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18 March 2013 Assessing sampling biases in logging impact studies in tropical forests
Juliana Laufer, Fernanda Michalski, Carlos A. Peres
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The ecological responses of tropical forest wildlife to selective timber extraction have received considerable attention in the last few decades, yet there is little consensus among the large number of studies about the most appropriate sampling design. Here, we reviewed 26 years of tropical forest logging literature to evaluate the relationship between sampling design and the quality of information reported, which varied greatly among 75 studies. Most studies (88%) failed to include a pre-logging baseline condition in the sampling design, and the temporal scale of post-logging studies was generally inadequate. Studies also usually failed to report key information on study areas; only half of the articles reported some information on the spatial scale of the study, and only one-third presented some quantitative metric to describe forest habitat structure. Additionally, most studies (64%) failed to report the type of forest management and almost half (45%) did not describe the intensity of timber harvest in the logged areas. These sampling and reporting biases in logging studies hugely undermine the comparability among studies. We conclude with some general guidelines to maximize comparability among studies, and to enhance the potential usefulness of future logging studies for wildlife conservation strategies in tropical forest regions.


The growing global demand for forest products has markedly fueled the expansion and intensity of the logging industry in tropical forest regions [1234]. The last remaining tracts of pristine tropical forests safeguard the highest tree species diversity and the most valuable timber species [5], raising questions about the long-term viability of timber extraction [6]. This is especially true in Amazonia, which holds over half of the world's remaining tropical forest [7].

Selective logging has been proposed as a type of land use that is the least detrimental to animal and plant communities in tropical forests [891011]. Hence, a growing number of studies have sought to determine the effects of selective logging on tropical forests [12]. However, most of these studies incorporate potential biases and use different sampling techniques, taxonomic groups, temporal and spatial scales, forest management treatments, and other covariates that could affect the strength and direction of their results [10, 13141516].

The application of different sampling designs in studies evaluating the ecological effects of selective logging on fauna render their comparability difficult, reducing the effectiveness of conservation initiatives in tropical forests [17]. These differences can be found in spatial scales and duration of studies, biodiversity metrics used, number of spatial and temporal replicates, and information on habitat structure and composition within the study areas.

One of the key points in study design is the choice of spatial scale used, which is intimately related to the effect size of disturbance [18] and the biological traits of species [17]. Based on the intermediate disturbance hypothesis [19], a number of authors have suggested that small-scale selective logging may increase species richness and diversity by boosting habitat heterogeneity and paving the way for habitat specialists and generalists to coexist side by side [2021]. However, the same taxa observed at a landscape scale may indicate a reduction in species turnover and higher levels of habitat homogeneity, leading to lower regional biodiversity metrics [18]. Thus, there is need to understand how the variation in the intensity of disturbance acts on the entire landscape, since logging areas are rarely selected randomly, and usually allocated to gentle topographic slopes with higher concentrations of commercially valuable timber species [2223].

The correct understanding of historical data of any given study area, including the forest management type applied, is critical background information for logging studies, because landscape history and context (e.g. hunting, clear-cutting, and fire - [2425]) can affect results and their interpretation. The history of logging disturbance (e.g. volume harvested, density of logging roads and skid trails built to extract roundlogs, collateral damage, and use of mechanized extraction) should also be evaluated to better understand the results obtained [21, 262728]. The intensity of timber harvesting is also directly and positively related to ecological impacts on wildlife and forest structure [8, 23, 2930].

The time lag between the end of any logging disturbance and the start of data acquisition may also strongly influence the results of selective logging studies [22, 3132]. When field studies are carried out within five years after selective logging is discontinued, this can affect results due to the possibility of response delay or ecological relaxation of many organisms (e.g. many species affected are associated with low reproductive rates and long life cycles [31, 333435]). On the other hand, studies conducted a long time (>15 years) after the latest logging disturbance have other confounding variables that must be taken into account in interpreting results. For example, over long time scales natural disturbance events could affect the study area (e.g. natural forest succession, hurricanes or drought [28, 33, 36]), all of which can mask ecological responses to anthropogenic disturbance.

In addition, understanding the structure and composition of the forest and the organisms investigated before and after logging disturbance can provide essential information for more accurate assessments about the effects of selective logging and its effects on faunal structure and composition [37383940]. The lack of baseline information on the pre-cutting period can result in incorrect assessments of species distributions due to the spatial heterogeneity of forest species within the study area [41]. Furthermore, complementarity afforded by pre- and post-logging data and a long-term study in the aftermath of logging is often the best approach to understand the effects of disturbance [22].

Additionally, the synergistic interactions between logging and other co-occurring land-uses and natural climatic variability comprise one of the main threats to tropical forest biodiversity [3, 42]. Therefore, organismal response to changes in forest structure/composition induced by concomitant activities must also be evaluated. Synergistic effects may affect the results and/or obscure the potential effects of selective logging [43]. For example, hunting and wildfires have been identified as activities that aggravate the effects of selective logging when occurring in the same area [3, 7, 2324, 444546]. Selective logging opens up access to isolated forests, facilitating hunter access [47], which in turn can create markets for bushmeat and increase hunting in new logging areas [3]. The bushmeat supply to local villages increased by 64% near a logging concession in the Democratic Republic of Congo following the creation of logging roads [46]. Intensive hunting pressure can alter the species diversity, standing biomass and structure of animal communities [4849], all of which can mask the effects of logging disturbance alone.

Despite the role of spatial and historical context in local ecological responses to selective logging, there is no consensus among researchers over the most appropriate sampling design to evaluate the effects of selective logging on tropical forest species [18]. This consensus is absent even at the continental scale, and there is also little agreement on how to report key forest habitat and management information. Furthermore, few studies describing the influence of time scale and harvesting intensity are available [1314, 505152], so that differences in cause-effect relationships associated with the responses detected remain obscure.

Here, we assess the wide variation in sampling design of studies addressing the basic question of how selective logging affects forest wildlife in tropical forest regions. We compiled a comprehensive checklist of neotropical and paleotropical studies on the effects of logging-induced forest disturbance on both vertebrates and invertebrates, in order to: (1) evaluate the relationship between sampling design and the quality of the information reported, to ensure comparability among studies, and (2) provide guidelines on which relevant information should be reported in any literature resulting from those studies. We aim to provide clear directions to maximize comparability among studies, and to enhance the potential usefulness of future studies for wildlife conservation strategies in tropical forest regions.


