Ecological integrity of tropical secondary forests: concepts and indicators Milena F. Rosenfield1†,* , Catarina C. Jakovac2,3† , Daniel L. M. Vieira4, Lourens Poorter2 , Pedro H. S. Brancalion5, Ima C. G. Vieira6, Danilo R. A. de Almeida5, Paulo Massoca7, Juliana Schietti8, Ana Luisa M. Albernaz9, Marciel J. Ferreira10 and Rita C. G. Mesquita1 1Instituto Nacional de Pesquisas da Amazônia (INPA), Av. André Araújo, 2936, Manaus, AM, 69083-000, Brazil 2Forest Ecology and Forest Management Group, Wageningen University & Research, PO Box 47, 6700 AA, Wageningen, The Netherlands 3Centro de Ciências Agr�arias, Universidade Federal de Santa Catarina (UFSC), Rod. Admar Gonzaga, 1346, Itacorubi, Florian�opolis, SC, 88034-000, Brazil 4Embrapa Recursos Genéticos e Biotecnologia, Empresa Brasileira de Pesquisa Agropecu�aria (Embrapa), Av. W5 Norte (final), Brasília, DF, 70770917, Brazil 5Departamento de Ciências Florestais, Escola Superior de Agricultura Luiz de Queiroz (ESALQ), Universidade de São Paulo (USP), Av. P�adua Dias, 11, Piracicaba, SP, 13418-900, Brazil 6Coordenação de Botânica, Museu Paraense Emílio Goeldi, Av. Magalhães Barata, 376, Belém, PA, 66040-170, Brazil 7Center for the Analysis of Social-Ecological Landscapes (CASEL), Indiana University, Student Building 331, 701 E. Kirkwood Avenue, Bloomington, IN, 47405, USA 8Departamento de Biologia, Instituto de Ciências Biol�ogicas, Universidade Federal do Amazonas (UFAM), Av. General Rodrigo Octavio Jordão Ramos, 1200, Coroado I, Manaus, AM, 69067-005, Brazil 9Coordenação de Ciências da Terra e Ecologia, Museu Paraense Emílio Goeldi, Av. Magalhães Barata, 376, Belém, PA, 66040-170, Brazil 10Departamento de Ciências Florestais, Universidade Federal do Amazonas (UFAM), Av. General Rodrigo Oct�avio Jordão Ramos, 3000, Manaus, AM, 69080-900, Brazil ABSTRACT Naturally regenerating forests or secondary forests (SFs) are a promising strategy for restoring large expanses of tropical forests at low cost and with high environmental benefits. This expectation is supported by the high resilience of tropical forests after natural disturbances, yet this resilience can be severely reduced by human impacts. Assessing the character- istics of SFs and their ecological integrity (EI) is essential to evaluating their role for conservation, restoration, and pro- visioning of ecosystem services. In this study, we aim to propose a concept and indicators that allow the assessment and classification of the EI of SFs. To this end, we review the literature to assess how EI has been addressed in different eco- systems and which indicators of EI are most commonly used for tropical forests. Building upon this knowledge we pro- pose a modification of the concept of EI to embrace SFs and suggest indicators of EI that can be applied to different successional stages or stand ages. Additionally, we relate these indicators to ecosystem service provision in order to sup- port the practical application of the theory. EI is generally defined as the ability of ecosystems to support and maintain composition, structure and function similar to the reference conditions of an undisturbed ecosystem. This definition does not consider the temporal dynamics of recovering ecosystems, such as SFs. Therefore, we suggest incorporation of an optimal successional trajectory as a reference in addition to the old-growth forest reference. The optimal successional tra- jectory represents the maximum EI that can be attained at each successional stage in a given region and enables the eval- uation of EI at any given age class. We further suggest a list of indicators, the main ones being: compositional indicators (species diversity/richness and indicator species); structural indicators (basal area, heterogeneity of basal area and canopy cover); function indicators (tree growth and mortality); and landscape proxies (landscape heterogeneity, landscape * Author for correspondence (Tel.: (92) 3643-3377; E-mail: milenarosenfield@gmail.com). † Authors contributed equally to this work. Biological Reviews 98 (2023) 662–676 © 2022 Cambridge Philosophical Society. Biol. Rev. (2023), 98, pp. 662–676. 662 doi: 10.1111/brv.12924 https://orcid.org/0000-0002-1799-2822 https://orcid.org/0000-0002-8130-852X https://orcid.org/0000-0003-1391-4875 mailto:milenarosenfield@gmail.com http://crossmark.crossref.org/dialog/?doi=10.1111%2Fbrv.12924&domain=pdf&date_stamp=2022-12-01 connectivity). Finally, we discuss how this approach can assist in defining the value of SF patches to provide ecosystem services, restore forests and contribute to ecosystem conservation. Key words: natural regeneration, secondary succession, ecological restoration, tropical forests, indicators, monitoring, vegetation structure, resilience. CONTENTS I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 663 II. Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 664 III. Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 665 (1) Concepts and main components of EI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .665 (2) Indicators and patterns across forest succession . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .666 IV. Application of concepts and indicators to SFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 667 (1) Including successional forests in the concept of EI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .667 (2) Successional trends of indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .669 (3) Suggested indicators for assessing the EI of tropical SFs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .670 V. Ecological integrity, societal demands and implications for decision making . . . . . . . . . . . . . . . . . . . . . . . 670 VI. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 672 VII. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 672 VIII. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 672 IX. Supporting information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 676 I. INTRODUCTION The Anthropocene is an era of unprecedented human impacts on the environment, where large extents of natural ecosystems have been converted and transformed (Hansen et al., 2013). The level of ecosystem transformation has histor- ically been associated with their ecological integrity (EI; for definitions of key terms see Table 1) (Karr & Dudley, 1981; Karr, Larson & Chu, 2022), with undisturbed ecosystems having higher EI – and therefore higher conservation value – and recovering systems having lower EI. This view ignores the temporal dynamics of ecosystem recovery and the fact that a recovering ecosystem can be functioning perfectly well despite being (still) very different from the undisturbed ones. While the concept of EI has helped set priorities for the con- servation of ecosystems, it fails to protect recovering systems that might importantly contribute to ecosystem functioning and biodiversity conservation at different scales. Adapting the concept to allow assessment of the EI of systems during the recovery process is of utmost importance for land-use planning and efficient implementation of biodiversity conser- vation and ecosystem restoration in the Anthropocene. Tropical forest regrowth covers approximately 600million hectares (Pan et al., 2011) and plays a crucial role in biodiver- sity conservation and ecosystem service provision in human- modified landscapes (Chazdon, 2014; Matos et al., 2020). Under optimal conditions, successional or secondary forests (SFs) that regrow naturally after the abandonment of pasture and agricultural lands can attain many similar characteristics to mature forests within a few decades to centuries (Poorter et al., 2021). These SFs can harbour a high diversity of plants and animals (Chazdon et al., 2009), including many species useful to people (Toledo & Salick, 2006; Junqueira, Shepard Jr. & Clement, 2010), connect forest fragments (Arroyo- Rodríguez et al., 2017), sequester large amounts of carbon (Pan et al., 2007; Poorter et al., 2016) and conserve floristic distinctiveness of biomes (Jakovac et al., 2022). Under limiting conditions, however, succession can be arrested, and fail to restore ecosystem functions fully (Arroyo-Rodríguez et al., 2017). Differentiating these different ecological condi- tions along the stages of succession, i.e. before its full recov- ery, is essential for identifying the conservation and restoration value of SF patches and the need for manage- ment to accelerate recovery. Decades of studies on tropical forest succession have iden- tified how natural and anthropogenic drivers affect the capacity of forests to regenerate. Drivers operating at differ- ent spatial scales importantly affect the capacity of forests to regenerate and to return to levels similar to their original state, through their influence on species availability and per- formance (Pickett, Collins & Armesto, 1987). At the regional scale, climate and soil properties define productivity levels and functional characteristics that shape the divergent rates of recovery of different forest types (Poorter et al., 2016; Rozendaal et al., 2019). At the landscape scale, forest cover and configuration determine forest connectivity and conse- quently the availability of seeds and biotic dispersal agents (Robiglio & Sinclair, 2011; Arroyo-Rodríguez et al., 2017). At the local scale, previous and current land use determine local soil quality and in situ propagule availability such as seeds, stumps, or sprouts (Mesquita et al., 2001; Gehring, Denich & Vlek, 2005; Jakovac et al., 2021). SFs that regrow in fragmented landscapes and on sites with an intensive land-use history have a limited capacity to restore ecosystem functioning, reduced recovery rates of vegetation structure Biological Reviews 98 (2023) 662–676 © 2022 Cambridge Philosophical Society. Ecological integrity of tropical secondary forests 663 1469185x, 2023, 2, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/brv.12924 by M useu Paraense E m ílio G oeldi, W iley O nline L ibrary on [15/01/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense and diversity, and show altered species compositions (Styger et al., 2007; Jakovac et al., 2015; Mesquita et al., 2015; Pinho et al., 2018; César et al., 2021; Heinrich et al., 2021). Within a given forest type and region, therefore, the level of anthro- pogenic impact at the landscape and local levels ultimately determines the ecological condition of SFs and their capacity to recover ecosystem functioning fully. We lack, however, a theoretical basis that allows classifica- tion of the ecological condition of SF patches during the recovery process, i.e. at different ages after abandonment. Current concepts of ecological condition, such as EI (Karr & Dudley, 1981; Karr et al., 2022), use reference sys- tems that can be decades away from early recovering forests, making reasonable comparisons difficult. Forest restoration assessments use average values from naturally regenerating forests as references for monitoring restoration success, ignoring that natural succession can be arrested. Setting intermediate benchmarks over time (Balaguer et al., 2014) that reflect an optimal successional trajectory could facilitate the assessment of ecosystem recovery at different successional stages. Setting such benchmarks over the successional process requires recognizing the variation in successional pathways. Additionally, ecosystem attributes show different trajectories over time and different rates of recovery (Poorter et al., 2021), potentially requiring different indicators for each succes- sional level. The accumulated knowledge on the limiting con- ditions for succession and the drivers of multiple successional pathways can support the design of a concept and indicators that allow assessing and classifying the EI of different succes- sional stages or stand age. Herein we review the literature to understand how EI has been assessed in different ecosystems, and which EI