of butterflies. We used an extensive data set on the occurrence of butterflies of the Toquima Range (Nevada, USA) to build the models. Poisson models based on the occurrence of five and four Indicator Species explained 88% and 77% of the deviance of observed Species richness of resident and montane-resident butterfly assemblages, respectively. We then developed a test framework, including formally defined “rejection criteria,” for validating and refining the models. The sensitivity of the models to inventory intensity (number of years of da...
Carlo Ricotta - One of the best experts on this subject based on the ideXlab platform.
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A new method for Indicator Species analysis in the framework of multivariate analysis of variance
Journal of Vegetation Science, 2021Co-Authors: Carlo Ricotta, Sandrine Pavoine, Bruno Cerabolini, Valério PillarAbstract:Question In vegetation science, the compositional dissimilarity among two or more groups of plots is usually tested with dissimilarity‐based multivariate analysis of variance (db‐MANOVA), whereas the compositional characterization of the different groups is performed by means of Indicator Species analysis. Although db‐MANOVA and Indicator Species analysis are apparently very far from each other, the question we address here is: can we put both approaches under the same methodological umbrella? Methods We will show that for a specific class of dissimilarity measures, the partitioning of variation used in one‐factor db‐MANOVA can be additively decomposed into Species‐level values allowing us to identify the Species that contribute most to the compositional differences among the groups. The proposed method, for which we provide a simple R function, is illustrated with one small data set on Alpine vegetation sampled along a successional gradient. Conclusion The Species that contribute most to the compositional differences among the groups are preferentially concentrated in particular group of plots. Therefore, they can be appropriately called Indicator Species. This connects multivariate analysis of variance with Indicator Species analysis.
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From abundance-based to functional-based Indicator Species
Ecological Indicators, 2020Co-Authors: Carlo Ricotta, Alicia Teresa Rosario Acosta, Marco Caccianiga, Bruno Enrico Leone Cerabolini, Sandrine Godefroid, Marta CarboniAbstract:Abstract Indicator Species with high fidelity to a-priori defined groups of sites are a relevant tool to ecologically characterize plant or animal assemblages. The identification of Indicator or diagnostic Species is usually performed by summarizing the Species abundances within each group of sites. Species with high concentration in a given group of sites are considered diagnostic of that particular group. Among the methods proposed for the determination of Indicator Species, only very few have considered the Species functional traits. This is quite surprising, as Species influence ecosystem processes via their traits. Therefore, the Species functional traits should give a much better ecological characterization of a group of sites than the Species abundances. The aim of this paper is thus to use the Species functional characteristics to improve their diagnostic value. These characteristics include the Species functional traits and all Species-level Indicators of environmental association. The proposed method consists of combining the Species abundances and their functional characteristics into a single composite index, which can be interpreted as the Species fuzzy degree of compatibility with each group of sites. The interpretation of this index in terms of fuzzy set theory allows to introduce a high degree of flexibility in the computation of the Species diagnostic values. To show the behavior of the proposed index, two worked examples with data on Alpine vegetation in northern Italy and urban alien Species in the city of Brussels (Belgium) are used.