Compilation of studies

We reviewed the available formal logging literature focusing on tropical forest fauna. We first conducted a search of ISI Web of Knowledge on the 3rd August 2012, using the terms “selective logging,” “logging,” or “timber” together with “tropical forest” and “fauna,” “vertebrates,” “invertebrates” or “wildlife.” These searches returned a total of 249 publications, which were then examined and filtered to ensure we considered all studies reporting on the impact of tropical timber extraction on any faunal taxa. We explored studies that reported on the effects of logging per se as well as those considering other anthropogenic perturbations in tropical forests (e.g., clear-cutting, monocultures, and wildfires) coupled with logging. From the initial total of 249 potential articles we obtained, only 36 met these criteria. We conducted another search using the same keywords within Google Scholar and identified an additional 14 relevant articles within the first 50 records. Additionally, we included 25 articles based on our background reading but not found in the searches that also addressed our research questions. This resulted in a total of 75 articles.

Articles were reviewed to extract the following data: 1) geographic location and coordinates; 2) taxa studied (small mammals, bats, birds, reptiles, amphibians, medium and large-bodied mammals, and invertebrates); 3) spatial scale of the study, including the total area sampled (i.e. measured on the basis of the most extreme vertices describing the study area polygon, excluding for Ernst et al. [27], Barlow et al. [53], Cleary [54], Johns [55], and Jones et al. [56], which considered much longer distances between study plots [> 800 km] and we used the largest studied area of each one as spatial scale), and the distances between study sites, measured from any logged sampling area to the nearest unlogged primary forest or between sampling sites, such as transects, traps or point counts, between forest management treatments; 4) recovery time scale (years between the cessation of any logging activity and the time of data acquisition); 5) management type of each study site (i.e., reduced-impact selective logging - hereafter Reduced Impact Logging or RIL, conventional selective logging - hereafter Conventional Logging or CL, non-mechanized selective logging - hereafter Non-mechanized logging or NML, or unreported by the study), including number of cutting cycles, and selective logging intensity (e.g., stump density - stems/ha, basal-area removal - m2/ha, or volumetric removal - m3/ha). This was often reported as stems per hectare or cubic meters per hectare, so we used different metrics to evaluate the intensity of selective logging; 6) habitat quality of residual logged and unlogged areas (i.e., based on maps, tree density, or forest basal area); 7) presence of (pseudo) control areas and availability of pre-logging data on flora or fauna from logged areas; 8) sampling technique (e.g., line-transect census, point counts, traps); 9) use of environmental covariates to interpret the results; 10) co-occurrence of other anthropogenic disturbances that could affect the results (e.g., wildfire, hunting, and fragmentation) in the study area.

When the report failed to provide geographic coordinates, we used Google Earth (GE) to obtain the most approximate coordinates supported by maps of the study area and key landmarks such as rivers, roads, villages, towns and other visual features that could be clearly distinguished by GE images. When studies reported more than one study site coordinate, we calculated the mean positional fixes between these points, with equal distances between all points reported. The mean distances reported within any given study ranged from 0.5 to 12.9 km (mean = 1.38 km). However, for two intercontinental comparative studies, coordinate points from each study area were plotted in the map [27, 55]. We used the ArcGis 9.2 [57] in order to produce the final distribution map (Fig. 1).

When we were unable to find reliable information on forest management patch size, distance between sampling sites or distance to the nearest unlogged primary forest, we simply estimated these values based on maps and spatial scale provided by the reports, and/or based on GE images. We selected the longest time interval when examining studies that compared different periods between the onset of forest disturbance and data collection. In order to standardize the time scale variable, whereby we recorded the time between logging activity and data collection, we calculated the time (years) from the last year of any reported disturbance until the onset of field sampling within each study.

Fig. 1.

Geographic distribution of single taxon or multi-taxa studies on faunal responses to selective logging in Tropical Forests (see Appendix 1 for references). Orange circles are sized proportionally to the total number of studies at each site (range = 1 – 4 studies per site).



Geographic and taxonomic spread of studies

The 75 papers we selected (Fig. 2, Appendix 1) were published from 1986 to 2012, most of them since 2000 (72%). Most studies were carried out in South/Southeast Asia (39%), followed by South America (31%), Africa (20%), Oceania (5%), and Mesoamerica (5%) (Fig. 1, Appendix 1). Most logging impact studies took place in Malaysia (27%), especially the state of Sabah in Borneo (21%), followed by Brazil (17%) and Uganda (9%). However, most Asian studies are concentrated in few geographic areas, whereas those in the Neotropics are more widely distributed, with only a few countries having more than three studies (Fig.1).

Fig. 2.

Annual number of logging studies from 1986 to 2012. Line thickness is proportional to the total area sampled (log10 km2) in each study.


In relation to the taxonomic group covered, most studies evaluated the effects of logging on vertebrates (70%) followed by invertebrates (30%) (Fig. 3, Appendix 1). Butterflies, ants, and beetles comprise the most studied invertebrates, with all remaining taxa representing 28% of all studies. However, all lepidopterans combined (butterflies and moths) represent the most popular invertebrate taxon in the logging impact literature (31%). Medium and large-bodied mammals, and primates in particular, represent the most heavily studied verterbrate taxa (44%), followed by birds (28%), small nonvolant mammals (12%), herpetofauna (11%) and bats (5%).

Fig. 3.

Number of logging studies per faunal group studied. Frequency distribution of 13 faunal groups from single taxon and multi-taxa logging impact studies published from 1986 to 2012 (n = 75 articles) in tropical forests.


Potential sampling biases

Spatial and time scale

The spatial scale of the studies we reviewed ranged from less than one square kilometre to tens of thousands of square kilometres (<1 - ~53,000 km2) (Fig. 4). Although many studies assessed logging effects at a scale of dozens to thousands of square kilometres, 38 studies were carried out at scales below 100 km2 (Appendix 1) and 14 addressed even smaller spatial scales (<10 km2) (Fig. 4).

Invertebrate studies were conducted at highly variable spatial scales (range = 2 – 16900 km2) with the smallest and largest scales in two studies on butterflies and termites in Belize and Sumatra [22, 28], and ants and spiders in Amazon [58], respectively. Compared with invertebrates, vertebrate studies were conducted at larger spatial scales (range = <1 – 53,000 km2). Overall, the spatial scale used in multi-taxa studies was larger than those considering a single taxon (Fig. 4).