indica- tors are commonly used for tropical forests. Specifically, we: (i) modify the concept of EI so that it embraces temporal dynamics and is applicable to SFs; (ii) identify a list of indica- tors that can be used to evaluate the EI of SFs; and (iii) asso- ciate indicators of EI to the provision of ecosystem services, in order to connect the theoretical concept with its societal rel- evance. This study synthesizes the literature to promote a theoretical basis for identifying and classifying the ecological condition of SFs, allowing for better land-use planning, and the implementation and monitoring of conservation and restoration initiatives. Eventually, this will help in achieving the ambitious climate-change mitigation goals (e.g. Bonn Challenge, Paris Agreement, and Trillion Trees programs; Brancalion & Holl, 2020) as well as efficiently implementing the targets for the UN Decade on Ecosystem Restoration. II. MATERIALS AND METHODS To identify the main concepts and indicators associated with EI, we conducted two separate literature searches in Web of Science. The first search aimed to identify and describe the concepts and main ecological components of EI, as applied Table 1. Terminology and definitions used in this study. Term Definition Component (of EI) Main group of elements that define the integrity of an ecosystem (composition, structure and function). Ecological integrity (EI) The capacity of a system to support and maintain a balanced, integrated, adaptive community of organisms with a species composition, diversity, and functional organization comparable to that of natural habitat of the region. Ecological resilience Ability of ecosystems to absorb changes of state variables and reorganize or adapt to multiple ecosystem equilibrium states. Ecosystem attributes Characteristics of an ecosystem that can be identified and potentially measured. Ecosystem functioning The outcome of a set of processes and ecological functions determined from biotic and abiotic interactions. Ecosystem health Specific types and rates of ecological processes and arrangement of structural elements that characterize diverse and productive ecosystems. Ecosystem services The benefits that people obtain from ecosystems (Millennium Ecosystem Assessment, 2005) or the contributions of ecosystem structure and function (in combination with other inputs) to human well-being (Burkhard & Maes, 2017). Indicator Ecosystem attribute or measure of environmentally relevant phenomena used to depict or evaluate ecosystem conditions and their changes or to set environmental goals (Heink & Kowarik, 2010; Prach et al., 2019). A good indicator is easy to measure, is sensitive to stresses and has a known response to natural and anthropogenic disturbances and changes over time (Dale & Beyeler, 2001). Mature reference state Historical natural characteristics from an undisturbed ecosystem or an ecosystem in an advanced stage of succession. Old-growth forest A forest ecosystem that has grown for a long period of time (usually over 100 years old) and that harbours historically known characteristics associated with biodiversity and ecosystem functioning. Resilience See Ecological resilience. Secondary forest (SF) Regenerating forests growing after disturbances such as logging or complete land clearance of the original forest, usually on abandoned pastures or agricultural fields. SFs can originate from fully natural regeneration, assisted regeneration or active planting. Succession (secondary succession) Process of recovery of natural ecosystems following natural or anthropogenic disturbance events. Biological Reviews 98 (2023) 662–676 © 2022 Cambridge Philosophical Society. 664 Milena F. Rosenfield and others 1469185x, 2023, 2, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/brv.12924 by M useu Paraense E m ílio G oeldi, W iley O nline L ibrary on [15/01/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense to any ecosystem type (e.g. aquatic or terrestrial systems). The aim of this search was to obtain as broad an input as pos- sible, to define a concept of EI that is relevant to (secondary) forests. The second search aimed to identify indicators that are specifically used to evaluate EI of forest systems. The search string used for search #1 was: TITLE = (‘ecological integrity’ OR ‘ecological quality’ OR ‘ecological health’ OR ‘ecological resilience’ OR ‘biotic integrity’ OR ‘biotic qual- ity’ OR ‘biotic health’ OR ‘biotic resilience’ OR ‘ecosystem integrity’ OR ‘ecosystem quality’OR ‘ecosystem health’ OR ‘ecosystem resilience’ OR ‘forest integrity’ OR ‘forest quality’ OR ‘forest health’ OR ‘forest resilience’). We thus searched using the most common con- cepts used to describe and assess some sort of ecological qual- ity of ecosystems in order to identify a concept that was most likely to embrace SFs. We included combinations of the words health, quality and resilience as they may be used in the lit- erature with a similar meaning to integrity. We restricted our search to review articles from environmental disciplines (Environmental science, Biodiversity conservation, Ecology, Forestry, Remote sensing, Geosciences multidisciplinary, Environmental studies, Physical Geography, Multidisciplin- ary sciences, Plant sciences, and Biology). This first search returned a total of 112 articles. We first screened the title and abstract from these articles, identifying if the main objec- tive of the study revolved around concepts and/or indicators of EI in native ecosystems. This screening reduced the num- ber of relevant articles to 50, which were then assessed by reading the main text to identify the concepts of EI used, and whether they represented a new concept or a citation of an older reference. We excluded nine articles that did not explicitly indicate the concept they were using, resulting in a total of 41 articles (Table S1). Additionally, we assessed the original studies cited within the compiled references if they were not located by our search and included these arti- cles (N = 6; Table S1) and concepts in our final results. This search enabled us to identify not only the concepts associated with EI, but also the main components used to describe it. For search #2, we focused on the indicators used to evalu- ate EI in forest ecosystems. This search was not constrained by methodology (search #1 was restricted to review articles), so it included original research studies using field data, modelling or remote-sensing approaches, as well as review articles. We used the same search string as above, with the additional terms: AND TOPIC = (forest*) AND (indicator* OR metric*). This search was restricted to the same environmental disciplines listed above and returned a total of 140 articles. We performed an initial screening to remove studies focused on aquatic systems (e.g. mangrove, stream and wetland stud- ies), non-forest native ecosystems (e.g. coastal vegetation, grassland or savanna, and urban forest), species or organism characteristics (i.e. not addressing EI directly, and studies that focused only on the effect of air pollutants and pathogens on tree health) and studies that did not clearly present a list of indicators. We also removed two studies that were not avail- able for download. This resulted in a final total of 72 articles (Table S2). From each article, we extracted information on the study sites (region, history of disturbance), the indicators used to evaluate EI (indicator name and metric, and associated component of EI) and finally, the ecosystem ser- vices associated with each indicator (Table S3) as indicators of EI could be used to assess ecosystem services. For the most frequent indicators that resulted from our screening, we assessed, based on expert knowledge, the quality of its association with ecosystem services: categorized as good, fair or no direct association. We assessed the suitability of these indicators to evaluate the EI of SFs based on the following main evaluation criteria: (i) its behaviour is known in different forest types and across successional trajectories; (ii) it is easy to measure, monitor and understand; and (iii) it can be used at different spatial scales (patch or landscape level). For our analysis we further subdivided these into five evaluation criteria, we: (i) used the most frequent indicators that resulted from our screening, which are those more commonly used and known to assess patterns and processes in ecological studies, and determined in which successional stage they most efficiently indicated variations in EI (early, intermediate or late); (ii) classified indicators according to sampling complexity (easy – rapid assessment; medium – requires some specific knowledge; or hard – complex indicator requiring specialized equipment); (iii) classified indicators according to sampling frequency required (single or multiple); (iv) classified indicators by main methodological approach required (field based, remote sensing, other); and (v) classified indicators according to the spatial scale assessed [local plot (patch) or landscape level]. See Table S3 and Appendix S1 for the full list of information extracted from articles. Based on this classification scheme and on our expertise, we thus identified and classified the indicators both in terms of usefulness to assess overall EI of SFs and on cost-effectiveness and applicability for ecological assessments and monitoring. We focus on individual metrics instead of compound indices comprising multiple indicators because individual indicators (i) are easier to interpret and to implement by a wide range of technicians, (ii) allow for the identification of management practices required to improve the EI of successional forests (whereas a compound index would require de-composing to interpret which metric is most influential), and because (iii) the suitability of indica- tors might change with successional age. III. LITERATURE REVIEW (1) Concepts and main components of EI Among the 47 articles assessed for search #1, three main con- cepts were presented: ecological or biotic integrity, ecological resilience, and ecosystem or forest health. EI was first defined by Karr & Dudley (1981, p. 56), as ‘[…] the capacity of support- ing and maintaining a balanced, integrated, adaptive community of organisms having a species composition, diversity, and functional organi- zation comparable to that of natural habitat of the region. […] A system possessing integrity can withstand, and recover from most perturbations Biological Reviews 98 (2023) 662–676 © 2022 Cambridge Philosophical Society. Ecological integrity of tropical secondary forests 665 1469185x, 2023, 2, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/brv.12924 by M useu Paraense E m ílio G oeldi, W iley O nline L ibrary on [15/01/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense imposed by natural environmental processes, as well as many major dis- ruptions induced by man’. This concept was initially proposed to monitor aquatic systems, mainly with regard to water quality goals for human use (Karr & Dudley, 1981). It was later adapted by Parrish, Braun & Unnasch (2003, p. 852), as ‘the ability of an ecological system to support and maintain a community of organisms that has species composition, diversity, and functional orga- nization comparable to those of natural habitats within a region. An eco- logical system or species has integrity or is viable when its dominant ecological characteristics (e.g., elements of composition, structure, func- tion, and ecological processes) occur within their natural ranges of varia- tion and can withstand and recover from most perturbations imposed by natural environmental dynamics or human disruptions’. Andreasen et al. (2001), who developed a terrestrial index of EI, and Tierney et al. (2009), who addressed the monitoring and eval- uation of EI of forest ecosystems, are often incorrectly cited as the source of the concept, with