Effects of Landscape Attributes on Medium- and Large Terrestrial Non-Volant Mammals: A Systematic Review of Camera Trap Studies (2010--2023)

Effects of Landscape Attributes on Medium- and Large Terrestrial Non-Volant Mammals: A Systematic Review of Camera Trap Studies (2010--2023) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Effects of Landscape Attributes on Medium- and Large Terrestrial Non-Volant Mammals: A Systematic Review of Camera Trap Studies (2010--2023) Jenner Rodas-Trejo, Sergio López Mendoza, Cesar Tejeda Cruz, Jaime R. Rau, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6116754/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Terrestrial landscapes are undergoing unprecedented transformations due to human activities, resulting in habitat loss, degradation, and fragmentation on a global scale. This has severe effects on wildlife, especially on medium- and large-sized terrestrial mammals. Landscape ecology seeks to understand how habitat configuration, quantity, quality, and connectivity impact wildlife populations. This article presents a meta-analysis exploring the effects of landscape attributes and habitat fragmentation on populations of medium- and large-sized terrestrial mammals, highlighting the role of landscape ecology in biodiversity conservation. A total of 180 articles published between 2010 and 2023 were analyzed, selected from scientific databases. Patterns were evaluated in terms of geographic coverage, research topics, response variables, land use, and landscape metrics applied. Most studies were conducted in the Americas, Asia, and Africa, focusing on tropical and subtropical biomes. Of these, 68.89% centered on mammal communities in general. The most frequently studied response variables were species richness (28.45%), occupancy (25.63%), and abundance (12.39%). The most commonly used landscape metrics were related to human disturbances and habitat quantity. Studies were mainly conducted in native forests (77.17%) and areas with agricultural activities (42.39%). This review highlights the growing importance of camera traps in mammalian research and the need to understand landscape effects on their conservation. Species were observed to respond differently to landscape transformation, with some exhibiting ecological flexibility and others experiencing negative impacts. Animal Science Camera traps terrestrial mammals habitat fragmentation landscape ecology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Landscapes are heterogeneous physical spaces whose characteristics result from the interaction of biophysical and socioeconomic components (Clark 2010 ). In these spaces, the arrangement of elements and landscape structure is determined primarily by natural changes and those caused by human activities (Cushman et al. 2010 , Kühne and Antrop 2015 ). Currently, owing to human activities, terrestrial landscapes are experiencing unprecedented transformations, leading to habitat loss, degradation, and fragmentation on a global scale (Prugh et al. 2008 , Schipper et al. 2008 , Thornton et al. 2011 ). These landscape alterations have severe effects on wildlife, and their survival largely depends on factors such as landscape composition, the availability of habitat resources within the landscape structure, and species adaptability and resilience to new habitat matrices (Saura and Pascual-Hortal 2007 ; Brady et al. 2011 ; Schooley and Branch 2011 ; With 2019 ). Understanding how habitat configuration, quantity, quality, and connectivity influence wildlife populations has become a central research theme in landscape ecology (Saura 2020 ). Studies on the effects of landscape on wildlife employ a multilevel approach that examines both the landscape scale (amount and type of habitat, human pressure) and the patch scale (vegetation structure, size, and shape) to understand how habitat amount, structural complexity, and spatial configuration—particularly fragmentation and connectivity—affect various aspects of species, such as their presence, richness, abundance, and distribution (Fahrig 2003 , 2013 ; Hanski 2015 ; Keinath et al. 2016 ; Martin 2018 ; Gardiner et al. 2018 ). Medium- and large-sized terrestrial mammals are particularly vulnerable to fragmentation due to their space requirements, mobility, and dependence on well-preserved habitats. Many studies have aimed to assess the impacts of landscape modification on species' distribution, activity patterns, reproduction, survival, and habitat use. (Prugh et al. 2008 , Brady et al. 2011 , Crooks et al. 2017 , Zungu et al. 2020 ). These studies have provided insights into the effects of fragmentation and identified the attributes with the greatest impact on mammals, generating valuable information to guide area management and achieve conservation objectives (Cushman and McGarigal 2005). In recent years, research on the effects of landscape and fragmentation on medium and large terrestrial mammals has advanced significantly, largely owing to the use of camera traps as a methodological tool. This has allowed data collection at different scales, both spatially and temporally, providing detailed information on the distribution, behavior, and population dynamics of mammals in fragmented habitats (Caravaggi et al. 2017 ; Cruz et al. 2018 ; Rocha et al. 2018 ). Additionally, advances in analytical approaches, such as occupancy models and the application of more advanced numerical analysis, have allowed researchers to evaluate the effects of the landscape on species beyond traditional presence‒absence methods (With 2019 ; Mandujano et al. 2023 ). As tools and approaches continue to evolve, a comprehensive synthesis that integrates information gathered using camera trap is needed. This review not only provides an up-to-date summary of the knowledge acquired from 2010–2023 but also helps systematize emerging patterns, identify key variables, and enhance the understanding of how landscape attributes influence medium and large terrestrial mammals at various spatial scales and ecological regions. This synthesis also highlights emerging trends in the use of technologies such as camera traps and other methodological approaches. This review identifies patterns in geographic coverage, research topics, response variables, land use, and landscape metrics, while evaluating the effectiveness of camera traps and the impact of human intervention at landscape level. It helps to identify priority areas for future research and guides more effective conservation strategies to tackle fragmentation and landscape transformation. Materials and methods A search for articles published from 2010–2023 was conducted via the EBSCO, Google Scholar, ScienceDirect, and Web of Science databases, along with artificial intelligence (AI) enhanced tools such as ResearchRabbit and Semantic Scholar. We limited our search to peer-reviewed articles in academic journals which were available in English. Three key search elements were combined: (1) the study base with the words “landscape,” “land use,” “landscape change,” “fragmentation,” “forest fragments,” “habitat,” “habitat use,” and “habitat loss”; (2) the study object with the words “mammal,” “terrestrial mammal,” “medium and large mammals”; and (3) the data collection method with the words “camera trap,” “camera trapping,” and “infrared triggered camera.” Publications that studied communities, populations, and species of medium- and large-sized terrestrial mammals at the following levels were selected: a) Analysis of occupancy, diversity, richness, composition, community structure, populations, and species of medium and large terrestrial mammals in conserved, deforested, fragmented, disturbed (fires, agriculture, livestock, forestry plantations, among others), urbanized environments, and/or biological corridors. b) Use, rejection, and/or preference of the landscape by communities, populations, and species of medium and large terrestrial mammals in conserved, fragmented, disturbed, urbanized environments and/or biological corridors. c) Effects of landscape, fragment, and/or habitat attributes on communities, populations, and species of medium and large terrestrial mammals. Articles that did not consider studies on landscape and/or fragmentation were excluded, as many reported diversity, new species records, activity patterns, inventories, resource selection, and diet. The articles were systematized in an Excel database with information divided into three groups and 10 categories following McCallum ( 2012 ), Burton et al. ( 2015 ), Correa (2016), Keinath et al. ( 2016 ), Agha et al. ( 2018 ), and Delisle et al. ( 2021 ) (Table 1 ). Table 1 Groups and categories included in the analysis of the articles. Groups Categories General Information and Content Journal and year of publication. Countries, continents, and biomes where the research was conducted. Scale of the study: local, national (> 3 provinces of a country), transnational (> 2 countries within the same continent), intercontinental (> 2 continents). Research topics Study Design and Approach Object of study. Level at which the study was conducted: landscape, fragmentation, landscape-fragmentation. Analysis Metrics and Variables Response variables used. Landscape metrics, fragmentation metrics, and detection covariates integrated into the analysis. Number of detected species reported. Land use where the studies were conducted. Work inside or outside Protected Natural Areas. Group 1: General Information and Content: The articles were classified by year, the journals in which they were published, and spatial scale (the continents, countries, and biomes where the research was conducted), consulting the Ecoregions2017©Resolve site ( https://ecoregions.appspot.com ). The study scale was determined as a) local (those studies that covered only one region), b) national (those that considered at least three provinces or regions of a country), c) transnational (those that covered more than two countries on the same continent), and d) intercontinental (those that conducted studies on more than one continent). To group publications into research topics, an analysis of the hypotheses and objectives of each article was conducted and classified as follows: 1) climate change; 2) distribution and occurrence of communities, populations, and species of medium and large terrestrial mammals in different environments, vegetation types, and land use; 3) effect of habitat, landscape, and fragment characteristics on communities, populations, and species of medium and/or large terrestrial mammals; 4) effects of anthropogenic activities on communities, populations, and species of medium and large terrestrial mammals in different environments; 5) current status of communities, populations, and species of medium and large terrestrial mammals in different environments, habitat conditions, and protected natural areas (PNA); 6) factors influencing the occupancy of communities, populations, and species in conserved and/or fragmented environments; 7) activity patterns of species in different environments, habitats, landscapes, and/or fragments; and 8) connectivity and use of biological corridors. Each article could contain one or more research topics. Group 2: Study Design and Approach: To characterize the study objects, the subjects of the studies were considered, and the records were grouped according to whether they were reported as focal species when the studies focused on a particular species, multiple species when all recorded species were considered, and trophic guilds when the study was directed at a particular guild (Agha et al. 2018 ). The study level was classified as a) landscape for studies analyzing landscape-level elements, b) fragments for studies focusing specifically on remans elements, and c) landscape fragmentation for those conducting studies at both levels. Group 3: Analysis Metrics and Variables Considering ecological objectives, response metrics were classified into eight variables: abundance (primarily considering the relative abundance index (RAI)), activity patterns, community structure, density, diversity, occupancy, detection probability, and species richness (Burton et al. 2015 , Kays et al. 2020 , Delisle et al. 2021 ). Detection covariates, landscape metrics, and fragmentation metrics were classified into 23 subcategories, focusing on a set of metrics widely addressed in the literature (Botequilha Leitão and Ahern 2002 , Uuemaa et al. 2009 , Uuemaa et al. 2013 , Keinath et al. 2016 ) (see Table 2 ). Table 2 Classification of landscape metrics, fragments and detection covariates included in the analyses of the articles. Clasificación Code Category Description Human disturbance Hum_dist Landscape metrics Elements of the landscape that impact or have been impacted by human activities such as tourism, hunting, human settlements (Li et al. 2022 ). Hábitat amount Hab_amo Landscape metrics Proportion of primary and secondary forest. The hypothesis predicts that species richness at a habitat site increases with the amount of habitat in the "local landscape" defined by an appropriate distance around the site, without distinctive effects of the habitat patch size in which the site is located (Fahrig 2013 , Saura 2020 ). Abiotic factors Abi_fact Landscape metrics Abiotic factors involved in the characterization of a given ecosystem, such as altitude, topography, slope, among others (Nakashima et al. 2020 ). Matrix the Patch Mat_P Patch metrics Indicates the types of habitat of the patches included in a study. Patches refer to preserved vegetation or secondary forest (Keinath et al. 2016 ). Spatial configuration of the landscape Spa_confL Landscape metrics Elements that constitute the quantitative landscape (Parsons et al. 2016 ). Landscape impact Land_imp Landscape metrics Proportion of areas where human and natural activities occur that impact the landscape (Cruz et al. 2018 ). Hábitat type Hab_ty Landscape metrics Indicates the main habitat type included in a study, referring to preserved vegetation or secondary forest (Keinath et al. 2016 ). Matrix type Mat_ty Landscape metrics Represents the primary driver of fragmentation in a study. The categories are urban, agriculture, livestock, and seminatural (e.g., burning, flooding) (Garmendia et al. 2013 , Keinath et al. 2016 ). Landscape size Land_siz Landscape metrics Composition and size of each landscape element. Interspecific interactions Intr_rel Detection covariates Directly or indirectly examine interspecific interactions (e.g., predator‒prey, competition, mutualism) (Cruz et al. 2018 , Delisle et al. 2021 ). Isolation Iso Patch metrics Distance to the nearest forest patch (Garmendia et al. 2013 ). Isolation is due to landscape resistance exerted on the remaining habitats by the surrounding nonhabitat matrix (Botequilha et al. 2002). Camera Camera Detection covariates Evaluates the success of