An overview of recovery time scales showed that most studies (45%) were carried out long after the cessation of logging disturbance (> 15 years). The remaining studies evaluated logging effects at medium (6-15 years, 35%) or short time scales (≤ 5 years, 20%) following logging. However, 32% of all studies were conducted >20 years after logging disturbance. The only study representing a much longer time period (> 50 years) used a theoretical rather than empirical modelling approach [38]

Fig. 4.

Relationship between study duration and spatial extent of tropical forest logging studies. Symbols show values from different regions and the taxonomic coverage of 75 logging impact studies published from 1986 to 2012. The line shows predictions from loess regression and the grey shaded area is the 95% confidence interval.


Forest quality report

The information on forest habitat structure reported by authors varied greatly, as well as the quality of figures and/or maps presented in these studies. Half of the articles showed a figure or map with some information about scale, ranging from regional to local scales of the study sites, sampling design (sampling points or transects), logged patches, type of habitat and surrounding landscape. However, only 16 of these studies clearly presented a map of the study area, and 15 studies reported the habitat and/or landscape surrounding the study sites.

From all studies compiled, 24 presented some quantitative metric to describe forest structure, with 11 articles providing information on forest basal area (m2/ha). The remaining studies reported other quantitative variables to describe habitat structure such as the number, density (trees/ha), and volume (m3/ha) of trees in the original stand or removed by logging. From all 24 studies with some information on forest structure, only eight presented quantitative metrics of both logged and unlogged forest separately, while other studies only presented a brief description of logged or unlogged forest.

Forest management report

Despite its critical relevance, 64% of the studies we reviewed did not even report the type of forest management used in the logged forests studied. From those papers reporting forest management type, the most common was conventional logging, followed by reduced-impact logging, and non-mechanized logging, occurring in 17%, 12%, and 4% of the studies, respectively.

The intensity of timber harvest in the study areas was reported under four different metrics, of which stems and cubic meters per hectare were most frequent. The intensity of harvest was also measured by the percentage of trees basal area removal (m2/ha), or ranked (e.g. low intensity - [59]). Those studies reporting the logging intensity in terms of volumetric removal (m3/ha) reported ranges from 0.22 in the Democratic Republic of Congo [20] to 145.3 m3/ha in Sabah, Borneo [32]. The logging intensity of the studies that reported stems/ha, ranged from 0.23 stem/ha in a mahogany logging operation in southeastern Amazonia [60] to 19.5 stem/ha in Guyana [27].

Other relevant sampling information

Most studies reviewed here (93%) used faunal empirical data, and almost all studies (96%) used both a primary or unlogged forest as a control site, while only three studies failed to use a control site. Although the acquisition of fauna and pre-logging habitat data was highlighted as very important in order to understand disturbance effects [61], these data were missing from the majority of the studies (88%).

The use of environmental covariates to interpret the effect of logging on forest fauna was common in most studies (71%), although the number of covariates and the sampling technique used ranged widely. Most of these covariates were linked with forest structure (e.g., canopy openness, topography, forest type, logging damage). Information on presence or absence of other disturbances that could influence the organism's responses (e.g., hurricanes, hunting, agriculture or fire) was reported in approximately half of the studies (55%).


There is an increase in the number and extent of sustainable-use reserves worldwide, as 86% of all global protected areas now permit some form of human use [62]. Consequently, there is a huge opportunity to integrate multiple resource use, including selective logging, in the largest remaining expanses of tropical forest [63]. Additionally, there is a global increase in certification applications [61]. Among the minimum requirements for certification, logging companies must gather information that is also useful to assess the effects of selective logging (e.g., 100% inventory, spatial data on canopy trees, harvested species, forest stand, and timber offtake). This creates a unique opportunity for new logging studies to use these data. However, such studies and their results must be standardized to enhance comparability [64]. The availability of comparable data for logging studies would contribute to the long-term conservation and sustainability of large mosaics of logged and unlogged tropical forests.

Published logging studies differ in several aspects that limit any comparative analysis on logging impacts [64]. Differences include focal taxa, forest management system, spatial and temporal scales of the studies, and a general failure to address potentially confounding covariates (Appendix 1). Due to the complexities of the forest management study system few studies provide appropriate summaries on the effects of selective logging on wildlife [11, 14, 51, 656667].

Choice of Spatial and Time scale

Logging impacts increase the challenges of analyzing the effect of spatial scale on naturally heterogeneous tropical forest landscapes [26, 39, 68]. Indeed, several authors have suggested that the impact of logging depends on the spatial scale of the study [18, 69]. The spatial heterogeneity of the study area must therefore be explicitly considered in the sampling design [707172]. Additionally, selective logging disturbance is not homogeneous throughout the landscape, with areas containing higher basal areas of commercially valuable timber species typically being the most intensively impacted [21, 56].

In general, studies showed an increase of biodiversity metrics between logged and unlogged forests at small spatial scales. This occurs due to an increase of non-forest dependent organisms associated with greater habitat disturbance after logging [10]. Logging could therefore be erroneously interpreted as a low impact disturbance. However, if we consider larger spatial scales, the same biodiversity metrics may decrease [20]. Different effects have been variously reported for butterflies [14, 18, 50], small mammals [73], beetles [20] and medium and large vertebrates [40]. This variation in biodiversity metrics is in broad agreement with the intermediate disturbance theory [19].

In fact, logging operations may be far more degrading than our perception prior to this review, as there is no consensus to date on the ecological impacts on forest wildlife, with many studies reporting highly idiosyncratic and/or species-specific responses to logging disturbance [15, 31, 33, 58]. For instance, while population density of some primates decreases in logged forests in central Guyana, it increases for some terrestrial gamebirds (Tinamous) at the same site [31]. Thus, until more studies describe the effects of logging on different faunal groups, we cannot determine whether or not the overall effects of selective logging on forest wildlife are benign or detrimental.