the concepts they use being the ones proposed by Karr & Dudley (1981) and Parrish et al. (2003) as described above. For a detailed history of the concept of EI, its terms and usage, seeWurtzebach & Schultz (2016) and Roche & Campagne (2017). Ecological resilience is implicitly included in the concepts discussed above, but it has often been used separately in the ecological literature. Holling (1973, p. 17) first defined eco- logical resilience as ‘resilience determines the persistence of relation- ships within a system and is a measure of the ability of these systems to absorb changes of state variables, driving variables, and parameters, and still persist’. Holling (1973) characterized stability as per- sistence of a system near or close to an equilibrium state and resilience as the amount of disturbance that a system can absorb without changing its state. More recently, Gunderson (2000) presented a review of ecological resilience theory and application, and proposed that no single mecha- nism can guarantee the maintenance of resilience. When a system experiences shifts into an undesirable state, a diversity of ecological processes allows the system to reorganize itself and return to a desirable state or to reorganize and adapt to the alternative condition. Ecosystem health and forest health concepts correspond to different notions of the status of ecosystems. The use of the word ‘health’ comes from a human-centred utilitarian/ instrumental perspective and was initially associated with organism-level measurements, e.g. tree health (Kolb, Wagner & Covington, 1994; Ferretti, 1997). The term ‘health’ can be used as a bridge between scientists and non- scientists regarding the values of ecosystems (Kolb et al., 1994). Rapport (1989) and Costanza (1992) initially suggested approaches to characterize andmeasure the health of nature. In an ecosystem perspective, Kolb et al. (1994, p. 12) found great difficulty in applying the concept of health to such complex systems as forests, and suggested that a def- inition should include ‘specific types and rates of ecological processes, and numbers and arrangement of structural elements that characterize diverse, productive, forest ecosystems in major biogeographic regions’. Kolb et al. (1994, p. 12) also listed four essential elements in their definition of forest ecosystem health: ‘(1) physical and biotic resources to support forest cover; (2) resistance to catastrophic change and/or ability to recover after catastrophe; (3) functional equilibrium between supply and demand of essential resources; and (4) diversity of seral stages and stand structures’. Regardless of the wording, all concepts presented above have similarities regarding the most important components that characterize EI, which include native species, structural and physical characteristics, ecosystem functioning and abil- ity to recover after disturbance. Following the main compo- nents of EI proposed by Karr & Dudley (1981) and Parrish et al. (2003), which divided EI into composition, structure, and function, and the concepts of health discussed above, we further subdivide these three components into separate categories applicable to forest ecosystems (Table 2): (i) Com- position has a single category – Biological diversity; (ii) Struc- ture is subdivided into two categories –Vegetation structure, and Landscape structure and composition; and (iii) Function has three categories – Physical or environmental condition, Ecosystem functioning, and Resilience (see Table 1 for defi- nitions). This enabled us to accommodate these concepts more explicitly and to classify indicators accordingly, encom- passing different aspects of the integrity of natural ecosys- tems. These different categories encompass different spatial scales, from landscape-level attributes setting the landscape matrix context (landscape structure and composition) to patch conditions (biological diversity and vegetation struc- ture), which together determine ecosystem functioning and resilience. (2) Indicators and patterns across forest succession Indicators are measurable characteristics of the ecosystem that are related to the ecosystem condition or state (Table 1). Indicators provide information on the current con- dition of an ecosystem and enable the evaluation of ecosys- tem development over time (Wurtzebach & Schultz, 2016). They can be used to assess levels of degradation of ecosystems and to identify the need for management interventions in order to achieve a specified goal. In the case of EI, indicators are related to composition, structure and function compo- nents. Good indicators should be concise and reflect changes in community and/or ecosystem attributes (Table 1), being able to be used by researchers and practitioners to monitor ecosystem recovery. Based on our literature search, we found 58 indicators (Table 2; Appendix S2) and 33 indices (i.e. combinations of multiple indicators) used to evaluate forest EI (Table S4). For each group of indicators, we listed a number of indicator metrics (see Table S5 for full list) that were used in each study, either using field-based or remote-sensing approaches. Indi- ces based on combinations of multiple metrics were used to evaluate broad ecosystem integrity (e.g. Ecological Resilience Index, Ecosystem Health Index, Index of Biotic Integrity; Table S4). From the full list of indicators, the 10 most fre- quently assessed indicators (Table 3) were related to compo- sition (indicator species or group; species diversity or richness); structure (canopy cover and structure; herbaceous or shrub cover or abundance; landscape composition and Biological Reviews 98 (2023) 662–676 © 2022 Cambridge Philosophical Society. 666 Milena F. Rosenfield and others 1469185x, 2023, 2, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/brv.12924 by M useu Paraense E m ílio G oeldi, W iley O nline L ibrary on [15/01/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense heterogeneity; landscape connectivity and fragmentation; tree size and biomass); and function (tree growth and mortal- ity; vegetation condition and damage; soil physical and/or chemical parameters). Additionally, we indicate the quality of these indicators in terms of assessment of ecosystem ser- vices potentially provided by forests (Table 3). IV. APPLICATION OF CONCEPTS AND INDICATORS TO SFS (1) Including successional forests in the concept of EI The EI concepts (described above) use a desirable state as a reference (Andreasen et al., 2001; Wurtzebach & Schultz, 2016), which is usually a non-disturbed state, such as an old-growth forest located within the same region. This desirable state is often described through the natural range of values of multiple indicators. This assumes that the reference ecosystem holds the highest EI and the more similar a system is to its undisturbed original condition, the higher is its EI. This definition fails to incorporate changes in state condi- tion during the process of forest regeneration, and conse- quently assumes that EI is directly dependent on the time since recovery started, i.e. the age of the SF. To acknowledge the successional dynamics, intermediate reference states should be considered, in addition to the undisturbed state, to represent the optimal successional trajectory. Our approach is very similar to that used in monitoring children’s health: although there is a need to know the physical and mental conditions they must achieve by adulthood, it is more important to assess health across a trajectory of development and growth. We modified the definition from Parrish et al. (2003) to allow it to be applied to and operationalized for SFs. Thus, we describe EI as ‘the ability of an ecological system to sup- port and maintain a community of organisms that has species composition, diversity, and functional organization compa- rable to those of natural habitats within a region and at a given age class. An ecological system has integrity when its dominant ecological characteristics/indicators (e.g. elements of compo- sition, structure, and function) occur within an optimal natu- ral range for that specific age class (i.e. time since succession started) and can withstand and recover from most perturbations imposed by natural environmental dynamics or human dis- ruptions’. This means that SFs following successional trajec- tories under optimal conditions can serve as a reference for the maximum EI possibly attained at each successional stage (or age class) in a given region and forest type. Optimal con- ditions imply minimum limitations for successional processes, i.e. high species availability and favourable conditions for species performance. Such optimal conditions for forest suc- cession can be found in forest gaps and in transformed land- scapes with a land-use history of low intensity, duration and frequency and with a high forest cover (Fig. 1). To make this concept operational, we propose that reference values of indicators are retrieved from two refer- ence systems: an undisturbed or mature reference state (as historically used for EI) and an optimal successional Table 2. Main components of ecological integrity (EI) (see Section III.1) and associated indicators (see Section III.2). Main and (sub)components of EI Associated indicators Composition Biological diversity Indicator species or group, phylogenetic traits, sapling and/or seedling composition, sapling and/or seedling diversity or richness, species composition, species composition dynamics, species distribution dynamics, species diversity or richness, species functional diversity, species functional traits or groups Structure Vegetation structure Canopy cover and structure, community structure (plant), habitat condition, herbaceous or shrub cover or abundance, sapling and/or seedling abundance, sapling and/or seedling condition and size, snag and coarse woody debris, stand age, surface reflectance indices, surface texture measures, tree abundance, tree crown attributes, tree size and biomass, vegetation cover Landscape structure and composition Anthropogenic pressure, habitat specialization, land-cover and land-use dynamics, landscape composition and heterogeneity, landscape connectivity and fragmentation, landscape diversity, patch abundance, patch size, topography Function Physical or environmental condition Chemical parameters of deposition, soil erosion, soil physical and/or chemical parameters, water regimes Ecosystem functioning Community health (animal), community structure dynamics, ecosystem service provision or monetary value, flower, fruit and seed attributes, growth andmortality, insect outbreak frequency or intensity, litter structure, productivity and carbon sequestration, recruitment, species functional traits or groups change, tree growth and mortality, trophic interactions, vegetation condition and damage, water-related processes (transpiration, water-use efficiency, etc.) Resilience Disturbance frequency or intensity, disturbance model, forest recovery rate, network resistance, productivity and carbon sequestration dynamics, resilience coefficient or score, trajectories of vegetation condition and damage Biological Reviews 98 (2023) 662–676 © 2022 Cambridge Philosophical Society. Ecological integrity of tropical secondary forests 667 1469185x, 2023, 2, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/brv.12924 by M useu Paraense E m ílio G oeldi, W iley O nline L ibrary on [15/01/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense trajectory. For the mature reference, a range of values must be identified that embrace the spatio-temporal variation in the absolute values of indicators found in old-growth forests or, in their absence, in forests in advanced stages of succes- sion located in the same biogeographic region. For the opti- mal successional trajectory, values of indicators should be retrieved for different age classes or successional stages along trajectories under minimum