capture due to the different operational capabilities and characteristics of camera models (Penjor et al. 2021 ). Patch area Area_P Patch metrics Represents the contiguous area of a remaining habitat patch, measured in hectares (Keinath et al. 2016 ). Protected areas PA Landscape metrics Considered public property and management land, protected from private urbanization (size of the protected area, type of protected area, etc.) (Massara et al. 2017 , Chen et al. 2022 ). Domestic mammals Dom_mam Detection covariates Presence of domestic animals in the study area (Parsons et al. 2016 , Massara et al. 2017 ). Basal area of trees/saplings Basal_ar Patch metrics Basal area of small trees/sprouts within the patch estimated per hectare, applies to lianas, shrubs, etc. (Thornton et al. 2011 ). Intraspecific characteristics Intr_char Detection covariates Species characteristics such as reproductive rate, home range, body mass. Shape Shape Patch metrics Habitat fragmentation measured by the patch shape index, explains an increase in the length of the habitat patch edge; more circular areas have less edge effect (Garmendia et al. 2013 ). Number of patches Num_P Landscape metrics Indicates the number of habitat patches evaluated within a study (Keinath et al. 2016 ). Density of forest edges Dens_forEd Landscape metrics Total density of forest - nonforest edge in the landscape (km/ha) (Thornton et al. 2011 ). Density of forest patches Dens_forP Landscape metrics Density of forest patches in the landscape (no./km²) (Thornton et al., 2011 , Pfeifer et al. 2017 ). Time since fragmentation or disturb Time_dist Landscape metrics Time elapsed since the loss of primary or secondary forest (Semper-Pascual et al. 2021 ). Mean patch size Mean_sizP Landscape metrics Average size of all forest patches in the landscape (ha) (Thornton et al. 2011 ). For the number of species, articles focusing on multispecies studies were considered, excluding those focused on trophic guilds or focal species, and these were grouped by continent. These variables were calculated only in local-level studies, and the minimum, maximum, standard deviation, and arithmetic means were reported (Green et al. 2020 ). To quantify land use, the characteristics of the vegetation types and anthropogenic activities described at the study sites were considered. The vegetation types were grouped into 1) Native forests, 2) Degraded forests, 3) Wetlands and riparian areas, 4) Native grasslands, 5) Secondary forests, and 6) Shrub vegetation. Human activities were summarized as follows: 7) agriculture, 8) monoculture plantations, 9) introduced pastures for livestock, 10) urban and developed areas, 11) livestock, and 12) mining and energy. The studies conducted inside and outside protected natural areas were counted. A chi-square analysis was performed to determine if there were significant differences between them. The number of species reported in the studies by continent was counted, and the means and standard deviations were obtained. Results General Information and Content The search yielded 314 scientific publications, 180 of which met the inclusion criteria and were included in this study. Of these, 66.11% (n = 119) were published in the past six years (Fig. 1 ). The reviewed articles were published in 73 scientific journals, with ten accounting for 46.67% (n = 84) of the works. Of these, 7.77% were published in Biological Conservation (n = 14), 6.66% in PLOS ONE (n = 12), 6.11% (n = 11) in Mammalian Biology, and 5.56% (n = 10) in Oryx; in 44 journals, only one article was found. The studies were conducted across five continents (114 in America, 30 in Asia, 24 in Africa, seven in Europe, and three in Oceania) and in 54 countries (16 in America, 16 in Africa, 15 in Asia, six in Europe, and one in Oceania), with five countries accounting for 50.00% (n = 89). The six countries with the highest contributions were Brazil, with 38 studies; Argentina, with 17; the United States, with 14; and Malaysia and Mexico, with 10 each (Fig. 2 ). The most common scale of the studies was local, focusing on a single region within a country (95.56%; n = 172); four studies were national (2.22%); four were transnational, two were in America and one was in Asia; and three were continental, encompassing America, Europe, Asia, and Africa (2.22%). Thirteen biomes (Olson et al. 2001 ) were represented, with Tropical and Subtropical Moist Broadleaf Forests accounting for 47.16% (n = 83), Tropical and Subtropical Grasslands, Savannas, and Shrublands accounting for 17.06% (n = 30), Temperate Broadleaf and Mixed Forests accounting for 12.50% (n = 22), and Mediterranean Forests, Woodlands, and Scrub accounting for 5.11% (n = 9). Research topics were included 272 times, with the topics “Effects of anthropogenic activities on populations of medium and large terrestrial mammals in different environments” and “Effects of habitat, landscape, and fragment characteristics on populations of medium and large mammals” identified in 67 articles (24.63%) and 61 publications (22.43%), respectively. The “Distribution and occurrence of populations of medium and large terrestrial mammals in different environments, vegetation types, and land use” was the third most recorded topic (20.59%; n = 56); the topic “Current status of communities and/or populations of medium and large terrestrial mammals in different environments, habitat conditions, and protected natural areas (PNAs)” was the fourth most common topic (10.66%; n = 29); and the topic “climate change” was recorded in only 1.47% (n = 4) of the publications (see Fig. 3 ). Study Design and Approach Regarding species/community approach, 68.89% (n = 124) of the studies focused on the mammal community as a whole, while 15.00% (n = 27) targeted specific trophic guilds, primarily carnivores and herbivores. Additionally, 29 studies (16.11%) examined 40 individual species, with Panthera onca, Vulpes vulpes, and Puma concolor being the most frequently studied, each appearing in three studies. Camera traps were the predominant sampling method, employed exclusively in 90.00% (n = 162) of the studies, followed by a combination of camera traps and track searches in 7.22% (n = 13). Other methods, such as interviews, direct observations, and DNA sampling, were used in 2.77% (n = 5) of the studies. Research was conducted primarily at the landscape level (n = 140; 77.77%), with fewer studies focusing on the fragment level (n = 28; 15.56%) and the landscape fragmentation level (n = 12; 6.67%). Analysis Metrics and Variables The response variables were presented 355 times in the articles and focused primarily on two of the eight response variables, species richness (28.45%, n = 101) and occupancy (25.63%, n = 91), which accounted for the highest number of records and have shown an increasing trend since 2016, followed by the abundance variable (12.39%, n = 44). The fourth response variable was community structure (9.86%, n = 35), whereas detection probability, diversity, activity patterns, and density were the variables with the lowest records, with values of 9.01% (n = 32), 7.89% (n = 28), 5.35% (n = 19), and 1.41% (n = 5), respectively (Fig. 4 ). Landscape, fragmentation metrics, and detection covariates were integrated into the studies' analyses 808 times. Landscape metrics appeared 435 times, with "human disturbance" covariates, which include human activities, distances to settlements, population density, and hunting pressure, being mentioned 152 times, and "habitat amount", which includes the proportions of conserved and secondary vegetation cover, was mentioned 87 times (Fig. 5 a). Patch metrics appeared in 202 instances, with the most important covariate being the "Matrix the patch," which includes counts of palms, trees, and shrubs, among others, with 101 mentions, followed by "Isolation", with 33 (Fig. 5 a). The detection covariates appeared 153 times, with "Abiotic factors," which include altitude, temperature, precipitation, and season of the year, among others, mentioned 92 times, followed by “Interspecific relationships,” which integrates the frequency of appearance and abundance of mammals (Fig. 5 a). Land use was defined based on the described location where the camera traps were placed to detect species, considering human activities and vegetation types. The studies focused on Native Forest (n = 142, 77.17%), which encompasses vegetation in its original state, followed by Grasslands (n = 86, 46.74%), Wetlands and Riparian Forests (n = 26, 14.13%), and Secondary Forests (n = 20, 10.87%) (Fig. 5 b). Human activities included sites with agriculture (n = 78, 42.39%), in particular maize and rice cultivation, followed by monoculture plantations (n = 44, 23.91%), such as oil palm and forestry plantations such as pine and eucalyptus, and cattle pastures (n = 30, 16.30%) (Fig. 5 b). Africa had the highest average number of detected species at 26.16 ± 12.74, with a maximum of 44 and a minimum of 5. Asia was the second continent with 23.26 ± 16.26, with a maximum of 61 and a minimum of 3, followed by America with 17.29 ± 10.87, a maximum of 40 and a minimum of 3; Europe with 8.50 ± 3.32, with a maximum of 12 and a minimum of 4; and Oceania with 17.50 ± 7.78, with a maximum of 23 and a minimum of 12. The published studies that conducted sampling within PNA accounted for 55.56% (n = 100), with no significant differences found compared with those conducted outside them (Χ²=2.222, df = 1, p value = 0.136). Discussion General Information This review presents an overview of published articles on the effects of landscape and fragmentation on populations of medium- and large terrestrial mammals, using camera traps as a sampling method. The analysis includes information on research topics, response variables, land use, and the most evaluated landscape and fragmentation metrics; geographic coverage; species and guild focus; and study design details. This review emphasizes the importance of camera traps as a commonly used and vital method for assessing how habitat variables and the structure of natural and anthropogenic landscapes influence the status, composition, distribution, and activity patterns of medium and large terrestrial mammal populations and communities (Agha et al. 2018 , Piña et al. 2019 , Chen et al. 2022 ). The results highlight an annual growth in publications over the last six years, indicating a growing research interest in studying mammal populations and communities and their relationships with natural and anthropogenic landscapes. This growth is largely driven by technological innovations and reductions in camera trap costs, methodological advancements, the advent of open-source statistical software packages and geographic information systems (GIS), the availability of satellite imagery, and land-use cartographic information that facilitates more detailed landscape analyses (Burton et al. 2015 , Rose et al. 2014 , Correa et al. 2016 , Steenweg et al. 2016 , Mandujano 2019 ). The studies were published in many journals, providing researchers with broad opportunities to disseminate knowledge and indicating that there is interest from specialized journals in various disciplines of environmental sciences, such as biological conservation, conservation biology, animal ecology, landscape ecology, and evolution, in these types of studies. Content In terms of the number of articles per continent and country, America, specifically South America (Brazil and Argentina), stands out for its scientific output. This contrasts with findings by McCallum ( 2012 ), Uuemaa et al. ( 2013 ), and Correa et al. ( 2016 ) that North America has produced the most research, both to evaluate camera traps as a data collection tool and to understand trends in the use of landscape spatial metrics. The increase in publications in South America may be due to the reduction in equipment costs, access to information, and the formation of research networks. Delisle et al. ( 2021 ), in a study on trends in camera trap applications by thematic composition, taxonomy, and geography, reported the highest frequency of studies in South America, North America, Asia, and southern Africa, whereas the Middle East, western Australia, and northern Africa presented the lowest number of published articles, which aligns with the results of this study. The preferred study environments identified were tropical and subtropical biomes, specifically Tropical and Subtropical Moist Broadleaf Forests and Tropical and Subtropical Grasslands, Savannas and Shrublands, which are found in North, Central, and South America; Central and Southern Africa; and Southern Asia. This preference is likely due to the high levels of endemism, habitat complexity, and mammalian richness in these biomes, as well as the significant threats they face from land-use changes due to human activities and climate change (Olson & Dinerstein 2002 , Ferrer-Paris et al. 2019 , Hoang & Kanemoto 2021 ). Therefore, it is imperative to continue generating knowledge on the dynamics and adaptations of terrestrial mammal populations in changing landscapes. Although some studies have conducted analyses across multiple continents and countries, most have focused on local studies, allowing for finer-scale analyses of landscapes and their components, since habitat disturbances generally occur at local levels; thus, generalizable patterns derived from coarse-scale metrics may not be applicable at finer scales (Lamine et al. 2018 ). The transformation, loss, and fragmentation of natural habitats by human activities are considered the greatest threats to biodiversity and significant causes of species extinction (Munguia et al. 2016, Zungu et al. 2020 ). Therefore, one of the main conservation challenges is understanding how wild species respond to the landscape matrix and land-use changes to ensure their survival over time and space (Bender et al. 1998 , Schipper et al. 2008 , Brady et al. 2011 ). In this context, it is not surprising that the main objectives addressed in the studies focused on evaluating the "Effects of anthropogenic activities on populations of medium and large terrestrial mammals in different environments," the "Effect of habitat, landscape, and fragment characteristics on populations of medium and large mammals," and the "Distribution and occurrence of populations of medium and large terrestrial mammals in different environments, vegetation types, and land use," seeking to understand how these factors influence variables such as richness, occupancy, abundance, diversity, and activity of mammals. Study Design and Approach One of the main advantages of camera traps is their ability to collect data that can be used to address multiple questions for multiple species (Tanwar et al. 2021 ). The ability of camera traps to generate information on a wide spectrum of species in numerous habitats simultaneously allows the acquisition