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Let the concept of Indicator Species be functional
Journal of Vegetation Science, 2015Co-Authors: Carlo Ricotta, Marta Carboni, Alicia Teresa Rosario AcostaAbstract:Aims The identification of diagnostic or Indicator Species with high fidelity to a given group of sites is an important step for the ecological characterization of habitats or community types. The determination of the degree of fidelity to a target group is traditionally performed by analysing the concentration of Species occurrences or abundances in different groups of sites. Surprisingly, although one of the main purposes of Indicator Species analysis is to give ecological meaning to groups of sites, none of the methods proposed to date take into account the functional ecology of diagnostic Species. Therefore, the question we address here is: can we use functional traits of Species to improve the diagnostic value of Indicator Species? Location Sand dune communities in central Italy. Methods In this paper we propose a two-step procedure for incorporating the functional traits of a given Species in the evaluation of its diagnostic value. For a given set of plots that are classified into different groups, first the Indicator Species that best characterize each group of plots are identified with the usual statistical tools based on Species occurrences. Next, the functional association between the Indicator Species and the target groups of plots is tested by measuring the functional distance between the Indicator Species and the centroids of all plots in the target group. A Species is positively associated with a group if its mean functional distance from all plot centroids in the group is significantly lower than expected. Results In this example, we show that the functional association of the Indicator Species with a given habitat type is represented by less Species than the association highlighted solely through Species occurrences and/or abundances. This subset of Species appears to better characterize the functional ecology of coastal dune plant assemblages and shows a higher diagnostic value in comparison with those obtained through the traditional Indicator analysis. Conclusions As functional traits are the main ecological attributes by which different Species influence ecosystem processes, we believe that the methodology proposed here provides a relevant tool for ecological applications as distinct as vegetation science, conservation biology or landscape management.
Ralph Charles Mac Nally - One of the best experts on this subject based on the ideXlab platform.
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using Indicator Species to predict Species richness of multiple taxonomic groups
Conservation Biology, 2005Co-Authors: Erica Fleishman, James Robertson Thomson, Ralph Charles Mac Nally, Dennis D Murphy, John P FayAbstract:: Values of Species richness are used widely to establish conservation and management priorities. Because inventory data, money, and time are limited, use of surrogates such as “Indicator” Species to estimate Species richness has become common. Identifying sets of Indicator Species that might reliably predict Species richness, especially across taxonomic groups, remains a considerable challenge. We used genetic algorithms and a Bayesian approach to explain individual and combined Species richness of two taxonomic groups as a function of occurrence patterns of Indicator Species drawn from either both groups or one group. Genetic algorithms iteratively screen large numbers of potential models and predictor variables in a process that emulates natural selection. The best-fitting models of bird Species richness and butterfly Species richness explained approximately 80% of deviances and included only Indicator Species from the same taxonomic group. Using Species from both taxonomic groups as potential predictors did not improve model fit but slightly improved the parsimony (fewer predictors) of the model of bird Species richness. The best model of combined Species richness included five butterflies and one bird and explained 83% of deviance, whereas a model of combined Species richness based on six butterflies as Indicators explained 82% of deviance. A model of combined Species richness based on birds alone explained 72% of deviance. We found that a small, common set of Species could be used to predict separately the Species richness of multiple taxonomic groups. We built models explaining approximately 70% of the deviance in Species richness of birds and butterflies based on a common set of three bird Species and three butterfly Species. We also identified a set of six Species of butterflies that predicted ≥66% of both bird Species richness and butterfly Species richness. Our approach is applicable to any assemblage or ecosystem, and may be useful both for estimating Species richness and for gaining insight into mechanisms that influence diversity patterns. Resumen: Los valores de riqueza de eSpecies son ampliamente utilizados para definir prioridades de conservacion y manejo. Debido a que los datos de inventarios, el dinero y el tiempo son limitados, se ha vuelto comun el uso de sustitutos, como las eSpecies “indicadoras,” para estimar la riqueza de eSpecies. La identificacion de conjuntos de eSpecies indicadoras que pronostiquen la riqueza de eSpecies confiablemente, especialmente en varios grupos taxonomicos, es un reto importante. Utilizamos algoritmos geneticos y un metodo Bayesiano para explicar las riquezas de eSpecies