limitations to succession. While limitations to succession reduce the EI of SFs, restoration and management practices have the potential to foster succession and improve the EI of SF patches (Fig. 1). The optimal suc- cessional trajectory can be expressed as absolute values (e.g. biomass values of 100Mg ha−1 at 20 years) or as propor- tions relative to the old-growth forests in the region (e.g. early successional forests will show 10–20% of the value found in mature forests located in the region). Deviations from the natural range of values found in the optimal successional tra- jectory should be interpreted as a reduction or increase in EI (Fig. 1). Building upon previously published EI concepts, we modify the concept of EI to add the temporal dynamics of SFs, allow- ing the assessment of EI across different successional stages. Previous studies have used mature forests and average values of naturally regenerating forests as references for monitoring forest restoration (e.g. Londe et al., 2020), overlooking the large variation in successional pathways as a result of limitations to succession. Here we explicitly recognize the large variation in indicator values across successional trajectories and propose that an optimal successional trajectory should be used as a reference. Operationalizing this reference of an optimal successional trajectory, however, can be challenging. Such reference sys- tems may not exist in degraded landscapes or in entire regions that have an ancient history of intense anthropogenic transformation. To circumvent this limitation, modelling approaches using data from multiple landscapes could build scenarios of low anthropogenic impact to model optimal suc- cessional trajectories. Moreover, future studies should Table 3. The 10 most frequently assessed indicators of forest ecological integrity used in the literature and their associations with eight different ecosystem services. The number of papers that assessed each indicator is shown in parentheses. Bullet points indicate whether the indicator is categorized as good (●) or fair (�) in assessing ecosystem services; cells without bullet points indicate no direct association. Indicator name Climate regulation Soil conservation Pollination or pest control Air quality or water regulation Biodiversity conservation Habitat integrity Provision: wild fruits and seeds Cultural services Composition Species diversity or richness (24) ● Indicator species or group (21) � ● � � � Structure Tree size and biomass (22) ● � ● � � Landscape connectivity and fragmentation (16) � � � Canopy cover and structure (13) ● � � Herbaceous or shrub cover or abundance (13) � � � � Landscape composition and heterogeneity (11) � � � ● Function Tree growth and mortality (14) ● Vegetation condition and damage (13) � Soil physical and/or chemical parameters (11) ● Biological Reviews 98 (2023) 662–676 © 2022 Cambridge Philosophical Society. 668 Milena F. Rosenfield and others 1469185x, 2023, 2, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/brv.12924 by M useu Paraense E m ílio G oeldi, W iley O nline L ibrary on [15/01/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense investigate whether optimum successional trajectories can be characterized by the proportional values of indicators in rela- tion to the old-growth forest and whether these can be applied across regions. Additionally, studies should provide decision-makers with maps or tables of regionalized refer- ence values for both old-growth forests and optimum succes- sional trajectories using either data-based or model-based approaches. (2) Successional trends of indicators The successional behaviour of most of these frequently used indicators of EI (Table 3) is well known, especially for vegeta- tion attributes (Finegan, 1996). A recent meta-analysis across a range of climatic conditions (ca. 1200–2500 mm of annual rainfall) showed that recovery to 90% of old-growth values is fastest for soils (<1 decade) and plant functions (<2.5 decades), intermediate for vegetation structure and species diversity (2.5–6 decades), and slowest for biomass and species composition (>12 decades) (Poorter et al., 2021). Despite the large variation in successional pathways and rates of regrowth across landscapes (Norden et al., 2015), predictable general patterns have been described and are summarized below for themost frequently used indicators of EI (Table 3). Tropical forest successional stages, following optimal trajectories (Fig. 1, dark green band), are summarized following Finegan (1996) as: early stage characterized by the colonization of herbs, shrubs and pioneer tree species (0–10 years of regrowth), intermediate stages when pioneer trees dominate the canopy (10–30 years) and late stages when pioneers are replaced by late-successional species (>30 years). Within the composition component, as succession proceeds there is an increase in species diversity and richness and a replacement of indicator species and functional groups. Species richness recovers to 90% of old-growth forest values within 31 years on average while species composition can take more than 120 years or may never happen (Rozendaal et al., 2019). Increment in species richness is very steep at early to intermedi- ate successional stages and slows down at late stages (Rozendaal et al., 2019). Overall successional changes in species composi- tion are less predictable and can be extremely slow (Rozendaal et al., 2019; Poorter et al., 2021). Therefore, we rec- ommend that changes in the presence and abundance of indi- cator species or specific groups of species are used in assessments of successional changes. The replacement of pio- neer species by late-successional species happens at intermedi- ate successional stages for adult trees (Finegan, 1996), as seedlings of late-successional species may be present from the early stages (Guariguata&Ostertag, 2001; Peña-Claros, 2003). SFs with lower EI will show slower species turnover and species richness, and higher dominance by certain indicator species and plant groups (Gehring et al., 2005; Styger et al., 2007; Jakovac et al., 2016). These can be observed at all successional stages by showing lower species richness, higher abundances of non-tree life forms such as ferns, grasses, bamboos, vines, and lianas, and higher dominance by certain indicator tree species, compared to the reference trajectory (Gehring et al., 2005; Styger et al., 2007; Letcher &Chazdon, 2009; Jako- vac et al., 2016). Within the structure component, during succession basal area and biomass accumulate and consequently canopy cover increases and herbaceous and shrub cover or abun- dance are reduced (Guariguata & Ostertag, 2001; Estrada-Villegas et al., 2020). Total basal area tends to sta- bilize at intermediate successional stages while biomass and horizontal heterogeneity stabilizes at intermediate to Fig. 1. (A) Diagram illustrating the concept of ecological integrity (EI) applied to secondary forests (SFs). It shows the change in EI over time and across different successional trajectories (green bands) arising from clear-cut forests and highlighting the optimal successional trajectory (dark green band). Green bands with lighter colours represent trajectories with decreasing EI. We can assess early, intermediate or late successional stages based on increasing levels of EI across time (A). In the upper portion of the graph (grey band), we show the natural range of variation of EI that exists in mature reference forests (e.g. old-growth forests) that represent the potential maximum EI that a site can achieve. In (B), we show different successional trajectories that may arise due to limitations to succession or disturbances: (1) optimal successional trajectory; (2) temporal variations decreasing the recovery rate; (3) rapid initial development, but stabilization in a suboptimal condition; and (4) positive initial recovery, but future degradation resulting from a strong disturbance (e.g. pest outbreak or extreme drought). Biological Reviews 98 (2023) 662–676 © 2022 Cambridge Philosophical Society. Ecological integrity of tropical secondary forests 669 1469185x, 2023, 2, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/brv.12924 by M useu Paraense E m ílio G oeldi, W iley O nline L ibrary on [15/01/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense late successional stages (Poorter et al., 2021). As a conse- quence of plant colonization and the increase in basal area, canopy cover sharply increases and then stabilizes within early successional stages. SFs with lower EI will have lower average tree size and a more homogeneous tree-size distribution, showing lower basal area, biomass and canopy height and lower heterogeneity in tree size [e.g. Gini index of basal area or diameter at breast height (dbh)], compared to optimum reference trajectories (Marin-Spiotta, Ostertag & Silver, 2007; Chazdon et al., 2010; Jakovac et al., 2015; Mesquita et al., 2015; Poorter et al., 2016; Rozendaal et al., 2019; Pérez-C�ardenas et al., 2021). Landscape attributes, another commonly used indicator of structure, are not directly associated with successional changes, but can be used as proxies for the con- ditions enabling succession in the landscape (Arroyo-Rodrí- guez et al., 2017; Jakovac et al., 2021), and therefore can be used as indirect indicators of EI across successional stages. Within the function component, during succession rates of tree growth decrease (Norden et al., 2015) and mortality shows a hump-shaped relationship. Tree growth rates tend to be higher at early to intermediate stages of succession (Marin-Spiotta et al., 2007) due to the rapid growth rates of pioneer and early secondary species (Chazdon et al., 2010). Mortality rates tend to be higher at early to intermediate stages due to thinning and the mortality of same-aged pio- neer species but are reduced at late stages (van Breugel, Martínez-Ramos & Bongers, 2006). Successional changes in soil conditions are less predictable, probably due to a dependence on geomorphological characteristics and the conditions left after land use (which can increase or decrease soil fertility, for example) (Powers & Marín-Spiotta, 2017). However, recent studies suggest that soil nitrogen, carbon stock and bulk density increase sharply and then stabilize within the early stages of succession (Poorter et al., 2021; van der Sande et al., 2023). SFs with lower EI will show slower dynamics and therefore lower rates of tree growth and mor- tality, and slower restoration of soil conditions, especially at early to intermediate stages of succession. (3) Suggested indicators for assessing the EI of tropical SFs Based on the list of indicators used most frequently in the liter- ature (Table 3) and our evaluation criteria [known behaviour across successional trajectories, ease of measurement (sam- pling complexity and sampling frequency), methodological approach and spatial scale] we present in Table 4 a list of indi- cators for use in assessments of the EI of SFs. With this list, researchers and practitioners can identify the combination of indicators that best match their objectives and resources. To evaluate the EI of SFs, at least one indicator for each component of EI (composition, structure and function) should be used. Across a range of characteristics, such indicators can be easy tomeasure, for example requiring singlemeasurements from field surveys and allowing assessments at local scale, or they can be harder to obtain, requiringmultiple measurements and remote-sensing techniques, but allowing assessments at regional scales (Table 4). Remote-sensing approaches require technologies that may not be available for local institutions, but are essential for upscaling the classification and monitoring of SFs, usually of interest for national governments and research institutions. Field-based evaluations tend to require less-specific technology and therefore are more