of robust data that provide insights into variables of interest such as species richness, diversity, occupancy, resource selection, habitat use, and activity patterns in the landscape, whether for focal species or species groups (Rovero et al. 2013 , Sunarto & Kelly 2013 , Burton et al. 2015 , Caravaggi et al. 2017 , Mandujano 2019 ). In this review, the 68.89% (n = 124) of the articles focused on multispecies studies aimed at protecting the biodiversity and communities of entire mammals, while some studies targeted groups, mainly carnivores, and fewer focused on specific species. Conservation efforts targeting multiple species are generally more efficient than single-species strategies, where the objective is to maintain biodiversity and ecosystem functions (Carroll et al. 2003 ). However, it is essential to consider that generalizing may be less effective in conserving a particular species or group than a specifically designed strategy, as the ability to protect viable populations can be achieved only through detailed population analyses. Therefore, it is crucial to consider conservation objectives, habitat selection, and whether the impact of human-induced stress factors varies significantly among species (Early & Thomas 2007 , Brodie et al. 2015). Most focal species in the reviewed articles were indicator species, such as predators, mesopredators, and large mammals, which aligns with the findings of Correa et al. ( 2016 ), who noted that these species are selected because of their extensive habitat area requirements, relatively low population densities, charismatic nature, and significant susceptibility to human influence; thus, their conservation benefits other species and habitats (Green et al. 2020 ). The exclusive use of camera traps as the sole sampling method in 90% of the publications highlights the widespread adoption of this technique, as demonstrated in previous reviews documenting the adoption and growth of camera trap use in wildlife research (Rovero et al. 2013 , Delisle et al. 2021 ). Analysis Metrics and Variables In a review for the estimation of abundance in unmarked animals, Gilbert et al. ( 2020 ) reported that the main variables were relative abundance indices (RAI), followed by occupancy and, finally, species richness. In this review, most studies focused on estimating species richness, occupancy, and abundance variables. The analysis of these variables provides a basis for detecting population changes over time and space, thereby allowing for the testing of hypotheses about how wildlife mammal communities and populations respond to landscape modifications and human influence (Kays et al., 2020 ). The concept of species richness, represented as the number of species in a given area and time period, is the most frequently employed measure of biodiversity, allowing for inferences about community structure. However, its measurement in extensive regions or with diverse taxa requires significant investment in sampling effort (López-Gómez et al. 2006). Unlike our results, McCallum ( 2012 ), in a review of the use of camera traps in habitats, taxa, and study types, reported that studies on species richness constituted only 10%, although a growing trend was noted, similar to the trend observed by Burton et al. ( 2015 ), which might be due to economic and rapid results. The metric was primarily used to compare community composition between sites impacted by human activities versus conserved sites or those under some conservation scheme and was estimated conjointly with occupancy and, second, with species abundance measurements. Studies estimating occupancy have experienced growth associated with theoretical advancements in models for their implementation (Broadley et al. 2019 , Delisle et al. 2021 ), as reflected in the review, with an increase since 2015. Occupancy refers to the proportion of area, fragments, or sites occupied by a species, accounting for the detection probability over the estimates of species presence (MacKenzie et al. 2018 ). The results allowed inference of habitat use, landscape effects, and disturbances (e.g., logging, agriculture, livestock) at the level of a single species or community. Notably, one of the research topics identified in the articles was the estimation of factors influencing species occupancy in conserved and/or fragmented environments, with the goal of validating occupancy models. The detection probability was presented as a response variable in 32 studies (17.78%), reflecting the increasing methodological sophistication in the field. This variable is essential for correcting inherent biases in occupancy and abundance studies, especially when camera traps are used (Mandujano 2024 ). The importance of detection probability lies in its ability to distinguish between the true absence of a species and nondetection due to methodological or environmental factors (MacKenzie & Kendall 2002 ). In our review, we observed that researchers frequently incorporated this variable into their occupancy and abundance models, indicating a widespread awareness of the importance of addressing imperfect detectability in wildlife studies. The increased use of detection probability as a response variable can be attributed to the development of more robust analytical frameworks, such as hierarchical occupancy and abundance models (Royle & Dorazio 2008 ). These models allow researchers to separate ecological processes (e.g., true occupancy or abundance) from observation processes (the probability of detecting a species when present). This separation is crucial for obtaining unbiased estimates of the parameters of interest and understanding how landscape factors affect not only species presence, but also our ability to detect them. While camera traps have made significant technological advancements, one of their main limitations is the inability to identify unmarked individuals, making it difficult to ascertain whether multiple detections involve different individuals or the same one. This limitation complicates abundance estimation (Moeller et al. 2018 , Gilbert et al. 2020 ). Because of this methodological constraint, Burton et al. ( 2015 ) note that many researchers opt to estimate occupancy models or relative abundance indices (RAI). In the reviewed articles, abundance referred primarily to the RAI calculation per species, which indicates the number of detections per 100 traps/days, assuming a positive linear relationship with local real abundance (Chen et al. 2022 ). However, it is important to note that this index may be biased by variability in detectability, meaning that it does not always accurately reflect the real abundance of the studied species (Mandujano 2024 ). The RAI results were complemented with information on habitat preferences and landscape influences on medium and large terrestrial mammal populations and were used to infer differences in abundance between sites or fragments. In terms of community structure, this response variable provides a more comprehensive view of how landscape attributes affect mammal communities as a whole beyond the specific responses of individual species. Our analysis revealed that researchers used this variable to examine how changes in the landscape alter species interactions, community composition, and trophic networks (Garmendia et al. 2013 , Cusack et al. 2015 , Beltrão–Mendes et al. 2023). Community structure is typically evaluated via diversity indices, community similarity analyses, and multivariate ordination techniques (Ahumada et al. 2011 , Cassano et al. 2012 ). The focus on community structure reflects a shift toward a more holistic understanding of landscape ecology, with the recognition that species do not respond to habitat changes in isolation but rather are part of complex interaction networks (Clark 2010 , Del Val de Gortari 2022 ). This approach is particularly valuable in the context of habitat fragmentation and land-use change, where impacts on one species can have cascading effects on the entire community (Semenchuk et al. 2022 ). In the case of the activity patterns variable, it referred to spatial concurrence and not the interaction between species (e.g. predator-prey) and was used to infer habitat preferences. On the other hand, with regard to the density variable, McCallum ( 2012 ) in 414 papers from 1994 to 2011, found that the density variable was the most used, while in this review it was found only in four articles. This may be because with camera traps, density is primarily calculated for individuals with distinguishable markings (Green et al., 2020 ), and most of the studies focused on the mammal community in general. Natural environmental changes due to anthropogenic factors such as agriculture, livestock farming, pollution, and urbanization have affected up to 95% of the Earth's surface, directly impacting wildlife (Kennedy et al. 2019 ). Therefore, a significant area of conservation research seeks to understand the influence of these factors on faunal species and predict responses to establish management strategies (Lamine et al. 2018 , Li et al. 2022 ). Understanding how medium- and large terrestrial mammal species persist and adapt to modifications of natural habitats was the main conservation objective of the articles. Studies have shown that mammal species respond differently to anthropogenic landscape matrices (Ehlers Smith et al. 2017 ). In this context, surveys have been conducted in various types of ecosystems, vegetation, and anthropogenic disturbances, which are categorized into two groups: a) native and secondary forests, and b) areas impacted by human activities. Native forests primarily include tropical rainforests, Atlantic forests, mangroves, deciduous and semideciduous forests, wetlands and riparian forests, encompassing savannahs, steppes, native grasslands, and prairies, which are considered threatened habitats due to human activities. Conservation efforts aim to gather information for their preservation (Bernard et al. 2014 , Rich et al. 2016 , De Pinho et al. 2017 , Iezzi et al. 2020 , Mori et al. 2021 ). Surveys in conserved and secondary forests were regularly conducted to compare mammal structure and abundance with those of sites subjected to human disturbances, as well as to understand how habitat remnants preserve mammal populations. On the other hand, areas impacted by human activities refer primarily to those associated with agriculture involving monocultures, livestock farming, and urban zones. Research has focused mainly on oil palm, sugarcane, pine, and eucalyptus plantations; cattle ranching; and urban development, as these activities are considered the major causes of the conversion of natural habitats into anthropogenic landscapes (Bernard et al. 2014 , Ehlers Smith et al. 2017 , Kennedy et al. 2019 , Iezzi et al. 2020 ). The articles generally highlighted the impact of anthropogenic landscapes on mammal species composition compared with native forests, showing that most species respond negatively and emphasizing the importance of conserving habitat remnants to maintain ecological processes (Beca et al. 2017 , Cruz et al. 2018 , Boron et al. 2019 ). Another relevant aspect was the documentation of the ecological flexibility of certain species to land-use change and those that are habitat specialists (Pardo et al. 2018 , Colihueque et al. 2023 , Woodgate et al. 2023 ). Landscapes are spatial mosaics of biophysical and socioeconomic components that interact, are compositionally diverse, and are spatially heterogeneous (Wu 2013 ), influencing ecological processes and affecting species. Under this assumption, landscape metrics were adopted as components to explain these relationships (Uuemaa et al. 2013 ). Currently, hundreds of metrics are used to measure landscape patterns through many applications, but their use is not free from scrutiny (Frazier & Kedron 2017 ). These metrics are mainly classified into two categories: those that measure landscape composition and those that assess its spatial configuration, the latter being applicable both at the landscape and fragment scale. Researchers employing these metrics have focused on identifying the elements that influence mammal populations under different land use conditions, considering structural aspects that indicate spatial relationships (continuity and adjacency) between landscape elements (for example, forest fragments) and the functional aspect which refers to the landscape characteristics that facilitate or impede the movement of species between habitat patches (Frazier and Kedron 2017 ). The application of metrics to integrate them into analyses and contrast them with variables was highly diverse. The metrics most frequently used were those corresponding to human disturbances, incorporating those that, owing to productive or recreational human activities, limit or facilitate mammal movement. Most of the measurements of these metrics are based on distances calculated through geographic information systems (GIS), which are enabled by advances in computational technology; free access to satellite images with very high spatial, temporal, and spectral resolutions, which allows for the interpretation of complex datasets; the detection and monitoring of changes in vegetation and biodiversity over time; and the detection of rapid, large-scale changes (Rose et al. 2014 , Steenweg et al. 2016 , Mandujano 2019 ). Fragmentation is understood as a large expanse of habitat transformed into a series of smaller fragments of total area, which are isolated from each other by a matrix of habitats different from the original one (Fahrig 2003 ). Many studies have focused on analyzing habitat loss and fragmentation and their effects on species (Martin 2018 ). Currently, there is a scientific debate questioning whether the characteristics of the fragment (such as its size and isolation) are truly important as predictors of species richness, suggesting that these effects could simply be explained by total habitat loss in the landscape (Fahrig, 2013 ; Hanski, 2015 ; Martin, 2018 ). Owing to this situation, it is inferred that habitat quantity was the most commonly used landscape metric in analyses as a predictive covariate over those referring to fragments. A consideration for habitat-related metrics is that they must be well defined, considering that the spatial extent is appropriate for the species studied and the resolution at which the values are taken (Frazier & Kedron 2017 ). In most articles, metrics are calculated via remote sensing methods, not considering internal site aspects that are considered to complement the information and obtain more precise data when seeking to respond to landscape effects on mammal populations. One of the most common measures for biodiversity protection is the creation of PNA, which serve as refuges for wildlife (Ehlers Smith et al. 2017 ). Approximately half of the studies