individuales y combinadas de dos grupos taxonomico como una funcion de patrones de ocurrencia de eSpecies indicadoras extraidas de ambos grupos o de uno. Los algoritmos geneticos reiterativamente filtran grandes numeros de modelos potenciales y variables predictoras en un proceso que emula a la seleccion natural. Los modelos que mejor se ajustaron a la riqueza de eSpecies de aves y de mariposas explicaron aproximadamente 80% de las anormalidades e incluyeron solo a eSpecies indicadoras del mismo grupo taxonomico. Utilizando a eSpecies de ambos grupos taxonomicos como predictores potenciales no mejoro el ajuste del modelo pero mejoro ligeramente la parsimonia (menos predictores) del modelo de riqueza de eSpecies de aves. El mejor modelo de la riqueza de eSpecies combinada incluyo a cinco eSpecies de mariposas y una de ave y explico 83% de la anormalidad, mientras que un modelo de riqueza de eSpecies combinadas basada en seis eSpecies de mariposas explico 82% de la anormalidad. Un modelo de riqueza de eSpecies combinadas basado solo en aves explico 72% de la anormalidad. Encontramos que un conjunto pequeno, comun, podria ser utilizado para pronosticar, por separado, la riqueza de eSpecies de multiples grupos taxonomicos. Construimos modelos que explicaron aproximadamente 70% de la anormalidad en la riqueza de eSpecies de aves y mariposas con base en un conjunto comun de tres eSpecies de aves y tres de mariposas. Tambien identificamos un conjunto de seis eSpecies de mariposas que predijeron ≥ 66% de la riqueza de eSpecies tanto de aves como de mariposas. Nuestro metodo es aplicable a cualquier ensamble o ecosistema, y puede ser util tanto para estimar la riqueza de eSpecies como para incrementar el entendimiento de los mecanismos que influyen sobre los patrones de diversidad.
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Influence of the temporal resolution of data on the success of Indicator Species models of Species richness across multiple taxonomic groups
Biological Conservation, 2005Co-Authors: James Robertson Thomson, Erica Fleishman, Ralph Charles Mac Nally, David S. DobkinAbstract:Abstract Indicator Species models may be a cost-effective approach to estimating Species richness across large areas. Obtaining reliable distributional data for Indicator Species (and therefore reliable estimates of Species richness) often requires longitudinal data, that is, surveys for Indicator Species repeated for several years or time steps. Maximum information must be extracted from such data. We used genetic algorithms and a Bayesian approach to compare the influence of presence/absence data and reporting rate data (the proportion of survey years in which a Species was present) on models of Species richness based on Indicator Species. Using data on birds and butterflies from the Great Basin (Nevada, USA), we evaluated models of Species richness for one taxonomic group based on Indicator Species drawn from the same taxonomic group and from a different group. We also evaluated models of combined Species richness of both taxonomic groups based on Indicator Species drawn from either group. We identified suites of Species whose occurrence patterns explained as much as 70% of deviance in Species richness of a different taxonomic group. Validation tests revealed strong correlations between observed and predicted Species richness, with 83–100% of the observed values falling within the 95% credible intervals of the predictions. Whether reporting rate data improved the explanatory and predictive ability of cross-taxonomic models depended on the taxonomic group of the Indicator Species. The discrepancy in predictive ability was smaller for same-taxon models. Our methods provide a manager with the means to maximize the information obtained from longitudinal survey data.
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A successful predictive model of Species richness based on Indicator Species
Conservation Biology, 2004Co-Authors: Ralph Charles Mac Nally, Erica FleishmanAbstract:Because complete Species inventories are expensive and time-consuming, scientists and land man- agers seek techniques to alleviate logistic constraints on measuring Species richness, especially over large spatial scales. We developed a method to identify Indicators of Species richness that is applicable to any taxonomic group or ecosystem. In an initial case study, we found that a model based on the occurrence of five Indicator Species explained 88% of the deviance of Species richness of 56 butterflies in a mountain range in western North America. We validated model predictions and spatial transferability of the model using independent, newly collected data from another, nearby mountain range. Predicted and observed values of butterfly Species richness were highly correlated with 93% of the observed values falling within the 95% credible intervals of the predictions. We used a Bayesian approach to update the initial model with both the model-building and model-validation data sets. In the updated model, the effectiveness of three of the five Indicator Species was similar, whereas the effectiveness of two Species was reduced. The latter Species had more erratic distributions in the validation data set than in the original model-building data set. This objective method for identifying indi- cators of Species richness could substantially enhance our ability to conduct large-scale ecological assessments of any group of animals or plants in any geographic region and to make effective conservation decisions.