Recovery time scale is a critical criterion to interpret the effects of selective logging, because the length of time after disturbance can affect the magnitude and nature of responses of forest organisms [74]. In addition, it is hardly possible to compare studies with different time scales [75]. Studies carried out within a single period after logging provide only a snapshot of the structure of animal populations and fail to consider the natural population dynamics in different forest successional stages [32]. This snapshot of faunal community structure could generate misleading interpretations as a consequence [74]. Although some authors were unable to detect a clear pattern between biodiversity metrics and time-lag after logging [76], other researchers found differences between species. This may be a function of post-harvest recovery time and disturbance intensity [27, 30]. The heavier the disturbance, the more time is required for forest recovery. An anuran community showed strong signs of recovery during four years after logging disturbance, but the time-lag selected by the study is often insufficient to observe complete recovery [27]. In peninsular Malaysia, primates exhibited a very slow recovery response to disturbance, so that longer-term data were required before robust conclusions could be reached [37]. In Uganda, two primate species (Cercopithecus mitis and C. ascanius) continued to decline even three decades after logging, while Colobus guereza were found at higher density in logged than unlogged forest [33]. This variation could be explained by species-specific traits [31], such as trophic guild [58], population abundance, and home range mobility. In an avifaunal study in a logged forest in French Guiana, bird species richness did not recover to levels observed in primary forest even 10 years after moderate logging disturbance [21]. In Uganda, long-term research conducted after logging showed that even after three decades the bird community had lost a large number of forest-dependent species [30].

Researchers must consider that the results from different time scales during long-term studies could also be affected by synergistic interactions between logging and other forms of anthropogenic disturbance and natural climatic variation [23]. These confounding variables must be controlled to avoid misleading interpretations [43]. Therefore, we recommend the use of long time scales relative to the generation time of target taxa to evaluate the degree to which selective logging affects wildlife. In addition, any type of disturbance must be reported in studies investigating effects of selective logging.

Baseline data and control sites

Spatially correlated studies including both pre and post-logging forest sites provide a much better approach to study the effects of logging than simply using a space-for-time substitutional approach. Longitudinal studies can compare the same forest before and after logging harvests and can explicitly control for several environmental gradients within sites [56], which are typically subject to inherent systematic biases in the choice of logging areas by operators (e.g., logged-over forest tends to be associated with higher pre-logging basal areas). Most studies reviewed here lacked before-and-after data, so researchers should meet the assumption of forest homogeneity in order to compare data within and between studies [69]. Additionally, this presupposes that any structural or compositional differences found in patterns of species abundance and diversity between logged and unlogged forests are induced by logging [17, 77], when this is often not the case. It is essential to know the degree of within-forest heterogeneity in the absence of logging [39] and collect preliminary data on existing populations prior to logging [78], in order to have a better understanding of long-term variation in biodiversity metrics.

Improved use of control sites is also required to effectively compare faunal abundances between treatments (i.e., different logging intensities or management systems). Control sites can also differ in terms of floristic composition, patch scale, forest habitat heterogeneity [69], topography, levels of harvest intensity [56], and previous history of anthropogenic disturbance other than logging. In some studies the “undisturbed” control forest site used was far from undisturbed [7879808182], yet these areas are frequently considered to be a satisfactory baseline to compare logging effects on faunal abundances. Including these differences in the results is essential for interpreting site comparisons and for understanding effects that can actually be assigned to logging [31, 76]. In addition, the use of truly undisturbed primary forest as control site is desirable whenever possible to avoid misleading interpretations.

Quality of report information

Our review shows that variation in the collection of essential information limits our ability to compare tropical forest logging studies. For example, basic but critically important information such as geographic coordinates of study areas are rarely reported [22, 32, 58, 69]. Many of the studies we reviewed failed to present a map showing the geographic location of the logged and unlogged forest areas, the availability of unlogged primary forest or other relevant information on surrounding habitat types (e.g., secondary forest, plantation, pasture). Such data (geographic location/surrounding habitat) are essential to understand the spatial structure of faunal responses to local- and landscape-scale phenomena, and ultimately evaluate the impact of selective logging on the wildlife. At a minimum, this information should be reported in a map or table in addition to a distance scale.

Most studies did not even report the forest management system applied in the study areas. The type of forest management has a differential influence on wildlife responses [60]. In general, conventional logging has much greater impacts on faunal species than reduced impact logging [36, 40, 58]. In addition, logging damage information is essential and complementary to forest management type in assessing logging impacts on forest structure and faunal communities [73, 8384]. Measures such as canopy fracture, standing tree density, tree mortality, density of skid trails, density of logging roads and patios, and basal-area offtake (m2/ha) or harvested timber volume (m3/ha) should be reported in all logging studies. Other information particularly relevant to African and Neotropical frugivores, is the identification of the harvested timber species, since some harvested species provide key food resources to forest wildlife [21,33, 55, 74, 8586].

Implications for conservation

Logged areas will become of increasingly greater importance for tropical forest wildlife conservation [6], considering the growing global demand for timber products and the resulting increase in the intensity and extent of selective logging in vast areas of tropical forests [2, 64]. Here, we showed that most of the studies we reviewed have marked differences in sampling designs and the quality of the supporting information they provide, which hugely undermines the comparability among studies. In order to enhance any quantitative synthesis of logging studies and contribute to quantitative assessments of the ecological sustainability of this pattern of land-use, we list some information that we consider critical for future selective logging studies.

- Whenever possible, large spatial areas must be used, in order to evaluate both landscape or regional scale changes in ecological responses of forest organisms to logging;

- Preference should be given to long-term monitoring programmes, in order to identify changes in long-lived species and/or slow recovery trajectories across generations in short-lived species;

- Studies should report both pre- and post-disturbance data to determine species response while avoiding pseudoreplication in sampling designs;

- It is essential to use appropriate control forest sites in the same landscape incorporating the logged forest sites, or to use forest areas with comparable structure and species composition in both logged and unlogged sites;

- Studies should report detailed information on forest management systems, forest habitat quality (e.g. basal area, m2/ha), sampling design (e.g. field techniques, replication, distance between primary forest and sample sites), and other potential natural (e.g., wind throws) and anthropogenic disturbances (e.g. hunting and wildfire) that can also affect the study areas and confound the effects of logging on study organisms.


JL was funded by a Brazilian Ministry of Education (CAPES) PhD studentship. We are grateful to Alejandro Estrada and two anonymous referees for comments on the manuscript.