aimed at evaluating mammal populations within PNAs intend to assess whether these wildlife refuges fulfill their function and to detect possible threats. Conclusions There has been a significant increase in the number of publications over the past six years, with a diversification in the journals disseminating these studies, indicating a growing interest in understanding how landscape changes affect mammalian populations. This growth has been facilitated by technological advancements, particularly in the use of camera traps and more sophisticated analysis techniques. The studies are mainly concentrated in South America, North America, and Asia, with a particular focus on tropical and subtropical biomes. This distribution reflects both the rich biodiversity of these regions and the urgent need for conservation due to rapid landscape transformations. Most studies focus on entire mammalian communities, providing a more comprehensive view of the landscape's effects on biodiversity. However, these findings underscore the need for more studies focused on individual species to better understand specific responses. Species richness, occupancy, and abundance have emerged as the most studied response variables, providing crucial information on how mammals respond to landscape changes. The increase in occupancy studies reflects advances in analytical frameworks for dealing with imperfect detection. Metrics related to human disturbances and habitat quantity have emerged as key factors influencing mammal populations, highlighting the need to consider both landscape composition and configuration in conservation efforts. Although approximately half of the studies were conducted in PNA, no significant differences were found compared with studies outside these areas, suggesting the need for conservation strategies that extend beyond PNA. This review demonstrates substantial progress in our understanding of how landscape attributes affect populations of medium- and large-sized terrestrial mammals. However, it also reveals significant knowledge gaps, particularly concerning long-term effects, species interactions, and responses to rapid environmental changes, including climate change. Future studies should address these gaps by focusing on long-term research, expanding geographic coverage to underrepresented regions, and incorporating interdisciplinary approaches that consider both the ecological and socioeconomic factors driving landscape changes. Additionally, it is crucial that future research translates into more effective conservation and landscape management strategies to protect mammalian biodiversity in an increasingly anthropized world. Declarations The authors declare that they have no financial or non-financial interests, direct or indirect, that could represent a conflict of interest in relation to the work submitted for publication. Acknowledgments J.R.T. thanks the National Council of Humanities, Sciences, and Technologies of Mexico (CONAHCYT) for the scholarship granted (CVU: 206503), which made this work possible. 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Proceedings of the Royal Society B: Biological Sciences 288(1942), 20202466. https://doi.org/10.1098/rspb.2020.2466 Semenchuk P, Plutzar C, Kastner T, Matej S, Bidoglio G, Erb K, Essl F, Haberl H, Wessely J, Krausmann F, Dullinger S (2022) Relative effects of land conversion and land-use intensity on terrestrial vertebrate diversity. Nat Commun 13(1). https://doi.org/10.1038/s41467-022-28245-4 Steenweg R, Hebblewhite M, Kays R, Ahumada J, Fisher JT, Burton C, Townsend SE, Carbone C, Rowcliffe JM, Whittington J, Brodie J, Royle JA, Switalski A, Clevenger AP, Heim N, Rich LN (2016) Scaling-up camera traps: monitoring the planet's biodiversity with networks of remote sensors. Front Ecol Environ 15(1):26–34. https://doi.org/10.1002/fee.1448 Sunarto SR, Kelly MJ (2013) Camera Trapping for the Study and Conservation of tropical carnivores. Raffles Bull Zool 28(28):21–42 Tanwar KS, Sadhu A, Jhala YV (2021) Camera trap placement for evaluating species richness, abundance, and activity. Scientific Reports 11(1). https://doi.org/10.1038/s41598-021-02459-w Thornton DH, Branch LC, Sunquist ME (2011) The influence of landscape, patch, and within-patch factors on species presence and abundance: a review of focal patch studies. Landscape Ecol 26(1):7–18. https://doi.org/10.1007/s10980-010-9549-z Uuemaa E, Antrop M, Roosaare J, Marja R, Mander L (2009) Landscape Metrics and Indices: An Overview of Their Use in Landscape Research. Living Reviews Landsc Res. https://doi.org/10.12942/lrlr-2009-1 Uuemaa E, Mander L, Marja R (2013) Trends in the use of landscape spatial metrics as landscape indicators: A review. Ecol Ind 28:100–106. https://doi.org/10.1016/j.ecolind.2012.07.018 With KA (2019) Landscape Effects on Population Distributions and Dynamics. En Essentials of Landscape Ecology (1.a ed. Oxf Acad 291–335. https://doi.org/10.1093/oso/9780198838388.003.0007 Woodgate Z, Drouilly M, Distiller G, O'Riain MJ (2023) The Effect of Multi-Use Landscapes on Mammal Assemblages and Its Implication for Conservation. Land 12(3):599. https://doi.org/10.3390/land12030599 Wu J (2013) Landscape sustainability science: ecosystem services and human well-being in changing landscapes. Landscape Ecol 28(6):999–1023. https://doi.org/10.1007/s10980-013-9894-9 Zungu MM, Maseko MS, Kalle R, Ramesh T, Downs CT (2020) Effects of landscape context on mammal richness in the urban forest mosaic of EThekwini Municipality, Durban, South Africa. Global Ecol Conserv 21:e00878. https://doi.org/10.1016/j.gecco.2019.e00878 Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6116754","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":421525182,"identity":"6d926792-082c-425b-a677-1ea168154c8b","order_by":0,"name":"Jenner Rodas-Trejo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACxgY4M4GB4QOIliBFC+MMYrQggQQGZh5itDC39x578HEPQz4/e/Ljz7Ztd+T5ZzewbubB57Cec+mGM54xWM7seWYmndv2zHDGnQNsN2fg0zIjx0ya5wCDgcGNBDPm3LbDjBskEthufMCnZf4bM+k/YC3pnz9bth22B2tJwGsLj5k0A1hLjoE0Y9vhRMK29OSYSfYckDCQ7HlTJtlz7nDyjBuJbXj9Yth+xkzixwEbA3729M0ffpQdtu2fkXzsNr4QM2wAUyhxgRS92IA8XtlRMApGwSgYBSAAAFo+TyXvZD6tAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-6158-9734","institution":"Instituto de Ciencias Biológicas, Universidad de Ciencias y Artes de Chiapas","correspondingAuthor":true,"prefix":"","firstName":"Jenner","middleName":"","lastName":"Rodas-Trejo","suffix":""},{"id":421525183,"identity":"77f7c1fb-938f-4d2c-8923-dd6a31f7f377","order_by":1,"name":"Sergio López Mendoza","email":"","orcid":"https://orcid.org/0000-0001-5173-7238","institution":"Instituto de Ciencias Biológicas, Universidad de Ciencias y Artes de Chiapas","correspondingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"López","lastName":"Mendoza","suffix":""},{"id":421525184,"identity":"26205d52-c0e0-40c5-99ed-2c37313e0462","order_by":2,"name":"Cesar Tejeda Cruz","email":"","orcid":"https://orcid.org/0000-0003-1636-0409","institution":"Instituto de Ciencias Biológicas, Universidad de Ciencias y Artes de Chiapas","correspondingAuthor":false,"prefix":"","firstName":"Cesar","middleName":"Tejeda","lastName":"Cruz","suffix":""},{"id":421525185,"identity":"07efa87a-1353-4384-9d23-566cec4f7fed","order_by":3,"name":"Jaime R. Rau","email":"","orcid":"https://orcid.org/0000-0003-0444-578X","institution":"Laboratorio de Ecología, Departamento de Ciencias Biológicas y Biodiversidad, Universidad de Los Lagos, Campus Osorno","correspondingAuthor":false,"prefix":"","firstName":"Jaime","middleName":"R.","lastName":"Rau","suffix":""},{"id":421525186,"identity":"549a59bc-8579-412a-b173-f7ebd8e4399b","order_by":4,"name":"Carlos Tejeda Cruz","email":"","orcid":"","institution":"Universidad Autónoma de Chiapas","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"Tejeda","lastName":"Cruz","suffix":""}],"badges":[],"createdAt":"2025-02-27 02:16:38","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6116754/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6116754/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77388226,"identity":"66e1eb34-7155-4325-b4d6-d4015578d697","added_by":"auto","created_at":"2025-02-28 06:00:24","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53926,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of articles published per year\u003c/p\u003e","description":"","filename":"fig.1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6116754/v1/4e8581b8d5e7f2fad7820a96.jpeg"},{"id":77386597,"identity":"d3411d70-8a77-45e9-a3dc-9ae6e70beaca","added_by":"auto","created_at":"2025-02-28 05:28:24","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":44074,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of articles published by country\u003c/p\u003e","description":"","filename":"fig.2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6116754/v1/3d44500941559213aee4ed03.jpeg"},{"id":77386743,"identity":"4d3c1fd0-bf2a-41a4-af23-c59edb29015f","added_by":"auto","created_at":"2025-02-28 05:36:24","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72555,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of research topics recorded per year. Connectivity and use of biological corridors (Use_Corr), Factors influencing species occupancy in conserved and/or fragmented environments (Ocup), Effect of habitat, landscape, and fragment characteristics on populations of medium and large mammals (Eff_lands_frag), Effects of anthropogenic activities on populations of medium and large terrestrial mammals in different environments (Eff_anthr), Distribution and occurrence of populations of medium and large terrestrial mammals in different environments, vegetation types, and land use (Dist_Ocur), Current status of communities and/or populations of medium and large terrestrial mammals in different environments, habitat conditions, PNAs (Curr_stat), climate change (CC), activity patterns in different environments, habitats, landscapes, and/or fragments (Activ)\u003c/p\u003e","description":"","filename":"Fig.3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6116754/v1/1a1a9d1ad5a6ed9fc2517fdf.jpeg"},{"id":77386741,"identity":"5158e252-3713-41e1-9396-fff9e2273923","added_by":"auto","created_at":"2025-02-28 05:36:24","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71498,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of annual publications of the response variables on which the ecological objectives of the publications were based\u003c/p\u003e","description":"","filename":"Fig.4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6116754/v1/aa47c53b938ba018b840a5e2.jpeg"},{"id":77386611,"identity":"ba92c9d8-c154-4da6-9232-19f0c3133988","added_by":"auto","created_at":"2025-02-28 05:28:24","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":339579,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e5a.\u003c/strong\u003eLandscape metrics, fragmentation metrics and detection covariates that were integrated into the analyses in the research work. \u003cstrong\u003e\u0026nbsp;5b.\u003c/strong\u003e Vegetation types and human activities where the samples were collected\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6116754/v1/3b4612c755c5bd8d0a3aa472.jpeg"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEffects of Landscape Attributes on Medium- and Large Terrestrial Non-Volant Mammals: A Systematic Review of Camera Trap Studies (2010--2023)\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLandscapes are heterogeneous physical spaces whose characteristics result from the interaction of biophysical and socioeconomic components (Clark \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In these spaces, the arrangement of elements and landscape structure is determined primarily by natural changes and those caused by human activities (Cushman et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, K\u0026uuml;hne and Antrop \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Currently, owing to human activities, terrestrial landscapes are experiencing unprecedented transformations, leading to habitat loss, degradation, and fragmentation on a global scale (Prugh et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Schipper et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Thornton et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese landscape alterations have severe effects on wildlife, and their survival largely depends on factors such as landscape composition, the availability of habitat resources within the landscape structure, and species adaptability and resilience to new habitat matrices (Saura and Pascual-Hortal \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Brady et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Schooley and Branch \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; With \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Understanding how habitat configuration, quantity, quality, and connectivity influence wildlife populations has become a central research theme in landscape ecology (Saura \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudies on the effects of landscape on wildlife employ a multilevel approach that examines both the landscape scale (amount and type of habitat, human pressure) and the patch scale (vegetation structure, size, and shape) to understand how habitat amount, structural complexity, and spatial configuration\u0026mdash;particularly fragmentation and connectivity\u0026mdash;affect various aspects of species, such as their presence, richness, abundance, and distribution (Fahrig \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hanski \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Keinath et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Martin \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gardiner et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMedium- and large-sized terrestrial mammals are particularly vulnerable to fragmentation due to their space requirements, mobility, and dependence on well-preserved habitats. Many studies have aimed to assess the impacts of landscape modification on species' distribution, activity patterns, reproduction, survival, and habitat use. (Prugh et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Brady et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Crooks et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Zungu et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These studies have provided insights into the effects of fragmentation and identified the attributes with the greatest impact on mammals, generating valuable information to guide area management and achieve conservation objectives (Cushman and McGarigal 2005).