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using Indicator Species to model Species richness model development and predictions
Ecological Applications, 2002Co-Authors: Ralph Charles Mac Nally, Erica FleishmanAbstract:Reliable Indicators of Species richness (e.g., particular Species), if they can be found, offer potentially significant benefits for management planning. Few efficient and statistically valid methods for identifying potential Indicators of Species richness currently exist. We used Bayesian-based Poisson modeling to explore whether Species richness of butterflies in the Great Basin could be modeled as a function of the occurrence (presence or absence) of certain Species of butterflies. We used an extensive data set on the occurrence of butterflies of the Toquima Range (Nevada, USA) to build the models. Poisson models based on the occurrence of five and four Indicator Species explained 88% and 77% of the deviance of observed Species richness of resident and montane-resident butterfly assemblages, respectively. We then developed a test framework, including formally defined “rejection criteria,” for validating and refining the models. The sensitivity of the models to inventory intensity (number of years of da...
D J Gaughan - One of the best experts on this subject based on the ideXlab platform.
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a risk assessment and prioritisation approach to the selection of Indicator Species for the assessment of multi Species multi gear multi sector fishery resources
Marine Policy, 2018Co-Authors: Stephen J. Newman, Joshua I Brown, Lynda M Bellchambers, R C J Lenanton, Kim A Smith, David V. Fairclough, B. S. Wise, Brett W. Molony, Gary Jackson, D J GaughanAbstract:Assessing the stock status of mixed and/or multi-Species fishery resources is challenging. This is especially true in highly diverse systems, where landed catches are small, but comprise many Species. In these circumstances, whole-of-ecosystem management requires consideration of the impact of harvesting on a plethora of Species. However, this is logistically infeasible and cost prohibitive. To overcome this issue, selected ‘Indicator’ Species are used to assess the risk to sustainability of all ‘like’ Species susceptible to capture within a fishery resource. Indicator Species are determined via information on their (1) inherent vulnerability, i.e. biological attributes; (2) risk to sustainability, i.e. stock status; and (3) management importance, i.e. commercial prominence, social and/or cultural amenity value of the resource. These attributes are used to determine an overall score for each Species which is used to identify ‘Indicator’ Species. The risk status (i.e. current risk) of the Indicator Species then determines the risk-level for the biological sustainability of the entire fishery resource and thus the level of priority for management, monitoring, assessment and compliance. A range of fishery management regimes are amenable to the Indicator Species approach, including both effort limited fisheries (e.g. individually transferable effort systems) and output controlled fisheries (e.g. Species-specific catch quotas). The Indicator Species approach has been used and refined for fisheries resources in Western Australia over two decades. This process is now widely understood and accepted by stakeholders, as it focuses fishery dependent- and/or independent-monitoring, biological sampling, stock assessment and compliance priorities, thereby optimising the use of available jurisdictional resources.
David B Lindenmayer - One of the best experts on this subject based on the ideXlab platform.