Asner, G. P., Knapp, D. E., Broadbent, E. N., Oliveira, P. J. C., Keller, M., and Silva, J. N., 2005. Selective logging in the Brazilian Amazon. Science 310:480–482. Google Scholar


FAO. 2011. State of the World's Forests 2011. Food and Agriculture Organization, Roma, Italy. Google Scholar


Laurance, W. F., 1998. A crisis in the making: responses of Amazonian forests to land use and climate change. Trends in Ecology & Evolution 13:411–415. Google Scholar


Laurance, W. F., Alonso, A., Lee, M., and Campbell, P., 2006. Challenges for forest conservation in Gabon, central Africa. Futures 38:454–470. Google Scholar


Ahmed, S. E., and Ewers, R. M., 2012. Spatial Pattern of Standing Timber Value across the Brazilian Amazon. Plos One 7:e36099 Google Scholar


Putz, F. E., Zuidema, P. A., Synnott, T., Peña-Claros, M., Pinard, M. A., Sheil, D., Vanclay, J. K., Sist, P., Gourlet-Fleury, S., Griscom, B., Palmer, J., and Zagt, R., 2012. Sustaining conservation values in selectively logged tropical forests: the attained and the attainable. Conservation Letters 0:1–8. Google Scholar


FAO. 2010. Global Forest Resources Assessment 2010 Main report. Food and Agriculture Organization, Roma, Italy. Google Scholar


Grove, S. J., 2002. The influence of forest management history on the integrity of the saproxylic beetle fauna in an Australian lowland tropical rainforest. Biological Conservation 104:149–171. Google Scholar


Johns, A. D., 1991. Responses of Amazonian rain forest birds to habitat modification. Journal of Tropical Ecology 7:417–437. Google Scholar


Kudavidanage, E. P., Wanger, T. C., de Alwis, C., Sanjeewa, S., and Kotagama, S. W., 2012. Amphibian and butterfly diversity across a tropical land-use gradient in Sri Lanka; implications for conservation decision making. Animal Conservation 15:253–265. Google Scholar


Meijaard, E., and Sheil, D., 2008. The persistence and conservation of Borneo's mammals in lowland rain forests managed for timber: observations, overviews and opportunities. Ecological Research 23:21 Google Scholar


Shearman, P., Bryan, J., and Laurance, W. F., 2012. Are we approaching ‘peak timber’ in the tropics? Biological Conservation 151:17–21. Google Scholar


Danielsen, F., 1997. Stable environments and fragile communities: Does history determine the resilience of avian rain-forest communities to habitat degradation? Biodiversity and Conservation 6:423–433. Google Scholar


Hill, J. K., and Hamer, K. C., 2004. Determining impacts of habitat modification on diversity of tropical forest fauna: the importance of spatial scale. Journal of Applied Ecology 41:744–754. Google Scholar


Thiollay, J. M., 1997. Disturbance, selective logging and bird diversity: A Neotropical forest study. Biodiversity and Conservation 6:1155–1173. Google Scholar


Woltmann, S., 2003. Bird community responses to disturbance in a forestry concession in lowland Bolivia. Biodiversity and Conservation 12:1921–1936. Google Scholar


Stokes, E. J., Strindberg, S., Bakabana, P. C., Elkan, P. W., Iyenguet, F. C., Madzoke, B., Malanda, G. A. F., Mowawa, B. S., Moukoumbou, C., Ouakabadio, F. K., and Rainey, H. J., 2010. Monitoring Great Ape and Elephant Abundance at Large Spatial Scales: Measuring Effectiveness of a Conservation Landscape. Plos One 5:e10294. Google Scholar


Dumbrell, A. J., and Hill, J. K., 2005. Impacts of selective logging on canopy and ground assemblages of tropical forest butterflies: Implications for sampling. Biological Conservation 125:123–131. Google Scholar


Connell, J. H., 1978. Diversity in tropical rain forest and coral reefs - High diversity of trees and corals in maintained only in a non-equilibrium state. Science 199:1302–1310. Google Scholar


Davis, A. J., Holloway, J. D., Huijbregts, H., Krikken, J., Kirk-Spriggs, A. H., and Sutton, S. L., 2001. Dung beetles as indicators of change in the forests of northern Borneo. Journal of Applied Ecology 38:593–616. Google Scholar


Thiollay, J. M., 1992. Infuence of selective logging on bird species diversity in a Guianan rain forest. Conservation Biology 6:47–63. Google Scholar


Jones, D. T., Susilo, F. X., Bignell, D. E., Hardiwinoto, S., Gillison, A. N., and Eggleton, P., 2003. Termite assemblage collapse along a land-use intensification gradient in lowland central Sumatra, Indonesia. Journal of Applied Ecology 40:380–391. Google Scholar


Putz, F. E., Blate, G. M., Redford, K. H., Fimbel, R., and Robinson, J., 2001. Tropical forest management and conservation of biodiversity: an overview. Conservation Biology 15:7–20. Google Scholar


Barlow, J., and Peres, C. A., 2008. Fire-mediated dieback and compositional cascade in an Amazonian forest. Philosophical Transactions of the Royal Society B-Biological Sciences 363:1787–1794. Google Scholar


de Thoisy, B., Richard-Hansen, C., Goguillon, B., Joubert, P., Obstancias, J., Winterton, P., and Brosse, S., 2010. Rapid evaluation of threats to biodiversity: human footprint score and large vertebrate species responses in French Guiana. Biodiversity and Conservation 19:1567–1584. Google Scholar


Ancrenaz, M., Calaque, R., and Lackman-Ancrenaz, I., 2004. Orangutan nesting Behavior in disturbed forest of Sabah, Malaysia: Implications for nest census. International Journal of Primatology 25:983–1000. Google Scholar


Ernst, R., Linsenmair, K. E., and Rodel, M. O., 2006. Diversity erosion beyond the species level: Dramatic loss of functional diversity after selective logging in two tropical amphibian communities. Biological Conservation 133:143–155. Google Scholar


Lewis, O. T., 2001. Effect of experimental selective logging on tropical butterflies. Conservation Biology 15:389–400. Google Scholar


Pinto, A. C. B., Azevedo-Ramos, C., and de Carvalho,O.Jr. 2003. Activity patterns and diet of the howler monkey Alouatta belzebul in areas of logged and unlogged forest in eastern Amazonia. Animal Biodiversity and Conservation 26:39 Google Scholar


Sekercioglu, C. H., 2002. Effects of forestry practices on vegetation structure and bird community of Kibale National Park, Uganda. Biological Conservation 107:229–240. Google Scholar