\u003c/p\u003e \u003cp\u003eIn recent years, research on the effects of landscape and fragmentation on medium and large terrestrial mammals has advanced significantly, largely owing to the use of camera traps as a methodological tool. This has allowed data collection at different scales, both spatially and temporally, providing detailed information on the distribution, behavior, and population dynamics of mammals in fragmented habitats (Caravaggi et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cruz et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rocha et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, advances in analytical approaches, such as occupancy models and the application of more advanced numerical analysis, have allowed researchers to evaluate the effects of the landscape on species beyond traditional presence‒absence methods (With \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mandujano et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs tools and approaches continue to evolve, a comprehensive synthesis that integrates information gathered using camera trap is needed. This review not only provides an up-to-date summary of the knowledge acquired from 2010\u0026ndash;2023 but also helps systematize emerging patterns, identify key variables, and enhance the understanding of how landscape attributes influence medium and large terrestrial mammals at various spatial scales and ecological regions. This synthesis also highlights emerging trends in the use of technologies such as camera traps and other methodological approaches.\u003c/p\u003e \u003cp\u003eThis review identifies patterns in geographic coverage, research topics, response variables, land use, and landscape metrics, while evaluating the effectiveness of camera traps and the impact of human intervention at landscape level. It helps to identify priority areas for future research and guides more effective conservation strategies to tackle fragmentation and landscape transformation.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eA search for articles published from 2010\u0026ndash;2023 was conducted via the EBSCO, Google Scholar, ScienceDirect, and Web of Science databases, along with artificial intelligence (AI) enhanced tools such as ResearchRabbit and Semantic Scholar. We limited our search to peer-reviewed articles in academic journals which were available in English. Three key search elements were combined: (1) the study base with the words \u0026ldquo;landscape,\u0026rdquo; \u0026ldquo;land use,\u0026rdquo; \u0026ldquo;landscape change,\u0026rdquo; \u0026ldquo;fragmentation,\u0026rdquo; \u0026ldquo;forest fragments,\u0026rdquo; \u0026ldquo;habitat,\u0026rdquo; \u0026ldquo;habitat use,\u0026rdquo; and \u0026ldquo;habitat loss\u0026rdquo;; (2) the study object with the words \u0026ldquo;mammal,\u0026rdquo; \u0026ldquo;terrestrial mammal,\u0026rdquo; \u0026ldquo;medium and large mammals\u0026rdquo;; and (3) the data collection method with the words \u0026ldquo;camera trap,\u0026rdquo; \u0026ldquo;camera trapping,\u0026rdquo; and \u0026ldquo;infrared triggered camera.\u0026rdquo;\u003c/p\u003e \u003cp\u003ePublications that studied communities, populations, and species of medium- and large-sized terrestrial mammals at the following levels were selected:\u003c/p\u003e \u003cp\u003ea) Analysis of occupancy, diversity, richness, composition, community structure, populations, and species of medium and large terrestrial mammals in conserved, deforested, fragmented, disturbed (fires, agriculture, livestock, forestry plantations, among others), urbanized environments, and/or biological corridors.\u003c/p\u003e \u003cp\u003eb) Use, rejection, and/or preference of the landscape by communities, populations, and species of medium and large terrestrial mammals in conserved, fragmented, disturbed, urbanized environments and/or biological corridors.\u003c/p\u003e \u003cp\u003ec) Effects of landscape, fragment, and/or habitat attributes on communities, populations, and species of medium and large terrestrial mammals.\u003c/p\u003e \u003cp\u003eArticles that did not consider studies on landscape and/or fragmentation were excluded, as many reported diversity, new species records, activity patterns, inventories, resource selection, and diet. The articles were systematized in an Excel database with information divided into three groups and 10 categories following McCallum (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), Burton et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Correa (2016), Keinath et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Agha et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and Delisle et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGroups and categories included in the analysis of the articles.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral Information and Content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJournal and year of publication.\u003c/p\u003e \u003cp\u003eCountries, continents, and biomes where the research was conducted.\u003c/p\u003e \u003cp\u003eScale of the study: local, national (\u0026gt;\u0026thinsp;3 provinces of a country), transnational (\u0026gt;\u0026thinsp;2 countries within the same continent), intercontinental (\u0026gt;\u0026thinsp;2 continents).\u003c/p\u003e \u003cp\u003eResearch topics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy Design and Approach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObject of study.\u003c/p\u003e \u003cp\u003eLevel at which the study was conducted: landscape, fragmentation, landscape-fragmentation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalysis Metrics and Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse variables used.\u003c/p\u003e \u003cp\u003eLandscape metrics, fragmentation metrics, and detection covariates integrated into the analysis.\u003c/p\u003e \u003cp\u003eNumber of detected species reported.\u003c/p\u003e \u003cp\u003eLand use where the studies were conducted.\u003c/p\u003e \u003cp\u003eWork inside or outside Protected Natural Areas.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGroup 1: General Information and Content:\u003c/h2\u003e \u003cp\u003eThe articles were classified by year, the journals in which they were published, and spatial scale (the continents, countries, and biomes where the research was conducted), consulting the Ecoregions2017\u0026copy;Resolve site (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ecoregions.appspot.com\u003c/span\u003e\u003cspan address=\"https://ecoregions.appspot.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The study scale was determined as a) local (those studies that covered only one region), b) national (those that considered at least three provinces or regions of a country), c) transnational (those that covered more than two countries on the same continent), and d) intercontinental (those that conducted studies on more than one continent).\u003c/p\u003e \u003cp\u003eTo group publications into research topics, an analysis of the hypotheses and objectives of each article was conducted and classified as follows: 1) climate change; 2) distribution and occurrence of communities, populations, and species of medium and large terrestrial mammals in different environments, vegetation types, and land use; 3) effect of habitat, landscape, and fragment characteristics on communities, populations, and species of medium and/or large terrestrial mammals; 4) effects of anthropogenic activities on communities, populations, and species of medium and large terrestrial mammals in different environments; 5) current status of communities, populations, and species of medium and large terrestrial mammals in different environments, habitat conditions, and protected natural areas (PNA); 6) factors influencing the occupancy of communities, populations, and species in conserved and/or fragmented environments; 7) activity patterns of species in different environments, habitats, landscapes, and/or fragments; and 8) connectivity and use of biological corridors. Each article could contain one or more research topics.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGroup 2: Study Design and Approach:\u003c/h3\u003e\n\u003cp\u003eTo characterize the study objects, the subjects of the studies were considered, and the records were grouped according to whether they were reported as focal species when the studies focused on a particular species, multiple species when all recorded species were considered, and trophic guilds when the study was directed at a particular guild (Agha et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The study level was classified as a) landscape for studies analyzing landscape-level elements, b) fragments for studies focusing specifically on remans elements, and c) landscape fragmentation for those conducting studies at both levels.\u003c/p\u003e\n\u003ch3\u003eGroup 3: Analysis Metrics and Variables\u003c/h3\u003e\n\u003cp\u003eConsidering ecological objectives, response metrics were classified into eight variables: abundance (primarily considering the relative abundance index (RAI)), activity patterns, community structure, density, diversity, occupancy, detection probability, and species richness (Burton et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Kays et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Delisle et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Detection covariates, landscape metrics, and fragmentation metrics were classified into 23 subcategories, focusing on a set of metrics widely addressed in the literature (Botequilha Leit\u0026atilde;o and Ahern \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Uuemaa et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Uuemaa et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Keinath et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of landscape metrics, fragments and detection covariates included in the analyses of the articles.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClasificaci\u0026oacute;n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman disturbance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHum_dist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandscape metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElements of the landscape that impact or have been impacted by human activities such as tourism, hunting, human settlements (Li et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u0026aacute;bitat amount\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHab_amo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandscape metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProportion of primary and secondary forest. The hypothesis predicts that species richness at a habitat site increases with the amount of habitat in the \"local landscape\" defined by an appropriate distance around the site, without distinctive effects of the habitat patch size in which the site is located (Fahrig \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Saura \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbiotic factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbi_fact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandscape metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbiotic factors involved in the characterization of a given ecosystem, such as altitude, topography, slope, among others (Nakashima et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatrix the Patch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMat_P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatch metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndicates the types of habitat of the patches included in a study. Patches refer to preserved vegetation or secondary forest (Keinath et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpatial configuration of the landscape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpa_confL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandscape metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElements that constitute the quantitative landscape (Parsons et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandscape impact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand_imp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandscape metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProportion of areas where human and natural activities occur that impact the landscape (Cruz et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u0026aacute;bitat type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHab_ty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandscape metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndicates the main habitat type included in a study, referring to preserved vegetation or secondary forest (Keinath et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatrix type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMat_ty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandscape metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRepresents the primary driver of fragmentation in a study. The categories are urban, agriculture, livestock, and seminatural (e.g., burning, flooding) (Garmendia et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Keinath et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandscape size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand_siz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandscape metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComposition and size of each landscape element.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterspecific interactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntr_rel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDetection covariates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDirectly or indirectly examine interspecific interactions (e.g., predator‒prey, competition, mutualism) (Cruz et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Delisle et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsolation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatch metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDistance to the nearest forest patch (Garmendia et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Isolation is due to landscape resistance exerted on the remaining habitats by the surrounding nonhabitat matrix (Botequilha et al. 2002).