accessible to a wide range of professionals and institutions. Some field-based indicators, such as species diversity, however, require expertise in species identification, potentially restricting their application, particularly in biodiversity hotspots. The list of indicators and evaluation criteria presented in Table 4 thus will enable the selection of indicators that are most suitable for different aims, spatial and temporal scales and resource availability. Based on this literature review and on expert knowledge, we suggest a combination of the following indicators of composi- tion and structure for assessing the EI of SFs at a local scale: indicator species or species richness, canopy cover, basal area and heterogeneity of basal area (or dbh). These indicators are easy tomeasure in the field and their recovery is associated with the recovery of function components, such as soil properties and plant functional traits (Poorter et al., 2021). When using remote-sensing techniques, structure indicators can be assessed throughmetrics such as Leaf Area Index (LAI) andNormalized Difference Vegetation Index (NDVI), especially during early stages of succession (Table 4). For assessments of the EI of SFs at a regional scale, we suggest the inclusion of landscape structure indicators (e.g. landscape heterogeneity, landscape connectivity), which are known proxies for the capacity for recovery of composition, structure and function of SFs (Arroyo-Rodríguez et al., 2017; Jakovac et al., 2021). It is important to highlight that reference values are required for two reference systems: a mature reference state and an optimal successional trajectory. The reference values of the optimal successional trajectory can be absolute values (and therefore region specific) or determined relative to the old-growth forest (and therefore comparable across regions). Future studies should try to identify relative values of indica- tors that can be used as an optimal successional trajectory in multiple regions. In addition, we suggest identifying indicator species and how patterns of species dominance change with EI and successional stages, as well as evaluating other indica- tors of function that are easier to assess in the field. Notably, some indicators will be more appropriate for generalization across regions (e.g. structure indicators) while others will be region specific (e.g. indicator species), therefore a combina- tion of both may allow better characterization of EI given the heterogeneity of tropical plant communities. V. ECOLOGICAL INTEGRITY, SOCIETAL DEMANDS AND IMPLICATIONS FOR DECISION MAKING Ecological integrity is a concept applied to individual SF patches through the characterization of their composition, Biological Reviews 98 (2023) 662–676 © 2022 Cambridge Philosophical Society. 670 Milena F. Rosenfield and others 1469185x, 2023, 2, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/brv.12924 by M useu Paraense E m ílio G oeldi, W iley O nline L ibrary on [15/01/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense structure, and function, providing information on the conser- vation and restoration potential of individual SF patches. From a societal perspective, however, the relevance of a SF patch will depend on its socio-ecological context. Therefore, the decision-making process on the socio-ecological value (and fate) of SF patches depends on an evaluation that com- bines the actual EI of a SF patch, the potential of a SF patch to undergo succession (and increase its EI), its potential to contribute to the resilience of the landscape as a whole, and the societal demand for land use (e.g. agricultural produc- tion, demand for ecosystem services). From a socio-ecological perspective (Vieira et al., 2014; Balvanera et al., 2021), the value of SF patches therefore will greatly depend on the conditions of the landscape in which they are found. For example, in a highly deforested and frag- mented landscape, SF patches with high EI will have high value for conservation and restoration, also contributing to landscape connectivity. These SFs with high EI will harbour high levels of biodiversity, contribute to ecosystem function- ing and provide several ecosystem services including goods for people (such as food and materials). In the same highly fragmented landscape, SF patches with low EI may not have a high value for conservation at the patch level, but might play an important role for landscape connectivity and soil protection given the impoverished conditions of the land- scape. In this case, actively managing these low-EI patches (e.g. climber cutting, invasive species control, enrichment planting, exclusion of cattle grazing) could foster succession and consequently increase their EI and the provision of eco- system goods and services, also contributing to increased resilience of the landscape. Enhancing natural regeneration through management practices soon after agricultural aban- donment is an easier and cheaper way of increasing its EI compared to managing older SF patches (Rezende & Vieira, 2019; Vieira et al., 2021). Such low-EI SF patches could be managed for enhancing production through agro- forestry, e.g. soil fertility or timber and non-timber produc- tion, and thereby contribute to the societal needs and goals for that specific landscape (Michon et al., 2007; Chazdon, 2008; Diemont et al., 2011; Heinrich et al., 2021). Therefore, the classification of the EI of a SF patch is only one step in the decision tree for land-use planning and land- scape restoration. It is important to consider the social, legal, economic and political factors that influence the governance of SFs (Vieira et al., 2014). To facilitate such discussions, we highlight four properties that should be assessed in SF Table 4. Suggested indicators for use in assessments of the ecological integrity of tropical secondary forests and evaluation criteria used to classify each indicator: successional stage ( early, intermediate, late); sampling complexity ( easy, medium, hard); sampling frequency required ( single, multiple); methodological approach ( field based, remote sensing); and spatial scale ( local, regional). Indicator name Indicator metrics suggested Stage Complexity Frequency Approach Scale Composition Species diversity or richness Species diversity or richness Indicator species or group Indicator species presence/abundance (e.g. successional groups, exotic species) Structure Tree size and biomass Tree basal area (total) Tree basal area variation (e.g. standard deviation or Gini index) Leaf Area Index (LAI) Normalized Difference Vegetation Index (NDVI) Canopy cover and structure Canopy cover (% ground cover) Herbaceous or shrub cover or abundance Ground vegetation cover (e.g. grass or light- demanding herbs) Landscape composition and heterogeneity Landscape composition (land-use and land- cover types) Landscape connectivity and fragmentation Landscape connectivity or fragmentation Function Tree growth and mortality changes in tree abundance or basal area Tree growth and mortality Vegetation condition and damage Tree condition or damage (e.g. defoliation, damage to branches) Soil physical and/or chemical parameters Soil organic matter, soil nitrogen content, bulk density Biological Reviews 98 (2023) 662–676 © 2022 Cambridge Philosophical Society. Ecological integrity of tropical secondary forests 671 1469185x, 2023, 2, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/brv.12924 by M useu Paraense E m ílio G oeldi, W iley O nline L ibrary on [15/01/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense patches to support decision-making on land-use planning: (i) the current EI of the SF – the higher its EI, the higher will be its conservation value; (ii) current provision of ecosystem ser- vices and societal needs – a better match between ecosystem services provided and societal needs, the higher will be the value of preserving and/or managing that SF patch; (iii) the role of the SF patch in the functioning and resilience of the landscape; and (iv) the trade-offs and synergies with local and regional societal demands. The combination of these four aspects should be considered in decision making for land-use planning in order to determine whether to conserve, manage or convert SF patches. VI. CONCLUSIONS (1) This study reviews the concept of EI and proposes adap- tation of the concept to incorporate the temporal dynamics of recovering ecosystems such as SFs. We propose that the assessment of the EI of SFs uses two reference systems: a mature reference forest and an optimal successional trajec- tory. The mature forest is used to retrieve relative reference values, and the optimum successional trajectory serves as a reference for the maximum EI attainable at each age class or successional stage in a given region. The use of an optimal successional trajectory as a reference enables the evaluation of EI at any age class and successional stage. (2) We provide a list of indicators that can be used to assess the EI of SF patches at different successional stages and asso- ciate them with the provision of ecosystem services. By detail- ing a list of indicators for assessing EI, we provide strategies for field and remote-sensing assessments and monitoring of forest recovery and restoration. (3) Finally, we highlight that the socio-ecological value of SF patches extends beyond EI alone and might include the eco- system services provided in their individual landscape con- texts and for local societal demands and needs. SFs are seen variously as degraded forests by conservationists, as promis- ing areas for forest restoration by restoration practitioners and researchers, as fallows by shifting cultivation farmers, and as an opportunity for agricultural intensification by agri- business. Assessing the EI of SFs will help to identify their conservation value, and combined with a socio-ecological assessment will assist informed decisions on the fate of tropical SFs. VII. ACKNOWLEDGEMENTS This study was funded by CNPq through the Synthesis Cen- ter on Biodiversity and Ecosystem Services - SinBiose (grant number 442371/2019-5). M. F. R. received a scholarship from CNPq (process #152421/2020-3). D. L. M. V. has a research grant from the CNPq (#303884/2019-3). L. P. was supported by the European Research Council Advanced Grant PANTROP 834775. D. R. A. A. was supported by the São Paulo Research Foundation (#2018/21338-3). I. C. G. V. received a CNPq research productivity scholarship (#308778/2017-0). The São Paulo Research Foundation is acknowledged for financial support (#2018/18416-2). VIII. REFERENCES References identified with an asterisk (*) are cited in Table S1 of the supporting information, while those identified with a dagger symbol (†) are cited within Table S2. †Alexander, S. A. & Palmer, C. J. (1999). Forest health monitoring in the United States: first four years. Environmental Monitoring and Assessment 55, 267–277. †Alexandrino, E. R., Buechley, E. R., Karr, J. R., de Barros Ferraz, K. M. P. M., de Barros Ferraz, S. F., do Couto, H. T. Z. & Şekercio�glu, Ç. H. (2017). Bird based Index of Biotic Integrity: assessing the ecological condition of Atlantic Forest patches in human-modified landscape. Ecological Indicators 73, 662–675. Andreasen, J. K., O’Neill, R. V., Noss, R. & Slosser, N. C. (2001). Considerations for the development of a terrestrial index of ecological integrity. Ecological Indicators 1, 21–35. †Arianoutsou, M., Koukoulas, S. & Kazanis, D. (2011). Evaluating post-fire forest resilience using GIS and multi-criteria analysis: an example from Cape Sounion National Park, Greece. Environmental Management 47, 384–397. †Arnott, J. C., Osenga, E. C., Cundiff, J. L. & Katzenberger, J. W. (2015). Engaging stakeholders on forest health: a model for integrating climatic, ecological, and societal indicators at the watershed scale. Journal of Forestry 113, 447–453. Arroyo-Rodrı́guez, V., Melo, F. P. L., Martı́nez-Ramos, M., Bongers, F., Chazdon, R. L., Meave, J. A., Norden, N., Santos, B. A., Leal, I. R. & Tabarelli, M. (2017). Multiple successional pathways in human-modified tropical landscapes: new insights from forest succession, forest fragmentation and landscape ecology research. Biological Reviews 92, 326–340. †Atak, B. K.