Bicknell, J., and Peres, C. A., 2010. Vertebrate population responses to reduced-impact logging in a neotropical forest. Forest Ecology and Management 259:2267–2275. Google Scholar


Clark, C. J., Poulsen, J. R., Malonga, R., and Elkan, P. W., 2009. Logging Concessions Can Extend the Conservation Estate for Central African Tropical Forests. Conservation Biology 23:1281–1293. Google Scholar


Chapman, C. A., Balcomb, S. R., Gillespie, T. R., Skorupa, J. P., and Struhsaker, T. T., 2000. Long-Term Effects of Logging on African Primate Communities: a 28-Year Comparison From Kibale National Park, Uganda. Conservation Biology 14:207–217. Google Scholar


Dias, M. S., Magnusson, W. E., and Zuanon, J., 2010. Effects of Reduced-Impact Logging on Fish Assemblages in Central Amazonia. Conservation Biology 24:278–286. Google Scholar


Owiunji, I., 2000. Changes in avian communities of Budongo Forest Reserve after 70 years of selective logging. Ostrich 71:216–219. Google Scholar


Whitman, A. A., Hagan, J. M., and Brokaw, N. V. L., 1998. Effects of selection logging on birds in northern belize. Biotropica 30:449–457. Google Scholar


Johns, A. D., 1986. Effects of selective logging on the behavioral ecology of west malaysian primates. Ecology 67:684–694. Google Scholar


Kohler, P., Reinhard, K., and Huth, A., 2002. Simulating anthropogenic impacts to bird communities in tropical rain forests. Biological Conservation 108:35–47. Google Scholar


Potts, K. B., 2011. The Long-term Impact of Timber Harvesting on the Resource Base of Chimpanzees in Kibale National Park, Uganda. Biotropica 43:256–264. Google Scholar


Samejima, H., Ong, R., Lagan, P., and Kitayama, K., 2012. Camera-trapping rates of mammals and birds in a Bornean tropical rainforest under sustainable forest management. Forest Ecology and Management 270:248–256. Google Scholar


Peres, C. A., Gardner, T. A., Barlow, J., Zuanon, J., Michalski, F., Lees, A. C., Vieira, I. C. G., Moreira, F. M. S., and Feeley, K. J., 2010. Biodiversity conservation in human-modified Amazonian forest landscapes. Biological Conservation 143:2314–2327. Google Scholar


Laurance, W. F., and Useche, D. C., 2009. Environmental Synergisms and Extinctions of Tropical Species. Conservation Biology 23:1427–1437. Google Scholar


Laurance, W. R., 2004. Rapid land-use change and its impacts on tropical biodiversity. In Ecosystems and Land Use Change. pp.189–199. American Geophysical Union, Washington. Google Scholar


Fredericksen, N. J., and Fredericksen, T. S., 2002. Terrestrial wildlife responses to logging and fire in a Bolivian tropical humid forest. Biodiversity and Conservation 11:27–38. Google Scholar


Peres, C. A., Barlow, J., and Laurance, W. F., 2006. Detecting anthropogenic disturbance in tropical forests. Trends in Ecology & Evolution 21:227–229. Google Scholar


Poulsen, J. R., Clark, C. J., Mavah, G., and Elkan, P. W., 2009. Bushmeat Supply and Consumption in a Tropical Logging Concession in Northern Congo. Conservation Biology 23:1597–1608. Google Scholar


Peres, C. A., and Lake, I. R., 2003. Extent of nontimber resource extraction in tropical forests: Accessibility to game vertebrates by hunters in the Amazon basin. Conservation Biology 17:521–535. Google Scholar


Peres, C. A., 2001. Synergistic effects of subsistence hunting and habitat fragmentation on Amazonian forest vertebrates. Conservation Biology 15:1490–1505. Google Scholar


Wilkie, D. S., Sidle, J. G., and Boundzanga, G. C., 1992. Mechanized logging, market hunting, and a Bank Loan in Congo. Conservation Biology 6:570–580. Google Scholar


Hamer, K. C., and Hill, J. K., 2000. Scale-dependent effects of habitat disturbance on species richness in tropical forests. Conservation Biology 14:1435–1 Google Scholar


Meijaard, E., Sheil, D., Nasi, R., and Stanley, S. A., 2006. Wildlife conservation in Bornean timber concessions. Ecology and Society 11:47. Google Scholar


Willott, S. J., Lim, D. C., Compton, S. G., and Sutton, S. L., 2000. Effects of selective logging on the butterflies of a Bornean rainforest. Conservation Biology 14:1055–1065. Google Scholar


Barlow, J., Peres, C. A., Henriques, L. M. P., Stouffer, P. C., and Wunderlee, J. M., 2006. The responses of understorey birds to forest fragmentation, logging and wildfires: An Amazonian synthesis. Biological Conservation 128:182–192. Google Scholar


Cleary, D. F. R., 2004. Assessing the use of butterflies as indicators of logging in Borneo at three taxonomic levels. Journal of Economic Entomology 97:429–435. Google Scholar


Johns, A. D., 1992. Vertebrate response to selective logging: implications for design of logging systems. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 335:437–442. Google Scholar


Jones, M. J., Marsden, S. J., and Linsley, M. D., 2003. Effects of habitat change and geographical variation on the bird communities of two Indonesian islands. Biodiversity and Conservation 12:1013–1032. Google Scholar


ESRI. 2006. ArcGis-version 9.2. Redlands, California, USA. Google Scholar


Azevedo-Ramos, C., de Carvalho, O., and do Amaral, B. D., 2006. Short-term effects of reduced-impact logging on eastern Amazon fauna. Forest Ecology and Management 232:26–35. Google Scholar


Shankar, R. T. R., and Sukumar, R., 2002. Responses of tropical rainforest birds to abandoned plantations, edges and logged forest in the Western Ghats, India. Animal Conservation 5:201–216. Google Scholar


Lambert, T. D., Malcolm, J. R., and Zimmerman, B. L., 2005. Effects of mahogany (Swietenia macrophylla) logging on small mammal communities, habitat structure, and seed predation in the southeastern Amazon Basin. Forest Ecology and Management 206:381–398. Google Scholar


Pereira, D., Santos, D., Vedoveto, M., Guimarães, J., and Veríssimo, A., 2010. Fatos Florestais da Amazônia 2010. Instituto do Homem e Meio Ambiente da Amazônia - IMAZON, Belém, Brasil. Google Scholar