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCamera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCamera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDetection covariates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvaluates the success of capture due to the different operational capabilities and characteristics of camera models (Penjor et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatch area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea_P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatch metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRepresents the contiguous area of a remaining habitat patch, measured in hectares (Keinath et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtected areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandscape metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConsidered public property and management land, protected from private urbanization (size of the protected area, type of protected area, etc.) (Massara et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Chen et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomestic mammals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDom_mam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDetection covariates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePresence of domestic animals in the study area (Parsons et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Massara et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal area of trees/saplings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasal_ar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatch metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBasal area of small trees/sprouts within the patch estimated per hectare, applies to lianas, shrubs, etc. (Thornton et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntraspecific characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntr_char\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDetection covariates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecies characteristics such as reproductive rate, home range, body mass.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatch metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHabitat fragmentation measured by the patch shape index, explains an increase in the length of the habitat patch edge; more circular areas have less edge effect (Garmendia et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of patches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNum_P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandscape metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndicates the number of habitat patches evaluated within a study (Keinath et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDensity of forest edges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDens_forEd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandscape metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal density of forest - nonforest edge in the landscape (km/ha) (Thornton et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDensity of forest patches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDens_forP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandscape metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDensity of forest patches in the landscape (no./km\u0026sup2;) (Thornton et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Pfeifer et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime since fragmentation or disturb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime_dist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandscape metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTime elapsed since the loss of primary or secondary forest (Semper-Pascual et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean patch size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean_sizP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandscape metrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage size of all forest patches in the landscape (ha) (Thornton et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor the number of species, articles focusing on multispecies studies were considered, excluding those focused on trophic guilds or focal species, and these were grouped by continent. These variables were calculated only in local-level studies, and the minimum, maximum, standard deviation, and arithmetic means were reported (Green et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo quantify land use, the characteristics of the vegetation types and anthropogenic activities described at the study sites were considered. The vegetation types were grouped into 1) Native forests, 2) Degraded forests, 3) Wetlands and riparian areas, 4) Native grasslands, 5) Secondary forests, and 6) Shrub vegetation. Human activities were summarized as follows: 7) agriculture, 8) monoculture plantations, 9) introduced pastures for livestock, 10) urban and developed areas, 11) livestock, and 12) mining and energy.\u003c/p\u003e \u003cp\u003eThe studies conducted inside and outside protected natural areas were counted. A chi-square analysis was performed to determine if there were significant differences between them. The number of species reported in the studies by continent was counted, and the means and standard deviations were obtained.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGeneral Information and Content\u003c/h2\u003e \u003cp\u003eThe search yielded 314 scientific publications, 180 of which met the inclusion criteria and were included in this study. Of these, 66.11% (n\u0026thinsp;=\u0026thinsp;119) were published in the past six years (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe reviewed articles were published in 73 scientific journals, with ten accounting for 46.67% (n\u0026thinsp;=\u0026thinsp;84) of the works. Of these, 7.77% were published in Biological Conservation (n\u0026thinsp;=\u0026thinsp;14), 6.66% in PLOS ONE (n\u0026thinsp;=\u0026thinsp;12), 6.11% (n\u0026thinsp;=\u0026thinsp;11) in Mammalian Biology, and 5.56% (n\u0026thinsp;=\u0026thinsp;10) in Oryx; in 44 journals, only one article was found.\u003c/p\u003e \u003cp\u003eThe studies were conducted across five continents (114 in America, 30 in Asia, 24 in Africa, seven in Europe, and three in Oceania) and in 54 countries (16 in America, 16 in Africa, 15 in Asia, six in Europe, and one in Oceania), with five countries accounting for 50.00% (n\u0026thinsp;=\u0026thinsp;89). The six countries with the highest contributions were Brazil, with 38 studies; Argentina, with 17; the United States, with 14; and Malaysia and Mexico, with 10 each (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The most common scale of the studies was local, focusing on a single region within a country (95.56%; n\u0026thinsp;=\u0026thinsp;172); four studies were national (2.22%); four were transnational, two were in America and one was in Asia; and three were continental, encompassing America, Europe, Asia, and Africa (2.22%). Thirteen biomes (Olson et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) were represented, with Tropical and Subtropical Moist Broadleaf Forests accounting for 47.16% (n\u0026thinsp;=\u0026thinsp;83), Tropical and Subtropical Grasslands, Savannas, and Shrublands accounting for 17.06% (n\u0026thinsp;=\u0026thinsp;30), Temperate Broadleaf and Mixed Forests accounting for 12.50% (n\u0026thinsp;=\u0026thinsp;22), and Mediterranean Forests, Woodlands, and Scrub accounting for 5.11% (n\u0026thinsp;=\u0026thinsp;9).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eResearch topics were included 272 times, with the topics \u0026ldquo;Effects of anthropogenic activities on populations of medium and large terrestrial mammals in different environments\u0026rdquo; and \u0026ldquo;Effects of habitat, landscape, and fragment characteristics on populations of medium and large mammals\u0026rdquo; identified in 67 articles (24.63%) and 61 publications (22.43%), respectively. The \u0026ldquo;Distribution and occurrence of populations of medium and large terrestrial mammals in different environments, vegetation types, and land use\u0026rdquo; was the third most recorded topic (20.59%; n\u0026thinsp;=\u0026thinsp;56); the topic \u0026ldquo;Current status of communities and/or populations of medium and large terrestrial mammals in different environments, habitat conditions, and protected natural areas (PNAs)\u0026rdquo; was the fourth most common topic (10.66%; n\u0026thinsp;=\u0026thinsp;29); and the topic \u0026ldquo;climate change\u0026rdquo; was recorded in only 1.47% (n\u0026thinsp;=\u0026thinsp;4) of the publications (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Approach\u003c/h2\u003e \u003cp\u003eRegarding species/community approach, 68.89% (n\u0026thinsp;=\u0026thinsp;124) of the studies focused on the mammal community as a whole, while 15.00% (n\u0026thinsp;=\u0026thinsp;27) targeted specific trophic guilds, primarily carnivores and herbivores. Additionally, 29 studies (16.11%) examined 40 individual species, with Panthera onca, Vulpes vulpes, and Puma concolor being the most frequently studied, each appearing in three studies. Camera traps were the predominant sampling method, employed exclusively in 90.00% (n\u0026thinsp;=\u0026thinsp;162) of the studies, followed by a combination of camera traps and track searches in 7.22% (n\u0026thinsp;=\u0026thinsp;13). Other methods, such as interviews, direct observations, and DNA sampling, were used in 2.77% (n\u0026thinsp;=\u0026thinsp;5) of the studies. Research was conducted primarily at the landscape level (n\u0026thinsp;=\u0026thinsp;140; 77.77%), with fewer studies focusing on the fragment level (n\u0026thinsp;=\u0026thinsp;28; 15.56%) and the landscape fragmentation level (n\u0026thinsp;=\u0026thinsp;12; 6.67%).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalysis Metrics and Variables\u003c/h3\u003e\n\u003cp\u003eThe response variables were presented 355 times in the articles and focused primarily on two of the eight response variables, species richness (28.45%, n\u0026thinsp;=\u0026thinsp;101) and occupancy (25.63%, n\u0026thinsp;=\u0026thinsp;91), which accounted for the highest number of records and have shown an increasing trend since 2016, followed by the abundance variable (12.39%, n\u0026thinsp;=\u0026thinsp;44). The fourth response variable was community structure (9.86%, n\u0026thinsp;=\u0026thinsp;35), whereas detection probability, diversity, activity patterns, and density were the variables with the lowest records, with values of 9.01% (n\u0026thinsp;=\u0026thinsp;32), 7.89% (n\u0026thinsp;=\u0026thinsp;28), 5.35% (n\u0026thinsp;=\u0026thinsp;19), and 1.41% (n\u0026thinsp;=\u0026thinsp;5), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLandscape, fragmentation metrics, and detection covariates were integrated into the studies' analyses 808 times. Landscape metrics appeared 435 times, with \"human disturbance\" covariates, which include human activities, distances to settlements, population density, and hunting pressure, being mentioned 152 times, and \"habitat amount\", which includes the proportions of conserved and secondary vegetation cover, was mentioned 87 times (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Patch metrics appeared in 202 instances, with the most important covariate being the \"Matrix the patch,\" which includes counts of palms, trees, and shrubs, among others, with 101 mentions, followed by \"Isolation\", with 33 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The detection covariates appeared 153 times, with \"Abiotic factors,\" which include altitude, temperature, precipitation, and season of the year, among others, mentioned 92 times, followed by \u0026ldquo;Interspecific relationships,\u0026rdquo; which integrates the frequency of appearance and abundance of mammals (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eLand use was defined based on the described location where the camera traps were placed to detect species, considering human activities and vegetation types. The studies focused on Native Forest (n\u0026thinsp;=\u0026thinsp;142, 77.17%), which encompasses vegetation in its original state, followed by Grasslands (n\u0026thinsp;=\u0026thinsp;86, 46.74%), Wetlands and Riparian Forests (n\u0026thinsp;=\u0026thinsp;26, 14.13%), and Secondary Forests (n\u0026thinsp;=\u0026thinsp;20, 10.87%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Human activities included sites with agriculture (n\u0026thinsp;=\u0026thinsp;78, 42.39%), in particular maize and rice cultivation, followed by monoculture plantations (n\u0026thinsp;=\u0026thinsp;44, 23.91%), such as oil palm and forestry plantations such as pine and eucalyptus, and cattle pastures (n\u0026thinsp;=\u0026thinsp;30, 16.30%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfrica had the highest average number of detected species at 26.16\u0026thinsp;\u0026plusmn;\u0026thinsp;12.74, with a maximum of 44 and a minimum of 5. Asia was the second continent with 23.26\u0026thinsp;\u0026plusmn;\u0026thinsp;16.26, with a maximum of 61 and a minimum of 3, followed by America with 17.29\u0026thinsp;\u0026plusmn;\u0026thinsp;10.87, a maximum of 40 and a minimum of 3; Europe with 8.50\u0026thinsp;\u0026plusmn;\u0026thinsp;3.32, with a maximum of 12 and a minimum of 4; and Oceania with 17.50\u0026thinsp;\u0026plusmn;\u0026thinsp;7.78, with a maximum of 23 and a minimum of 12.\u003c/p\u003e \u003cp\u003eThe published studies that conducted sampling within PNA accounted for 55.56% (n\u0026thinsp;=\u0026thinsp;100), with no significant differences found compared with those conducted outside them (Χ\u0026sup2;=2.222, df\u0026thinsp;=\u0026thinsp;1, p value\u0026thinsp;=\u0026thinsp;0.136).