&Tonyalo�glu, E. E. (2020). Monitoring the spatiotemporal changes in regional ecosystem health: a case study in Izmir, Turkey. Environmental Monitoring and Assessment 192, 385. Balaguer, L., Escudero, A., Martı́n-Duque, J. F., Mola, I. & Aronson, J. (2014). The historical reference in restoration ecology: re-defining a cornerstone concept. Biological Conservation 176, 12–20. Balvanera, P., Paz, H., Arreola-Villa, F., Bhaskar, R., Bongers, F., Cortés, S., del Val, E., Garcı́a-Frapolli, E., Gavito, M. E., Gonz�alez- Esquivel, C. E., Martı́nez-Ramos, M., Martı́nez-Yrizar, A., Mora, F., Naime, J., Pascual-Ramı́rez, F., ET AL. (2021). Social ecological dynamics of tropical secondary forests. Forest Ecology and Management 496, 119369. *Beck,M.W.&Hatch, L. K. (2009). A review of research on the development of lake indices of biotic integrity. Environmental Reviews 17, 21–44. †Becker, D. A.,Wood, P. B., Strager, M. P.&Mazzarella, C. (2015). Impacts of mountaintop mining on terrestrial ecosystem integrity: identifying landscape thresholds for avian species in the central Appalachians, United States. Landscape Ecology 30, 339–356. Brancalion, P. H. S. & Holl, K. D. (2020). Guidance for successful tree planting initiatives. Journal of Applied Ecology 57, 2349–2361. *Brown, E. D. &Williams, B. K. (2016). Ecological integrity assessment as a metric of biodiversity: are we measuring what we say we are? Biodiversity and Conservation 25, 1011–1035. BURKHARD, B. & MAES, J. (eds) (2017). Mapping Ecosystem Services. Advanced Books. https://doi.org/10.3897/ab.e12837 †Busing, R. T., Liegel, L. H. & Labau, V. J. (1996). Overstory mortality as an indicator of forest health in California. Environmental Monitoring and Assessment 42, 285–295. †Cale, J. A., Klutsch, J. G., Erbilgin, N., Negr�on, J. F. & Castello, J. D. (2016). Using structural sustainability for forest health monitoring and triage: case study of a mountain pine beetle (Dendroctonus ponderosae)-impacted landscape. Ecological Indicators 70, 451–459. †Capmourteres, V.&Anand, M. (2016). Assessing ecological integrity: a multi-scale structural and functional approach using Structural Equation Modeling. Ecological Indicators 71, 258–269. †Cardoso, P., Borges, P. A. V. & Gaspar, C. (2007). Biotic integrity of the arthropod communities in the natural forests of Azores. Biodiversity and Conservation 16, 2883–2901. *†Carignan, V. & Villard, M.-A. (2002). Selecting indicator species to monitor ecological integrity: a review. Environmental Monitoring and Assessment 78, 45–61. Biological Reviews 98 (2023) 662–676 © 2022 Cambridge Philosophical Society. 672 Milena F. Rosenfield and others 1469185x, 2023, 2, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/brv.12924 by M useu Paraense E m ílio G oeldi, W iley O nline L ibrary on [15/01/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense https://doi.org/10.3897/ab.e12837 César, R. G.,Moreno, V. d. S.,Coletta, G. D., Schweizer, D.,Chazdon, R. L., Barlow, J., Ferraz, S. F. B.,Crouzeilles, R.& Brancalion, P. H. S. (2021). It is not just about time: agricultural practices and surrounding forest cover affect secondary forest recovery in agricultural landscapes. Biotropica 53, 496–508. *Chambers, J. C., Allen, C. R. & Cushman, S. A. (2019). Operationalizing ecological resilience concepts for managing species and ecosystems at risk. Frontiers in Ecology and Evolution 7, 241. Chazdon, R. L. (2008). Beyond deforestation: restoring forests and ecosystem services on degraded lands. Science 320, 1458–1460. Chazdon, R. L. (2014). Second Growth: The Promise of Tropical Forest Regeneration in an Age of Deforestation. The University of Chicago Press, Chicago. Chazdon, R. L., Finegan, B., Capers, R. S., Salgado-Negret, B., Casanoves, F., Boukili, V. & Norden, N. (2010). Composition and dynamics of functional groups of trees during tropical forest succession in northeastern Costa Rica. Biotropica 42, 31–40. Chazdon, R. L., Peres, C. A., Dent, D., Sheil, D., Lugo, A. E., Lamb, D., Stork, N. E. & Miller, S. E. (2009). The potential for species conservation in tropical secondary forests. Conservation Biology 23, 1406–1417. *Costanza, R. (1992). Toward an operational definition of ecosystem health. In Ecosystem Health: New Goals for Environmental Management (eds R. COSTANZA, B. G. NORTON and B. D. HASKELL). Island Press, Washington, DC. †Coulston, J. W. & Riitters, K. H. (2003). Geographic analysis of forest health indicators using spatial scan statistics. Environmental Management 31, 764–773. *Crawford, D. W., Bonnevie, N. L., Gillis, C. A. & Wenning, R. J. (1994). Historical changes in the ecological health of the Newark Bay estuary, New Jersey. Ecotoxicology and Environmental Safety 29, 276–303. *Cumming, G. S. & Peterson, G. D. (2017). Unifying research on social–ecological resilience and collapse. Trends in Ecology & Evolution 32, 695–713. Dale, V. H. & Beyeler, S. C. (2001). Challenges in the development and use of ecological indicators. Ecological Indicators 1, 3–10. Diemont, S. A. W., Bohn, J. L., Rayome, D. D., Kelsen, S. J. & Cheng, K. (2011). Comparisons of Mayan forest management, restoration, and conservation. Forest Ecology and Management 261, 1696–1705. *Drever, C. R., Peterson, G., Messier, C., Bergeron, Y. & Flannigan, M. (2006). Can forest management based on natural disturbances maintain ecological resilience? Canadian Journal of Forest Research 36, 2285–2299. †Drever, M. C., Aitken, K. E. H., Norris, A. R. & Martin, K. (2008). Woodpeckers as reliable indicators of bird richness, forest health and harvest. Biological Conservation 141, 624–634. †Egli, S. (2011). Mycorrhizal mushroom diversity and productivity - an indicator of forest health? Annals of Forest Science 68, 81–88. Estrada-Villegas, S., Bail�on, M.,Hall, J. S., Schnitzer, S. A., Turner, B. L., Caughlin, T. & Breugel, M. (2020). Edaphic factors and initial conditions influence successional trajectories of early regenerating tropical dry forests. Journal of Ecology 108, 160–174. *†Ferretti, M. (1997). Forest health assessment and monitoring – issues for consideration. Environmental Monitoring and Assessment 48, 45–72. Finegan, B. (1996). Pattern and process in neotropical secondary rain forests: the first 100 years of succession. Trends in Ecology & Evolution 11, 119–124. †Fraser, R. H., Olthof, I. & Pouliot, D. (2009). Monitoring land cover change and ecological integrity in Canada’s national parks. Remote Sensing of Environment 113, 1397–1409. †Frazier, A. E., Renschler, C. S. & Miles, S. B. (2013). Evaluating post-disaster ecosystem resilience using MODIS GPP data. International Journal of Applied Earth Observation and Geoinformation 21, 43–52. †Frego, K. A. (2007). Bryophytes as potential indicators of forest integrity. Forest Ecology and Management 242, 65–75. †Galvani, F. M., Graciano-Silva, T. & Cardoso-Leite, E. (2020). Is biotic integrity of urban forests remants related with their size and shape? Cerne 26, 9–17. †Gazol, A., Camarero, J. J., Vicente-Serrano, S. M., S�anchez-Salguero, R., Gutiérrez, E., de Luis, M., Sangüesa-Barreda, G., Novak, K., Rozas, V., Tı́scar, P. A., Linares, J. C., Martı́n-Hern�andez, N., Martı́nez del Castillo, E., Ribas, M., Garcı́a-Gonz�alez, I., ET AL. (2018). Forest resilience to drought varies across biomes. Global Change Biology 24, 2143–2158. Gehring, C., Denich, M. & Vlek, P. L. G. (2005). Resilience of secondary forest regrowth after slash-and-burn agriculture in central Amazonia. Journal of Tropical Ecology 21, 519–527. †Glennon, M. J. & Porter, W. F. (2005). Effects of land use management on biotic integrity: an investigation of bird communities. Biological Conservation 126, 499–511. *Gonz�alez-Quintero, C. & Avila-Foucat, V. S. (2019). Operationalization and measurement of social-ecological resilience: a systematic review. Sustainability 11, 6073. Guariguata, M. R. & Ostertag, R. (2001). Neotropical secondary forest succession: changes in structural and functional characteristics. Forest Ecology and Management 148, 185–206. *Gunderson, L. H. (2000). Ecological resilience - in theory and application. Annual Review of Ecology and Systematics 31, 425–439. †Han, R., Guo, L., Xu, N. & Wang, D. (2019). The effect of the Grain for Green Program on ecosystem health in the upper reaches of the Yangtze river basin: a case study of Eastern Sichuan, China. International Journal of Environmental Research and Public Health 16, 2112. Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O. & Townshend, J. R. G. (2013). High-resolution global maps of 21st-century forest cover change. Science 342, 850–853. *Heckmann, K. E., Manley, P. N. & Schlesinger, M. D. (2008). Ecological integrity of remnant montane forests along an urban gradient in the Sierra Nevada. Forest Ecology and Management 255, 2453–2466. Heink, U. & Kowarik, I. (2010). What are indicators? On the definition of indicators in ecology and environmental planning. Ecological Indicators 10, 584–593. Heinrich, V. H. A., Dalagnol, R., Cassol, H. L. G., Rosan, T. M., de Almeida, C. T., Silva Junior, C. H. L., Campanharo, W. A., House, J. I., Sitch, S., Hales, T. C., Adami, M., Anderson, L. O. & Aragao, L. E. O. C. (2021). Large carbon sink potential of secondary forests in the Brazilian Amazon to mitigate climate change. Nature Communications 12, 1785. †Hern�andez-Clemente, R., North, P. R. J., Hornero, A. & Zarco- Tejada, P. J. (2017). Assessing the effects of forest health on sun-induced chlorophyll fluorescence using the FluorFLIGHT 3-D radiative transfer model to account for forest structure. Remote Sensing of Environment 193, 165–179. *†Hilty, J. & Merenlender, A. (2000). Faunal indicator taxa selection for monitoring ecosystem health. Biological Conservation 92, 185–197. *Holling, C. S. (1973). Resilience and stability of ecological systems. Annual Review of Ecology and Systematics 4, 1–23. *†Ib�añez, I., Acharya, K., Juno, E., Karounos, C., Lee, B. R., McCollum, C., Schaffer-Morrison, S. & Tourville, J. (2019). Forest resilience under global environmental change: do we have the information we need? A systematic review. PLoS One 14, 1–17. Jakovac, C. C., Bongers, F., Kuyper, T. W., Mesquita, R. C. G. & Peña- Claros, M. (2016). Land use as a filter for species composition in Amazonian secondary forests. Journal of Vegetation Science 27, 1104–1116. Jakovac, C. C., Junqueira, A. B., Crouzeilles, R., Peña-Claros, M., Mesquita, R. C. G. & Bongers, F. (2021). The role of land-use history in driving successional pathways and its implications for the restoration of tropical forests. Biological Reviews 96, 1114–1134. Jakovac, C. C., Meave, J. A., Bongers, F., Letcher, S. G., Dupuy, J. M., Piotto, D., Rozendaal, D. M. A., Peña-Claros, M., Craven, D., Santos, B. A., Siminski, A., Fantini, A. C., Rodrigues, A. C., Hern�andez- Jaramillo, A., Id�arraga, A., ET AL. (2022). Strong floristic distinctiveness across Neotropical successional forests. Science Advances 8, eabn1767. Jakovac, C. C., Peña-Claros, M., Kuyper, T. W. & Bongers, F. (2015). Loss of secondary-forest resilience by land-use intensification in the Amazon. Journal of Ecology 103, 67–77. *Johnstone, J. F., Allen, C. D., Franklin, J. F., Frelich, L. E., Harvey, B. J., Higuera, P. E., Mack, M. C., Meentemeyer, R. K., Metz, M. R., Perry, G. L., Schoennagel, T. & Turner, M. G. (2016). Changing disturbance regimes, ecological memory, and forest resilience. Frontiers in Ecology and the Environment 14, 369–378. Junqueira, A. B., Shepard, G. H. Jr. & Clement, C. R. (2010). Secondary forests on anthropogenic soils in Brazilian Amazonia conserve agrobiodiversity. Biodiversity and Conservation 19, 1933–1961. *Kane, D. D., Gordon, S. I.,Munawar, M., Charlton, M. N. & Culver, D. A. (2009). The Planktonic Index of Biotic Integrity (P-IBI): an approach for assessing lake ecosystem health. Ecological Indicators 9, 1234–1247. *Karr, J. R. & Dudley, D. R. (1981). Ecological perspective on water quality goals. Environmental Management 5, 55–68. Karr, J. R., Larson, E. R. & Chu, E. W. (2022). Ecological integrity is both real and valuable. Conservation Science and Practice 4, e583. †Kay, C. (1997). The condition and trend of aspen, Populus tremuloides, in Kootenay and Yoho National Parks: implications for ecological integrity. Canadian Field-Naturalist 111, 607–616. †Klos, R. J.,Wang, G. G.,Bauerle,W. L.