Peres, C. A., 2011. Conservation in Sustainable-Use Tropical Forest Reserves. Conservation Biology 25:1124–1129. Google Scholar


Bandeira, R., Veríssimo, A., Coslovsky, Salo, Pereira, J., and Quintella, R., 2010. Potencial Econômico nas Florestas Estaduais da Calha Norte Madeira e Castanha-do-Brasil. Instituto do Homem e Meio Ambiente da Amazônia - IMAZON, Belém, Brasil. Google Scholar


ITTO. 2010. Annual Review and Assessment of the World Timber Situation 2010. International Tropical Timber Organization, Yokohama, Japan. Google Scholar


Frumhoff, P. C., 1995. Conserving Wildlife in Tropical Forest Managed for Timber: To provide a more viable complement to protected areas. Bioscience 45:456–464. Google Scholar


Dunn, R. R., 2004. Managing the tropical landscape: a comparison of the effects of logging and forest conversion to agriculture on ants, birds, and lepidoptera. Forest Ecology and Management 191:215–224. Google Scholar


Fimbel, R. A., 1997. Wildlife-logging interactions in tropical forests - Summary statement of a workshop hosted by WCS and BOLFOR in Santa Cruz, Bolivia 13-15 November, 1996. Discovery and Innovation 9:133–134. Google Scholar


Chouteau, P., 2004. The impacts of logging on the microhabitats used by two species of couas in the western forest of Madagascar. Comptes Rendus Biologies 327:1157–1170. Google Scholar


Wells, K., Kalko, E. K. V., Lakim, M. B., and Pfeiffer, M., 2008. Movement and ranging patterns of a tropical rat (Leopoldamys sabanus) in logged and unlogged rain forests. Journal of Mammalogy 89:712-. Google Scholar


Barlow, J., and Peres, C. A., 2004. Avifaunal responses to single and recurrent wildfires in Amazonian forests. Ecological Applications 14:1358–1373. Google Scholar


Berry, N. J., Phillips, O. L., Lewis, S. L., Hill, J. K., Edwards, D. P., Tawatao, N. B., Ahmad, N., Magintan, D., Khen, C. V., Maryati, M., Ong, R. C., and Hamer, K. C., 2010. The high value of logged tropical forests: lessons from northern Borneo. Biodiversity and Conservation 19:985–997. Google Scholar


Willott, S. J., 1999. The effects of selective logging on the distribution of moths in a Bornean rainforest. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 354:1783–1790. Google Scholar


Bernard, H., Fjeldsa, J., and Mohamed, M., 2009. A case study on the effects of disturbance and conversion of tropical lowland rain forest on the non-volant small mammals in north Borneo: Management implications. Mammal Study 34:85–96. Google Scholar


Johns, A. D., and Skorupa, J. P., 1987. Response of Rain-Forest Primates to Habitat Disturbance: A Review. International Journal of Primatology 8:157–191. Google Scholar


Johns, A. G., and Johns, B. G., 1995. Tropical forest primates and logging: Long-term coexistence? Oryx 29:205–211. Google Scholar


Plumptre, A. J., and Reynolds, V., 1994. The effect of selective logging on the primate populations in Budungo Forest Reserve, Uganda. Journal of Applied Ecology 31:631–641. Google Scholar


Henriques, L. M. P., HenriquesJ. M. W.Jr. Oren, D. C., and Willig, M. R., 2008. Effects of low impact selective logging on an understory bird community in the Tapajós National Forest, Pará, Brazil. Acta Amazonica 38:267–290. Google Scholar


Matthews, A., and Matthews, A., 2002. Distribution, population density, and status of sympatric cercopithecids in the Campo-Ma'an area, Southwestern Cameroon. Primates 43:155–168. Google Scholar


Clarke, F. M., Rostant, L. V., and Racey, P. A., 2005. Life after logging: post-logging recovery of a neotropical bat community. Journal of Applied Ecology 42:409–420. Google Scholar


Hill, J. K., Hamer, K. C., Lace, L. A., and Banham, W. M. T., 1995. Effects of selective logging on tropical forest butterflies on Buru, Indonesia. Journal of Applied Ecology 32:754–760. Google Scholar


Laurance, W. F., and Laurance, S. G. W., 1996. Responses of five arboreal marsupials to recent selective logging in tropical Australia. Biotropica 28:310–322. Google Scholar


Wijesinghe, M. R., and Brooke, M. D., 2005. Impact of habitat disturbance on the distribution of endemic species of small mammals and birds in a tropical rain forest in Sri Lanka. Journal of Tropical Ecology 21:661–668. Google Scholar


Peters, S. L., Malcolm, J. R., and Zimmerman, B. L., 2006. Effects of selective logging on bat communities in the southeastern Amazon. Conservation Biology 20:1410–1421. Google Scholar


Sist, P., and Ferreira, F. N., 2007. Sustainability of reduced-impact logging in the Eastern Amazon. Forest Ecology and Management 243:199–209. Google Scholar


Felton, A. M., Felton, A., Foley, W. J., and Lindenmayer, D. B., 2010. The role of timber tree species in the nutritional ecology of spider monkeys in a certified logging concession, Bolivia. Forest Ecology and Management 259:1642–1649. Google Scholar


Uhl, C., and Vieira, I. C. G., 1989. Ecological Impacts of selective Logging in the Brasilian Amazon: A case-study from the Paragominas Region of the State of Para. Biotropica 21:98–106. Google Scholar


Aleixo, A., 1999. Effects of selective logging on a bird community in the Brazilian Atlantic forest. Condor 101:537–548. Google Scholar


Azlan, J. M., and Sharma, D. S. K., 2003. Camera trapping the Indochinese tiger, Panthera tigris corbetti, in a secondary forest in Peninsular Malaysia. Raffles Bulletin of Zoology 51:421-. Google Scholar


Barclay, S., Ash, J. E., and Rowell, D. M., 2000. Environmental factors influencing the presence and abundance of a log-dwelling invertebrate, Euperipatoides rowelli (Onychophora: Peripatopsidae). Journal of Zoology 250:425–436. Google Scholar


Beck, J., Schulze, C. H., Linsenmair, K. E., and Fiedler, K., 2002. From forest to farmland: diversity of geometrid moths along two habitat gradients on Borneo. Journal of Tropical Ecology 18:33–51. Google Scholar