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGeneral Information\u003c/h2\u003e \u003cp\u003eThis review presents an overview of published articles on the effects of landscape and fragmentation on populations of medium- and large terrestrial mammals, using camera traps as a sampling method. The analysis includes information on research topics, response variables, land use, and the most evaluated landscape and fragmentation metrics; geographic coverage; species and guild focus; and study design details. This review emphasizes the importance of camera traps as a commonly used and vital method for assessing how habitat variables and the structure of natural and anthropogenic landscapes influence the status, composition, distribution, and activity patterns of medium and large terrestrial mammal populations and communities (Agha et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Pi\u0026ntilde;a et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Chen et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results highlight an annual growth in publications over the last six years, indicating a growing research interest in studying mammal populations and communities and their relationships with natural and anthropogenic landscapes. This growth is largely driven by technological innovations and reductions in camera trap costs, methodological advancements, the advent of open-source statistical software packages and geographic information systems (GIS), the availability of satellite imagery, and land-use cartographic information that facilitates more detailed landscape analyses (Burton et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Rose et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Correa et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Steenweg et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Mandujano \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe studies were published in many journals, providing researchers with broad opportunities to disseminate knowledge and indicating that there is interest from specialized journals in various disciplines of environmental sciences, such as biological conservation, conservation biology, animal ecology, landscape ecology, and evolution, in these types of studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eContent\u003c/h2\u003e \u003cp\u003eIn terms of the number of articles per continent and country, America, specifically South America (Brazil and Argentina), stands out for its scientific output. This contrasts with findings by McCallum (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), Uuemaa et al. (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and Correa et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) that North America has produced the most research, both to evaluate camera traps as a data collection tool and to understand trends in the use of landscape spatial metrics. The increase in publications in South America may be due to the reduction in equipment costs, access to information, and the formation of research networks. Delisle et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), in a study on trends in camera trap applications by thematic composition, taxonomy, and geography, reported the highest frequency of studies in South America, North America, Asia, and southern Africa, whereas the Middle East, western Australia, and northern Africa presented the lowest number of published articles, which aligns with the results of this study.\u003c/p\u003e \u003cp\u003eThe preferred study environments identified were tropical and subtropical biomes, specifically Tropical and Subtropical Moist Broadleaf Forests and Tropical and Subtropical Grasslands, Savannas and Shrublands, which are found in North, Central, and South America; Central and Southern Africa; and Southern Asia. This preference is likely due to the high levels of endemism, habitat complexity, and mammalian richness in these biomes, as well as the significant threats they face from land-use changes due to human activities and climate change (Olson \u0026amp; Dinerstein \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Ferrer-Paris et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Hoang \u0026amp; Kanemoto \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, it is imperative to continue generating knowledge on the dynamics and adaptations of terrestrial mammal populations in changing landscapes. Although some studies have conducted analyses across multiple continents and countries, most have focused on local studies, allowing for finer-scale analyses of landscapes and their components, since habitat disturbances generally occur at local levels; thus, generalizable patterns derived from coarse-scale metrics may not be applicable at finer scales (Lamine et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe transformation, loss, and fragmentation of natural habitats by human activities are considered the greatest threats to biodiversity and significant causes of species extinction (Munguia et al. 2016, Zungu et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, one of the main conservation challenges is understanding how wild species respond to the landscape matrix and land-use changes to ensure their survival over time and space (Bender et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, Schipper et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Brady et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In this context, it is not surprising that the main objectives addressed in the studies focused on evaluating the \"Effects of anthropogenic activities on populations of medium and large terrestrial mammals in different environments,\" the \"Effect of habitat, landscape, and fragment characteristics on populations of medium and large mammals,\" and the \"Distribution and occurrence of populations of medium and large terrestrial mammals in different environments, vegetation types, and land use,\" seeking to understand how these factors influence variables such as richness, occupancy, abundance, diversity, and activity of mammals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Approach\u003c/h2\u003e \u003cp\u003eOne of the main advantages of camera traps is their ability to collect data that can be used to address multiple questions for multiple species (Tanwar et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The ability of camera traps to generate information on a wide spectrum of species in numerous habitats simultaneously allows the acquisition of robust data that provide insights into variables of interest such as species richness, diversity, occupancy, resource selection, habitat use, and activity patterns in the landscape, whether for focal species or species groups (Rovero et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Sunarto \u0026amp; Kelly \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Burton et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Caravaggi et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Mandujano \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this review, the 68.89% (n\u0026thinsp;=\u0026thinsp;124) of the articles focused on multispecies studies aimed at protecting the biodiversity and communities of entire mammals, while some studies targeted groups, mainly carnivores, and fewer focused on specific species. Conservation efforts targeting multiple species are generally more efficient than single-species strategies, where the objective is to maintain biodiversity and ecosystem functions (Carroll et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). However, it is essential to consider that generalizing may be less effective in conserving a particular species or group than a specifically designed strategy, as the ability to protect viable populations can be achieved only through detailed population analyses. Therefore, it is crucial to consider conservation objectives, habitat selection, and whether the impact of human-induced stress factors varies significantly among species (Early \u0026amp; Thomas \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Brodie et al. 2015). Most focal species in the reviewed articles were indicator species, such as predators, mesopredators, and large mammals, which aligns with the findings of Correa et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), who noted that these species are selected because of their extensive habitat area requirements, relatively low population densities, charismatic nature, and significant susceptibility to human influence; thus, their conservation benefits other species and habitats (Green et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe exclusive use of camera traps as the sole sampling method in 90% of the publications highlights the widespread adoption of this technique, as demonstrated in previous reviews documenting the adoption and growth of camera trap use in wildlife research (Rovero et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Delisle et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis Metrics and Variables\u003c/h2\u003e \u003cp\u003eIn a review for the estimation of abundance in unmarked animals, Gilbert et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported that the main variables were relative abundance indices (RAI), followed by occupancy and, finally, species richness. In this review, most studies focused on estimating species richness, occupancy, and abundance variables. The analysis of these variables provides a basis for detecting population changes over time and space, thereby allowing for the testing of hypotheses about how wildlife mammal communities and populations respond to landscape modifications and human influence (Kays et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe concept of species richness, represented as the number of species in a given area and time period, is the most frequently employed measure of biodiversity, allowing for inferences about community structure. However, its measurement in extensive regions or with diverse taxa requires significant investment in sampling effort (L\u0026oacute;pez-G\u0026oacute;mez et al. 2006). Unlike our results, McCallum (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), in a review of the use of camera traps in habitats, taxa, and study types, reported that studies on species richness constituted only 10%, although a growing trend was noted, similar to the trend observed by Burton et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which might be due to economic and rapid results. The metric was primarily used to compare community composition between sites impacted by human activities versus conserved sites or those under some conservation scheme and was estimated conjointly with occupancy and, second, with species abundance measurements.\u003c/p\u003e \u003cp\u003eStudies estimating occupancy have experienced growth associated with theoretical advancements in models for their implementation (Broadley et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Delisle et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), as reflected in the review, with an increase since 2015. Occupancy refers to the proportion of area, fragments, or sites occupied by a species, accounting for the detection probability over the estimates of species presence (MacKenzie et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The results allowed inference of habitat use, landscape effects, and disturbances (e.g., logging, agriculture, livestock) at the level of a single species or community. Notably, one of the research topics identified in the articles was the estimation of factors influencing species occupancy in conserved and/or fragmented environments, with the goal of validating occupancy models.\u003c/p\u003e \u003cp\u003eThe detection probability was presented as a response variable in 32 studies (17.78%), reflecting the increasing methodological sophistication in the field. This variable is essential for correcting inherent biases in occupancy and abundance studies, especially when camera traps are used (Mandujano \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The importance of detection probability lies in its ability to distinguish between the true absence of a species and nondetection due to methodological or environmental factors (MacKenzie \u0026amp; Kendall \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In our review, we observed that researchers frequently incorporated this variable into their occupancy and abundance models, indicating a widespread awareness of the importance of addressing imperfect detectability in wildlife studies. The increased use of detection probability as a response variable can be attributed to the development of more robust analytical frameworks, such as hierarchical occupancy and abundance models (Royle \u0026amp; Dorazio \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These models allow researchers to separate ecological processes (e.g., true occupancy or abundance) from observation processes (the probability of detecting a species when present). This separation is crucial for obtaining unbiased estimates of the parameters of interest and understanding how landscape factors affect not only species presence, but also our ability to detect them.\u003c/p\u003e \u003cp\u003eWhile camera traps have made significant technological advancements, one of their main limitations is the inability to identify unmarked individuals, making it difficult to ascertain whether multiple detections involve different individuals or the same one. This limitation complicates abundance estimation (Moeller et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Gilbert et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Because of this methodological constraint, Burton et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) note that many researchers opt to estimate occupancy models or relative abundance indices (RAI). In the reviewed articles, abundance referred primarily to the RAI calculation per species, which indicates the number of detections per 100 traps/days, assuming a positive linear relationship with local real abundance (Chen et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, it is important to note that this index may be biased by variability in detectability, meaning that it does not always accurately reflect the real abundance of the studied species (Mandujano \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The RAI results were complemented with information on habitat preferences and landscape influences on medium and large terrestrial mammal populations and were used to infer differences in abundance between sites or fragments.\u003c/p\u003e \u003cp\u003eIn terms of community structure, this response variable provides a more comprehensive view of how landscape attributes affect mammal communities as a whole beyond the specific responses of individual species. Our analysis revealed that researchers used this variable to examine how changes in the landscape alter species interactions, community composition, and trophic networks (Garmendia et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Cusack et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Beltr\u0026atilde;o\u0026ndash;Mendes et al. 2023). Community structure is typically evaluated via diversity indices, community similarity analyses, and multivariate ordination techniques (Ahumada et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Cassano et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The focus on community structure reflects a shift toward a more holistic understanding of landscape ecology, with the recognition that species do not respond to habitat changes in isolation but rather are part of complex interaction networks (Clark \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Del Val de Gortari \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This approach is particularly valuable in the context of habitat fragmentation and land-use change, where impacts on one species can have cascading effects on the entire community (Semenchuk et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the case of the activity patterns variable, it referred to spatial concurrence and not the interaction between species (e.g. predator-prey) and was used to infer habitat preferences. On the other hand, with regard to the density variable, McCallum (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) in 414 papers from 1994 to 2011, found that the density variable was the most used, while in this review it was found only in four articles. This may be because with camera traps, density is primarily calculated for individuals with distinguishable markings (Green et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and most of the studies focused on the mammal community in general.