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direct measurement versus surrogate Indicator Species for evaluating environmental change and biodiversity loss
Ecosystems, 2011Co-Authors: David B Lindenmayer, Gene E LikensAbstract:The enormity and complexity of problems like environmental degradation and biodiversity loss have led to the development of Indicator Species and other surrogate approaches to track changes in environments and/or in biodiversity. Under these approaches particular Species or groups of Species are used as proxies for other biota, particular environmental conditions, or for environmental change. The Indicator Species approach contrasts with a direct measurement approach in which the focus is on a single entity or a highly targeted subset of entities in a given ecosystem but no surrogacy relationships with unmeasured entities are assumed. Here, we present a broad philosophical discussion of the Indicator Species and direct measurement approaches because their relative advantages and disadvantages are not well understood by many researchers, resource managers and policy makers. A goal of the direct measurement approach is to demonstrate a causal relationship between key attributes of the target ecosystem system (for example, particular environmental conditions) and the entities selected for measurement. The key steps in the approach are based on the fundamental scientific principles of hypothesis testing and associated direct measurement that drive research activities, management activities and monitoring programs. The direct measurement approach is based on four critical assumptions:(1) the ‘right’ entities to measure have been selected, (2) these entities are well known, (3) there is sufficient understanding about key ecological processes and (4) the entities selected can be accurately measured. The direct measurement approach is reductionist and many elements of the biota, many biotic processes and environmental factors must be ignored because of practical considerations. The steps in applying the Indicator Species approach are broadly similar to the direct measurement approach, except surrogacy relationships also must be quantified between a supposed Indicator Species or Indicator group and the factors for which it is purported to be a proxy. Such quantification needs to occur via: (1) determining the taxonomic, spatial and temporal bounds for which a surrogacy relationship does and does not hold. That is, the extent of transferability of a given surrogate such as an Indicator Species to other biotic groups, to landscapes, ecosystems, environmental circumstances or over time in the same location can be determined; and (2) determining the ecological mechanisms underpinning a surrogacy relationship (for example, through fundamental studies of community structure). Very few studies have rigorously addressed these two tasks, despite the extremely widespread use of the Indicator Species approach and similar kinds of surrogate schemes in virtually all fields of environmental, resource and conservation management. We argue that this has the potential to create significant problems; thus, the use of an Indicator Species approach needs to be better justified. Attempts to quantify surrogacy relationships may reveal that, in some circumstances, the alternative of direct measurement of particular entities of environmental or conservation interest will be the best option.
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future directions for biodiversity conservation in managed forests Indicator Species impact studies and monitoring programs
Forest Ecology and Management, 1999Co-Authors: David B LindenmayerAbstract:Abstract The validity and use of the Indicator Species concept, the design of logging impact studies, the need for long-term monitoring programs and how they might be designed, and, trade-offs between conservation strategies and economic costs are topics critical to the future direction of biological conservation in managed forests. The Indicator Species concept can make an important contribution to biodiversity conservation because of the impossibility of monitoring all taxa in Species-rich forest environments. However, the concept has yet to be rigourously tested by validating relationships between an Indicator Species and entities for which it is hypothesized to be indicative. There can be serious negative consequences if the Indicator Species concept is incorrectly applied or inappropriate Species are selected as Indicators. Long-term monitoring will be critical for assessing not only the validity of concepts like Indicator Species, but also for appraising other rarely tested approaches to conservation in managed forests such as: (1) stand level management strategies to create and maintain key structural and floristic attributes that form critical habitat components for wildlife (e.g. large living and dead trees), (2) landscape level strategies to ensure the maintenance of landscape heterogeneity and connectivity, such as the establishment of networks of riparian protection zones and wildlife corridors, and, (3) landscape and regional level management involving the identification of reserves. While the importance of monitoring is often discussed, more programs are needed to gather the data needed to inform the development of ecologically sustainable forest management practices. Treating forestry activities as an experiment and overlaying well-designed monitoring programs on such disturbance regimes is one useful way to accumulate key information on the effects of logging on biodiversity and how to mitigate such impacts. However, some major changes will be needed to instigate greater commitment to monitoring programs. These include: (1) identifying innovative ways to secure long-term funding that can be guaranteed beyond typical political and institutional timeframes, (2) education of funding bodies to ensure they recognise that useful results may take a prolonged period to obtain and that monitoring is not a second-rate science, (3) greater participation in the design and execution of monitoring programs by the scientific community, and, (4) stronger links among researchers and between researchers and managers to both improve the quality and validity of monitoring studies and to ensure that the results of such programs are incorporated into management practices.