Bloemers, G. F., Hodda, M., Lambshead, P. J. D., Lawton, J. H., and Wanless, F. R., 1997. The effects of forest disturbance on diversity of tropical soil nematodes. Oecologia 111:575–582. Google Scholar


Castro-Arellano, I., Presley, S. J., Willig, M. R., Wunderle, J. M., and Saldanha, L. N., 2009. Reduced-impact logging and temporal activity of understorey bats in lowland Amazonia. Biological Conservation 142:2131–2139. Google Scholar


Chung, A. Y. C., Eggleton, P., Speight, M. R., Hammond, P. M., and Chey, V. K., 2000. The diversity of beetle assemblages in different habitat types in Sabah, Malaysia. Bulletin of Entomological Research 90:475–496. Google Scholar


Crome, F. H. J., Thomas, M. R., and Moore, L. A., 1996. A novel Bayesian approach to assessing impacts of rain forest logging. Ecological Applications 6:1104–1123. Google Scholar


Dale, S., Mork, K., Solvang, R., and Plumptre, A. J., 2000. Edge effects on the understory bird community in a logged forest in Uganda. Conservation Biology 14:265–276. Google Scholar


Ganzhorn, J. U., Ganzhorn, A. W., Abraham, J. P., Andriamanarivo, L., and Ramananjatovo, A., 1990. The Imapct of Selective Logging on Forest Structure and Tenrec Population in Western Madagascar. Oecologia 84:126–133. Google Scholar


Gormley, L. H. L., Furley, P. A., and Watt, A. D., 2007. Distribution of ground-dwelling beetles in fragmented tropical habitats. Journal of Insect Conservation 11:131–139. Google Scholar


Gunawardene, N. R., Majer, J. D., and Edirisinghe, J. P., 2010. Investigating residual effects of selective logging on ant species assemblages in Sinharaja Forest Reserve, Sri Lanka. Forest Ecology and Management 259:555–562. Google Scholar


Heydon, M. J., and Bulloh, P., 1996. The impact of selective logging on sympatric civet species in Borneo. Oryx 30:31–36. Google Scholar


Heydon, M. J., and Bulloh, P., 1997. Mousedeer densities in a tropical rainforest: The impact of selective logging. Journal of Applied Ecology 34:484–496. Google Scholar


Holloway, J. D., Kirkspriggs, A. H., and Khen, C. V., 1992. The response of some rain forest insect groups to logging and conversion to plantation. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 335:425–436. Google Scholar


Imron, M. A., Herzog, S., and Berger, U., 2011. The Influence of Agroforestry and Other Land-Use Types on the Persistence of a Sumatran Tiger (Panthera tigris sumatrae) Population: An Individual-Based Model Approach. Environmental Management 48:276–288. Google Scholar


Laidlaw, R. K., 2000. Effects of habitat disturbance and protected areas on mammals of peninsular Malaysia. Conservation Biology 14:1639–1648. Google Scholar


Lima, A. P., Suarez, F. I. O., and Higuchi, N., 2001. The effects of selective logging on the lizards Kentropyx calcarata, Ameiva ameiva and Mabuya nigropunctata. Amphibia-Reptilia 22:209–216. Google Scholar


Mason, D., 1996. Responses of venezuelan understory birds to selective logging, enrichment strips, and vine cutting. Biotropica 28:296–309. Google Scholar


Ochoa, J., 2000. Effects of loggin on small-mammal diversity in the lowland forests of the Venezuelan Guyana region. Biotropica 32:146–164. Google Scholar


Pearman, P. B., 1997. Correlates of amphibian diversity in an altered landscape of Amazonian Ecuador. Conservation Biology 11:1211–1 Google Scholar


Pessoa, F. A. C., Medeiros, J. F., and Barrett, T. V., 2007. Effects of timber harvest on phlebotomine sand flies (Diptera : Psychodidae) in a production forest: abundance of species on tree trunks and prevalence of trypanosomatids. Memorias Do Instituto Oswaldo Cruz 102:593–599. Google Scholar


Remis, M. J., and Kpanou, J. B., 2011. Primate and ungulate abundance in response to multi-use zoning and human extractive activities in a Central African Reserve. African Journal of Ecology 49:70–80. Google Scholar


Rossi, J. P., and Blanchart, E., 2005. Seasonal and land-use induced variations of soil macrofauna composition in the Western Ghats, southern India. Soil Biology & Biochemistry 37:1093–1104. Google Scholar


Schulz, A., and Wagner, T., 2002. Influence of forest type and tree species on canopy ants (Hymenoptera : Formicidae) in Budongo Forest, Uganda. Oecologia 133:224–232. Google Scholar


Vasconcelos, H. L., Vilhena, J. M. S., and Caliri, G. J. A., 2000. Responses of ants to selective logging of a central Amazonian forest. Journal of Applied Ecology 37:508–514. Google Scholar


Waltert, M., Lien, Faber, K., and Muhlenberg, M., 2002. Further declines of threatened primates in the Korup Project Area, south-west Cameroon. Oryx 36:257–265. Google Scholar


Weisenseel, K., Chapman, C. A., and Chapman, L. J., 1993. Nocturnal Primates of Kilabe Forest: Effectes of Selective Logging on Prosimian Densities. Primates 34:445–450. Google Scholar


Appendix 1.

List of reviewed selective logging studies in Tropical Forest. In Forest management column the “np” means that the information was not reported. Continents are: LA – Latin America, AS – Asia, AF – Africa, OC – Oceania.

© 2013 Juliana Laufer, Fernanda Michalski and Carlos A. Peres. This is an open access paper. We use the Creative Commons Attribution 3.0 license - The license permits any user to download, print out, extract, archive, and distribute the article, so long as appropriate credit is given to the authors and source of the work. The license ensures that the published article will be as widely available as possible and that the article can be included in any scientific archive. Open Access authors retain the copyrights of their papers. Open access is a property of individual works, not necessarily journals or publishers.
Juliana Laufer, Fernanda Michalski, and Carlos A. Peres "Assessing sampling biases in logging impact studies in tropical forests," Tropical Conservation Science 6(1), 16-34, (18 March 2013).
Received: 26 October 2012; Accepted: 12 December 2012; Published: 18 March 2013

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