&Rieck, J. R. (2009). Drought impact on forest growth and mortality in the southeast USA: an analysis using Forest Health and Monitoring data. Ecological Applications 19, 699–708. *Kolb, B. T. E., Wagner, M. R. & Covington, W. W. (1994). Concepts of forest health. Journal of Forestry 92, 10–15. †Kopackov�a, V., Lhot�akov�a, Z., Oulehle, F. & Albrechtov�a, J. (2015). Assessing forest health via linking the geochemical properties of a soil profile with the biochemical parameters of vegetation. International journal of Environmental Science and Technology 12, 1987–2002. Biological Reviews 98 (2023) 662–676 © 2022 Cambridge Philosophical Society. Ecological integrity of tropical secondary forests 673 1469185x, 2023, 2, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/brv.12924 by M useu Paraense E m ílio G oeldi, W iley O nline L ibrary on [15/01/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense *Kuehne, L. M.,Olden, J. D., Strecker, A. L., Lawler, J. J.& Theobald, D. M. (2017). Past, present, and future of ecological integrity assessment for fresh waters. Frontiers in Ecology and the Environment 15, 197–205. †Ladin, Z. S., Higgins, C. D., Schmit, J. P., Sanders, G., Johnson, M. J., Weed, A. S., Marshall, M. R., Campbell, J. P., Comiskey, J. A. & Shriver, W. G. (2016). Using regional bird community dynamics to evaluate ecological integrity within national parks. Ecosphere 7, e01464. *†LaPaix, R., Freedman, B. & Patriquin, D. (2009). Ground vegetation as an indicator of ecological integrity. Environmental Reviews 17, 249–265. *Lausch, A., Borg, E., Bumberger, J., Dietrich, P., Heurich, M., Huth, A., Jung, A., Klenke, R., Knapp, S., Mollenhauer, H., Paasche, H., Paulheim, H., Pause, M., Schweitzer, C., Schmulius, C., ET AL. (2018). Understanding forest health with remote sensing, Part III: requirements for a scalable multi-source forest health monitoring network based on data science approaches. Remote Sensing 10, 1120. *†Lausch, A., Erasmi, S., King, D., Magdon, P. & Heurich, M. (2016). Understanding forest health with remote sensing - Part I—a review of spectral traits, processes and remote-sensing characteristics. Remote Sensing 8, 1029. *Lausch, A., Erasmi, S., King, D., Magdon, P. & Heurich, M. (2017). Understanding forest health with remote sensing-Part II—a review of approaches and data models. Remote Sensing 9, 129. Letcher, S. G. & Chazdon, R. L. (2009). Rapid recovey of biomass, species richness and species composition in a forest chronosequence in Northeastern Costa Rica. Biotropica 41(5), 608–617. †Liao, C., Yue, Y., Wang, K., Fensholt, R., Tong, X. & Brandt, M. (2018). Ecological restoration enhances ecosystem health in the karst regions of southwest China. Ecological Indicators 90, 416–425. †Liira, J., Sepp, T. & Parrest, O. (2007). The forest structure and ecosystem quality in conditions of anthropogenic disturbance along productivity gradient. Forest Ecology and Management 250, 34–46. †Liu, S., Zhao, Q.,Wen, M., Deng, L., Dong, S. &Wang, C. (2013). Assessing the impact of hydroelectric project construction on the ecological integrity of the Nuozhadu Nature Reserve, southwest China. Stochastic Environmental Research and Risk Assessment 27, 1709–1718. Londe, V., Turini Farah, F., Ribeiro Rodrigues, R. & Roberto Martins, F. (2020). Reference and comparison values for ecological indicators in assessing restoration areas in the Atlantic Forest. Ecological Indicators 110, 105928. †Longo, M., Knox, R. G., Levine, N. M., Alves, L. F., Bonal, D., Camargo, P. B., Fitzjarrald, D. R., Hayek, M. N., Restrepo-Coupe, N., Saleska, S. R., da Silva, R., Stark, S. C., Tapaj�os, R. P., Wiedemann, K. T., Zhang, K., ET AL. (2018). Ecosystem heterogeneity and diversity mitigate Amazon forest resilience to frequent extreme droughts. New Phytologist 219, 914–931. †Lucas, C. M., Sheikh, P., Gagnon, P. R. & McGrath, D. G. (2016). How livestock and flooding mediate the ecological integrity of working forests in Amazon River floodplains. Ecological Applications 26, 190–202. †Lyver, P. O. B., Timoti, P., Jones, C. J., Richardson, S. J., Tahi, B. L. & Greenhalgh, S. (2017). An indigenous community-based monitoring system for assessing forest health in New Zealand. Biodiversity and Conservation 26, 3183–3212. Marin-Spiotta, E., Ostertag, R. & Silver, W. L. (2007). Long-term patterns in tropical reforestation: plant community composition and aboveground biomass accumulation. Ecological Applications 17, 828–839. Matos, F. A. R., Magnago, L. F. S., Miranda, C. A. C., de Menezes, L. F. T., Gastauer, M., Safar, N. V. H., Schaefer, C. E. G. R., da Silva, M. P., Simonelli, M., Edwards, F. A., Martins, S. V., Meira-Neto, J. A. A. & Edwards, D. P. (2020). Secondary forest fragments offer important carbon and biodiversity cobenefits. Global Change Biology 26, 509–522. †McMullin, R. T., Ure, D., Smith, M., Clapp, H. & Wiersma, Y. F. (2017). Ten years of monitoring air quality and ecological integrity using field-identifiable lichens at Kejimkujik National Park and National Historic Site in Nova Scotia, Canada. Ecological Indicators 81, 214–221. †Medeiros, H. R., Bochio, G. M., Ribeiro, M. C., Torezan, J. M. & dos Anjos, L. (2015). Combining plant and bird data increases the accuracy of an Index of Biotic Integrity to assess conservation levels of tropical forest fragments. Journal for Nature Conservation 25, 1–7. †Medeiros, H. R. & Torezan, J. M. (2013). Evaluating the ecological integrity of Atlantic forest remnants by using rapid ecological assessment. Environmental Monitoring and Assessment 185, 4373–4382. †Meng, J.,Li, S.,Wang,W.,Liu, Q.,Xie, S.&Ma,W. (2016). Mapping forest health using spectral and textural information extracted from SPOT-5 satellite images. Remote Sensing 8, 719. †Meng, Y., Cao, B., Dong, C. & Dong, X. (2019). Mount Taishan Forest ecosystem health assessment based on forest inventory data. Forests 10, 1–14. †Meng, Y., Liu, X., Ding, C., Xu, B., Zhou, G. & Zhu, L. (2020). Analysis of ecological resilience to evaluate the inherent maintenance capacity of a forest ecosystem using a dense Landsat time series. Ecological Informatics 57, 101064. Mesquita, R. C. G., Ickes, K., Ganade, G. & Williamson, G. B. (2001). Alternative successional pathways in the Amazon Basin. Journal of Ecology 89, 528–537. Mesquita, R. D. C. G., Massoca, P. E. D. S., Jakovac, C. C., Bentos, T. V. & Williamson, G. B. (2015). Amazon rain forest succession: stochasticity or land- use legacy? BioScience 65, 849–861. †Michez, A., Piégay, H., Toromanoff, F., Brogna, D., Bonnet, S., Lejeune, P. & Claessens, H. (2013). LiDAR derived ecological integrity indicators for riparian zones: application to the Houille river in Southern Belgium/Northern France. Ecological Indicators 34, 627–640. Michon, G., de Foresta, H., Levang, P.&Verdeaux, F. (2007). Domestic forests: a new paradigm for integrating local communities’ forestry into tropical forest science. Ecology and Society 12, 1. *Millar, C. I. & Stephenson, N. L. (2015). Temperate forest health in an era of emerging megadisturbance. Science 349, 823–826. Millennium Ecosystem Assessment (2005). Ecosystems and Human Well-being: Biodiversity Synthesis. Word Resources Institute, Washington. †Misurec, J., Kopackov�a, V., Lhotakova, Z., Hanus, J., Weyermann, J., Entcheva-Campbell, P. & Albrechtova, J. (2012). Utilization of hyperspectral image optical indices to assess the Norway spruce forest health status. Journal of Applied Remote Sensing 6, 063545. †Mora, F. (2017). Nation-wide indicators of ecological integrity in Mexico: the status of mammalian apex-predators and their habitat. Ecological Indicators 82, 94–105. †Mora, F. (2019). The use of ecological integrity indicators within the natural capital index framework: the ecological and economic value of the remnant natural capital of México. Journal for Nature Conservation 47, 77–92. *Morel, A. C. & Nogué, S. (2019). Combining contemporary and paleoecological perspectives for estimating forest resilience. Frontiers in Forests and Global Change 2, 57. Norden, N., Angarita, H. A., Bongers, F., Martı́nez-Ramos, M., Granzow- de la Cerda, I., van Breugel, M., Lebrija-Trejos, E., Meave, J. A., Vandermeer, J., Williamson, G. B., Finegan, B., Mesquita, R. & Chazdon, R. L. (2015). Successional dynamics in Neotropical forests are as uncertain as they are predictable. Proceedings of the National Academy of Sciences of the United States of America 112, 8013–8018. *O’Brien, A., Townsend, K., Hale, R., Sharley, D. & Pettigrove, V. (2016). How is ecosystem health defined and measured? A critical review of freshwater and estuarine studies. Ecological Indicators 69, 722–729. †O’Laughlin, J. & Cook, P. (2003). Inventory-based forest health indicators: implications for national forest management. Journal of Forestry 101, 11–17. Pan, Y., Birdsey, R. A., Fang, J., Houghton, R., Kauppi, P. E., Kurz, W. A., Phillips, O. L., Shvidenko, A., Lewis, S. L., Canadell, J. G., Ciais, P., Jackson, R. B., Pacala, S. W., McGuire, A. D., Piao, S., ET AL. (2007). A large and persistent carbon sink in the World’s forests. Science 333, 988–993. Pan, Y., Birdsey, R. A., Fang, J., Houghton, R., Kauppi, P. E., Kurz, W. A., Phillips, O. L., Shvidenko, A., Lewis, S. L., Canadell, J. G., Ciais, P., Jackson, R. B., Pacala, S. W., McGuire, A. D., Piao, S., ET AL. (2011). A large and persistent carbon sink in the world’s forests. Science 333, 988–993. *Parrish, J. D., Braun, D. P. &Unnasch, R. S. (2003). Are we conserving what we say we are? Measuring ecological integrity within protected areas. BioScience 53, 851–860. Peña-Claros, M. (2003). Changes in forest structure and species composition during secondary forest succession in the Bolivian Amazon. Biotropica 35, 450–461. †Peng, J., Liu, Y., Li, T. &Wu, J. (2017). Regional ecosystem health response to rural land use change: a case study in Lijiang City, China. Ecological Indicators 72, 399–410. Pérez-C�ardenas, N., Mora, F., Arreola-Villa, F., Arroyo-Rodrı́guez, V., Balvanera, P., Flores-Casas, R., Navarrete-Pacheco, A. & Ortega- Huerta, M. A. (2021). Effects of landscape composition and site land-use intensity on secondary succession in a tropical dry forest. Forest Ecology and Management 482, 118818. †Perles, S. J., Wagner, T., Irwin, B. J., Manning, D. R., Callahan, K. K. & Marshall, M. R. (2014). Evaluation of a regional monitoring program’s statistical power to detect temporal trends in forest health indicators. Environmental Management 54, 641–655. *Perz, S. G., Muñoz-Carpena, R., Kiker, G. & Holt, R. D. (2013). Evaluating ecological resilience with global sensitivity and uncertainty analysis. Ecological Modelling 263, 174–186. Pickett, S. T. A., Collins, S. L. & Armesto, J. J. (1987). A hierarchical consideration of succession. Vegetatio 69, 109–114. Pinho, B. X.,Melo, F. P. L.,Arroyo-Rodrı́guez, V., Pierce, S., Lohbeck, M.& Tabarelli, M. (2018). Soil-mediated filtering organizes tree assemblages in regenerating tropical forests. Journal of Ecology 106, 137–147. Poorter, L., Bongers, F., Aide, T. M., Almeyda Zambrano, A. M., Balvanera, P., Becknell, J. M., Boukili, V., Brancalion, P. H. S., Broadbent, E. N., Chazdon, R. L., Craven, D., de Almeida-Cortez, J. S., Cabral, G. A. L., de Jong, B. H. J., Denslow, J. S., ET AL. (2016). Biomass resilience of Neotropical secondary forests. Nature 530, 1–15. Biological Reviews 98 (2023) 662–676 © 2022 Cambridge Philosophical Society. 