\u003c/p\u003e \u003cp\u003eNatural environmental changes due to anthropogenic factors such as agriculture, livestock farming, pollution, and urbanization have affected up to 95% of the Earth's surface, directly impacting wildlife (Kennedy et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, a significant area of conservation research seeks to understand the influence of these factors on faunal species and predict responses to establish management strategies (Lamine et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Li et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderstanding how medium- and large terrestrial mammal species persist and adapt to modifications of natural habitats was the main conservation objective of the articles. Studies have shown that mammal species respond differently to anthropogenic landscape matrices (Ehlers Smith et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this context, surveys have been conducted in various types of ecosystems, vegetation, and anthropogenic disturbances, which are categorized into two groups: a) native and secondary forests, and b) areas impacted by human activities.\u003c/p\u003e \u003cp\u003eNative forests primarily include tropical rainforests, Atlantic forests, mangroves, deciduous and semideciduous forests, wetlands and riparian forests, encompassing savannahs, steppes, native grasslands, and prairies, which are considered threatened habitats due to human activities. Conservation efforts aim to gather information for their preservation (Bernard et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Rich et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, De Pinho et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Iezzi et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Mori et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Surveys in conserved and secondary forests were regularly conducted to compare mammal structure and abundance with those of sites subjected to human disturbances, as well as to understand how habitat remnants preserve mammal populations.\u003c/p\u003e \u003cp\u003eOn the other hand, areas impacted by human activities refer primarily to those associated with agriculture involving monocultures, livestock farming, and urban zones. Research has focused mainly on oil palm, sugarcane, pine, and eucalyptus plantations; cattle ranching; and urban development, as these activities are considered the major causes of the conversion of natural habitats into anthropogenic landscapes (Bernard et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Ehlers Smith et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Kennedy et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Iezzi et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe articles generally highlighted the impact of anthropogenic landscapes on mammal species composition compared with native forests, showing that most species respond negatively and emphasizing the importance of conserving habitat remnants to maintain ecological processes (Beca et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Cruz et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Boron et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Another relevant aspect was the documentation of the ecological flexibility of certain species to land-use change and those that are habitat specialists (Pardo et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Colihueque et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Woodgate et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLandscapes are spatial mosaics of biophysical and socioeconomic components that interact, are compositionally diverse, and are spatially heterogeneous (Wu \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), influencing ecological processes and affecting species. Under this assumption, landscape metrics were adopted as components to explain these relationships (Uuemaa et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Currently, hundreds of metrics are used to measure landscape patterns through many applications, but their use is not free from scrutiny (Frazier \u0026amp; Kedron \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These metrics are mainly classified into two categories: those that measure landscape composition and those that assess its spatial configuration, the latter being applicable both at the landscape and fragment scale. Researchers employing these metrics have focused on identifying the elements that influence mammal populations under different land use conditions, considering structural aspects that indicate spatial relationships (continuity and adjacency) between landscape elements (for example, forest fragments) and the functional aspect which refers to the landscape characteristics that facilitate or impede the movement of species between habitat patches (Frazier and Kedron \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe application of metrics to integrate them into analyses and contrast them with variables was highly diverse. The metrics most frequently used were those corresponding to human disturbances, incorporating those that, owing to productive or recreational human activities, limit or facilitate mammal movement. Most of the measurements of these metrics are based on distances calculated through geographic information systems (GIS), which are enabled by advances in computational technology; free access to satellite images with very high spatial, temporal, and spectral resolutions, which allows for the interpretation of complex datasets; the detection and monitoring of changes in vegetation and biodiversity over time; and the detection of rapid, large-scale changes (Rose et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Steenweg et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Mandujano \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFragmentation is understood as a large expanse of habitat transformed into a series of smaller fragments of total area, which are isolated from each other by a matrix of habitats different from the original one (Fahrig \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Many studies have focused on analyzing habitat loss and fragmentation and their effects on species (Martin \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Currently, there is a scientific debate questioning whether the characteristics of the fragment (such as its size and isolation) are truly important as predictors of species richness, suggesting that these effects could simply be explained by total habitat loss in the landscape (Fahrig, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hanski, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Martin, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Owing to this situation, it is inferred that habitat quantity was the most commonly used landscape metric in analyses as a predictive covariate over those referring to fragments. A consideration for habitat-related metrics is that they must be well defined, considering that the spatial extent is appropriate for the species studied and the resolution at which the values are taken (Frazier \u0026amp; Kedron \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In most articles, metrics are calculated via remote sensing methods, not considering internal site aspects that are considered to complement the information and obtain more precise data when seeking to respond to landscape effects on mammal populations.\u003c/p\u003e \u003cp\u003eOne of the most common measures for biodiversity protection is the creation of PNA, which serve as refuges for wildlife (Ehlers Smith et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Approximately half of the studies aimed at evaluating mammal populations within PNAs intend to assess whether these wildlife refuges fulfill their function and to detect possible threats.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThere has been a significant increase in the number of publications over the past six years, with a diversification in the journals disseminating these studies, indicating a growing interest in understanding how landscape changes affect mammalian populations. This growth has been facilitated by technological advancements, particularly in the use of camera traps and more sophisticated analysis techniques.\u003c/p\u003e \u003cp\u003eThe studies are mainly concentrated in South America, North America, and Asia, with a particular focus on tropical and subtropical biomes. This distribution reflects both the rich biodiversity of these regions and the urgent need for conservation due to rapid landscape transformations.\u003c/p\u003e \u003cp\u003eMost studies focus on entire mammalian communities, providing a more comprehensive view of the landscape's effects on biodiversity. However, these findings underscore the need for more studies focused on individual species to better understand specific responses.\u003c/p\u003e \u003cp\u003eSpecies richness, occupancy, and abundance have emerged as the most studied response variables, providing crucial information on how mammals respond to landscape changes. The increase in occupancy studies reflects advances in analytical frameworks for dealing with imperfect detection.\u003c/p\u003e \u003cp\u003eMetrics related to human disturbances and habitat quantity have emerged as key factors influencing mammal populations, highlighting the need to consider both landscape composition and configuration in conservation efforts.\u003c/p\u003e \u003cp\u003eAlthough approximately half of the studies were conducted in PNA, no significant differences were found compared with studies outside these areas, suggesting the need for conservation strategies that extend beyond PNA.\u003c/p\u003e \u003cp\u003eThis review demonstrates substantial progress in our understanding of how landscape attributes affect populations of medium- and large-sized terrestrial mammals. However, it also reveals significant knowledge gaps, particularly concerning long-term effects, species interactions, and responses to rapid environmental changes, including climate change.\u003c/p\u003e \u003cp\u003eFuture studies should address these gaps by focusing on long-term research, expanding geographic coverage to underrepresented regions, and incorporating interdisciplinary approaches that consider both the ecological and socioeconomic factors driving landscape changes. Additionally, it is crucial that future research translates into more effective conservation and landscape management strategies to protect mammalian biodiversity in an increasingly anthropized world.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that they have no financial or non-financial interests, direct or indirect, that could represent a conflict of interest in relation to the work submitted for publication.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eJ.R.T. thanks the National Council of Humanities, Sciences, and Technologies of Mexico (CONAHCYT) for the scholarship granted (CVU: 206503), which made this work possible. This article presents part of the results obtained in the thesis project for the Doctorate in Sciences in Biodiversity and Conservation of Tropical Ecosystems at the University of Sciences and Arts of Chiapas (UNICACH).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgha M, Batter T, Bolas EC, Collins AC, Gomes da Rocha D, Monteza-Moreno CM, Preckler-Quisquater S, Sollmann R (2018) A review of wildlife camera trapping trends across Africa. 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Global Ecol Conserv 21:e00878. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gecco.2019.e00878\u003c/span\u003e\u003cspan address=\"10.1016/j.gecco.2019.e00878\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Universidad de Ciencias y Artes de Chiapas","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Camera traps, terrestrial mammals, habitat fragmentation, landscape ecology","lastPublishedDoi":"10.21203/rs.3.rs-6116754/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6116754/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTerrestrial landscapes are undergoing unprecedented transformations due to human activities, resulting in habitat loss, degradation, and fragmentation on a global scale. This has severe effects on wildlife, especially on medium- and large-sized terrestrial mammals. Landscape ecology seeks to understand how habitat configuration, quantity, quality, and connectivity impact wildlife populations. This article presents a meta-analysis exploring the effects of landscape attributes and habitat fragmentation on populations of medium- and large-sized terrestrial mammals, highlighting the role of landscape ecology in biodiversity conservation. A total of 180 articles published between 2010 and 2023 were analyzed, selected from scientific databases. Patterns were evaluated in terms of geographic coverage, research topics, response variables, land use, and landscape metrics applied. Most studies were conducted in the Americas, Asia, and Africa, focusing on tropical and subtropical biomes. Of these, 68.89% centered on mammal communities in general. The most frequently studied response variables were species richness (28.45%), occupancy (25.63%), and abundance (12.39%). The most commonly used landscape metrics were related to human disturbances and habitat quantity. Studies were mainly conducted in native forests (77.17%) and areas with agricultural activities (42.39%). This review highlights the growing importance of camera traps in mammalian research and the need to understand landscape effects on their conservation. Species were observed to respond differently to landscape transformation, with some exhibiting ecological flexibility and others experiencing negative impacts.\u003c/p\u003e","manuscriptTitle":"Effects of Landscape Attributes on Medium- and Large Terrestrial Non-Volant Mammals: A Systematic Review of Camera Trap Studies (2010--2023)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-28 05:27:40","doi":"10.21203/rs.3.rs-6116754/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bba1bc4e-eeb1-4f58-b3b2-906b2ec514fa","owner":[],"postedDate":"February 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44931736,"name":"Animal Science"}],"tags":[],"updatedAt":"2025-02-28T05:28:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-28 05:27:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6116754","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6116754","identity":"rs-6116754","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}