674 Milena F. Rosenfield and others 1469185x, 2023, 2, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/brv.12924 by M useu Paraense E m ílio G oeldi, W iley O nline L ibrary on [15/01/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense Poorter, L., Craven, D., Jakovac, C. C., van der Sande, M. T., Amissah, L., Bongers, F., Chazdon, R. L., Farrior, C. E., Kambach, S., Meave, J. A., Muñoz, R., Norden, N., Rüger, N., van Breugel, M., Almeyda Zambrano, A. M., ET AL. (2021). Multidimensional tropical forest recovery. Science 374, 1370–1376. Powers, J. S.&Marı́n-Spiotta, E. (2017). Ecosystem processes and biogeochemical cycles in secondary tropical forest succession. Annual Review of Ecology, Evolution, and Systematics 48, 497–519. Prach, K., Durigan, G., Fennessy, S., Overbeck, G. E., Torezan, J. M. & Murphy, S. D. (2019). A primer on choosing goals and indicators to evaluate ecological restoration success. Restoration Ecology 27, 917–923. †Randolph, K. C. (2013). Development history and bibliography of the US Forest Service crown-condition indicator for forest health monitoring. Environmental Monitoring and Assessment 185, 4977–4993. *Rapport, D. J. (1989). What constitutes ecosystem health? Perspectives in Biology and Medicine 33, 120–132. *Rapport, D. J., Costanza, R. & McMichael, A. J. (1998). Assessing ecosystem health. Trends in Ecology & Evolution 13, 397–402. †Rempel, R. S., Naylor, B. J., Elkie, P. C., Baker, J., Churcher, J. & Gluck, M. J. (2016). An indicator system to assess ecological integrity of managed forests. Ecological Indicators 60, 860–869. *Reyer, C. P. O., Brouwers, N., Rammig, A., Brook, B. W., Epila, J., Grant, R. F., Holmgren, M., Langerwisch, F., Leuzinger, S., Lucht, W., Medlyn, B., Pfeifer, M., Steinkamp, J., Vanderwel, M. C., Verbeeck, H., ET AL. (2015). Forest resilience and tipping points at different spatio-temporal scales: approaches and challenges. Journal of Ecology 103, 5–15. *†Reza, M. I. H.& Abdullah, S. A. (2011). Regional Index of Ecological Integrity: a need for sustainable management of natural resources. Ecological Indicators 11, 220–229. Rezende, G. M. & Vieira, D. L. M. (2019). Forest restoration in southern Amazonia: soil preparation triggers natural regeneration. Forest Ecology and Management 433, 93–104. Robiglio, V. & Sinclair, F. (2011). Maintaining the conservation value of shifting cultivation landscapes requires spatially explicit interventions. Environmental Management 48, 289–306. *Roche, P. K. & Campagne, C. S. (2017). From ecosystem integrity to ecosystem condition: a continuity of concepts supporting different aspects of ecosystem sustainability. Current Opinion in Environmental Sustainability 29, 63–68. *Rombouts, I., Beaugrand, G., Artigas, L. F., Dauvin, J.-C., Gevaert, F., Goberville, E., Kopp, D., Lefebvre, S., Luczak, C., Spilmont, N., Travers-Trolet, M., Villanueva, M. C. & Kirby, R. R. (2013). Evaluating marine ecosystem health: case studies of indicators using direct observations and modelling methods. Ecological Indicators 24, 353–365. Rozendaal, D. M. A., Bongers, F., Aide, T. M., Alvarez-D�avila, E., Ascarrunz, N., Balvanera, P., Becknell, J. M., Bentos, T. V., Brancalion, P. H. S., Cabral, G. A. L., Calvo-rodriguez, S., Chave, J., César, R. G., Chazdon, R. L., Condit, R., ET AL. (2019). Biodiversity recovery of Neotropical secondary forests. Science Advances 5, eaau3114. †Sanders, S. & Grochowski, J. (2014). Alternative metrics for evaluating forest integrity and assessing change at four Northern-tier U.S. National Parks. The American Midland Naturalist 171, 185–203. *Sasaki, T., Furukawa, T., Iwasaki, Y., Seto, M. & Mori, A. S. (2015). Perspectives for ecosystem management based on ecosystem resilience and ecological thresholds against multiple and stochastic disturbances. Ecological Indicators 57, 395–408. *Scheffer, M., Carpenter, S. R., Dakos, V. & van Nes, E. H. (2015). Generic indicators of ecological resilience: inferring the chance of a critical transition. Annual Review of Ecology, Evolution, and Systematics 46, 145–167. †Sharma, A. & Goyal, M. K. (2018). Assessment of ecosystem resilience to hydroclimatic disturbances in India. Global Change Biology 24, e432–e441. *Souza, G. B. G. & Vianna, M. (2020). Fish-based indices for assessing ecological quality and biotic integrity in transitional waters: a systematic review. Ecological Indicators 109, 105665. †Sowi�nska-Świerkosz, B. (2017). Application of surrogate measures of ecological quality assessment: the introduction of the Indicator of Ecological Landscape Quality (IELQ). Ecological Indicators 73, 224–234. †Stevens-Rumann, C. S., Kemp, K. B., Higuera, P. E., Harvey, B. J., Rother, M. T., Donato, D. C., Morgan, P. & Veblen, T. T. (2018). Evidence for declining forest resilience to wildfires under climate change. Ecology Letters 21, 243–252. †Stolte, K. W. (2001). Forest health monitoring and forest inventory analysis programs monitor climate change effects in forest ecosystems. Human and Ecological Risk Assessment: An International Journal 7, 1297–1316. †Styers, D. M., Chappelka, A. H., Marzen, L. J. & Somers, G. L. (2010a). Developing a land-cover classification to select indicators of forest ecosystem health in a rapidly urbanizing landscape. Landscape and Urban Planning 94, 158–165. †Styers, D. M., Chappelka, A. H., Marzen, L. J. & Somers, G. L. (2010b). Scale matters: indicators of ecological health along the urban-rural interface near Columbus, Georgia. Ecological Indicators 10, 224–233. Styger, E., Rakotondramasy, H. M., Pfeffer, M. J., Fernandes, E. C. M. & Bates, D. M. (2007). Influence of slash-and-burn farming practices on fallow succession and land degradation in the rainforest region of Madagascar. Agriculture, Ecosystems and Environment 119, 257–269. †Talukdar, N. R., Ahmed, R., Choudhury, P. & Barbhuiya, N. A. (2020). Assessment of forest health status using a forest fragmentation approach: a study in Patharia Hills Reserve Forest, northeast India. Modeling Earth Systems and Environment 6, 27–37. *Tett, P.,Gowen, R. J., Painting, S. J., Elliott, M., Forster, R.,Mills, D. K., Bresnan, E., Capuzzo, E., Fernandes, T. F., Foden, J., Geider, R. J., Gilpin, L. C., Huxham, M., McQuatters-Gollop, A. L., Malcolm, S. J., ET AL. (2013). Framework for understanding marine ecosystem health. Marine Ecology Progress Series 494, 1–27. †Thormann, M. N. (2006). Lichens as indicators of forest health in Canada. Forestry Chronicle 82, 335–343. *†Tierney, G. L., Faber-Langendoen, D., Mitchell, B. R., Shriver, W. G. & Gibbs, J. P. (2009). Monitoring and evaluating the ecological integrity of forest ecosystems. Frontiers in Ecology and the Environment 7, 308–316. Toledo, M. & Salick, J. (2006). Secondary succession and indigenous management in semideciduous forest fallows of the Amazon basin. Biotropica 38, 161–170. *Truchy, A.,Angeler, D. G., Sponseller, R. A., Johnson, R. K.&McKie, B. G. (2015). Linking biodiversity, ecosystem functioning and services, and ecological resilience: towards an integrative framework for improved management. Advances in Ecological Research 53, 55–96. *Trumbore, S., Brando, P. & Hartmann, H. (2015). Forest health and global change. Science 349, 814–818. †Urker, O. & Ilemin, Y. (2019). A pioneer study on the wildlife properties of Anatolian sweetgum forests, a case assesment on mammalian diversity in terms of ecosystem integrity. Fresenius Environmental Bulletin 28, 5474–5480. van Breugel, M., Martı́nez-Ramos, M. & Bongers, F. (2006). Community dynamics during early secondary succession in Mexican tropical rain forests. Journal of Tropical Ecology 22, 663–674. van der Sande, M. T., Powers, J. S., Kuyper, T. W., Norden, N., Salgado- Negret, B., Almeida, J. S., Bongers, F., Delgado, D., Dent, D. H., Derroire, G., Espirito Santo, M. M., Dupuy, J. M., Fernandes, G. W., Finegan, B., Gavito, M. E., ET AL. (2023). Soil resistance and recovery during Neotropical forest succession. Philosophical Transactions of the Royal Society B: Biological sciences 378, 20210074. Vieira, D. L. M.,Rodrigues, S. B., Jakovac, C. C., da Rocha, G. P. E.,Reis, F.& Borges, A. (2021). Active restoration initiates high quality forest succession in a deforested landscape in Amazonia. Forests 12, 1022. Vieira, I., Gardner, T., Ferreira, J., Lees, A. & Barlow, J. (2014). Challenges of governing second-growth forests: a case study from the Brazilian Amazonian State of Par�a. Forests 5, 1737–1752. *Vora, R. S. (1997). Developing programs to monitor ecosystem health and effectiveness of management practices on Lakes States National Forests, USA. Biological Conservation 80, 289–302. †Wang, N. & Bao, Y. (2011). Modeling forest quality at stand level: a case study of loess plateau in China. Forest Policy and Economics 13, 488–495. *Wilcox, D. A.,Meeker, J. E.,Hudson, P. L., Armitage, B. J., Black, M. G. & Uzarski, D. G. (2002). Hydrologic variability and the application of Index of Biotic Integrity metrics to wetlands: a great lakes evaluation. Wetlands 22, 588–615. †Woodall, C. W., Amacher, M. C., Bechtold, W. A., Coulston, J. W., Jovan, S., Perry, C. H., Randolph, K. C., Schulz, B. K., Smith, G. C., Tkacz, B. & Will-Wolf, S. (2011). Status and future of the forest health indicators program of the USA. Environmental Monitoring and Assessment 177, 419–436. †Woodall, C. W.,Grambsch, P. L.,Thomas, W.&Moser, W. K. (2005). Survival analysis for a large-scale forest health issue: Missouri oak decline. Environmental Monitoring and Assessment 108, 295–307. †Woodall, C. W., Morin, R. S., Steinman, J. R. & Perry, C. H. (2010). Comparing evaluations of forest health based on aerial surveys and field inventories: oak forests in the Northern United States. Ecological Indicators 10, 713–718. †Wu, J.& Liang, S. (2020). Assessing terrestrial ecosystem resilience using satellite leaf area index. Remote Sensing 12, 595. †Wu, L., Weibin, Y., Zhirong, J., Shihong, X. & Dongjin, H. (2018). Ecosystem health assessment of Dongshan Island based on its ability to provide ecological services that regulate heavy rainfall. Ecological Indicators 84, 393–403. Wurtzebach, Z. & Schultz, C. (2016). Measuring ecological integrity: history, practical applications, and research opportunities. BioScience 66, 446–457. *Xu, F. L., Jørgensen, S. E. & Tao, S. (1999). Ecological indicators for assessing freshwater ecosystem health. Ecological Modelling 116, 77–106. Biological Reviews 98 (2023) 662–676 © 2022 Cambridge Philosophical Society. Ecological integrity of tropical secondary forests 675 1469185x, 2023, 2, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/brv.12924 by M useu Paraense E m ílio G oeldi, W iley O nline L ibrary on [15/01/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense *Xulu, S., Gebreslasie, M. T. & Peerbhay, K. Y. (2019). Remote sensing of forest health and vitality: a South African perspective. Southern Forests: A Journal of Forest Science 81, 91–102. *Yang, H., Shao, X. & Wu, M. (2019). A review on ecosystem health research: a visualization based on CiteSpace. Sustainability 11, 4908. †Zarnoch, S. J., Bechtold, W. A. & Stolte, K. W. (2004). Using crown condition variables as indicators of forest health. Canadian Journal of Forest Research 34, 1057–1070. †Zhao, J.,Wang, R., Luo, P., Xing, L. & Sun, T. (2017). Visual ecology: exploring the relationships between ecological quality and aesthetic preference. Landscape and Ecological Engineering 13, 107–118. IX. SUPPORTING INFORMATION Additional supporting information may be found online in the Supporting Information section at the end of the article. Table S1. List of 41 references obtained from search #1 in Web of Science, using search terms associated with the concept of ecological integrity, and the additional six references sourced from citations