The Impact of Land-Use Intensification on the Conservation Management of Native Forest Remnants Embedded Within Production Landscapes

The impact of land-use intensification on the conservation management of native forest remnants embedded within production landscapes

Lisa H. Denmead

B. Sc. (Biology), University of Canterbury, New Zealand

This thesis is presented for the degree of

Masters of Science by Research in Ecology

School of Animal Biology

The University of Western Australia

2012

Declaration

I declare that this thesis is my own account of my research conducted during my period of enrolment at the University of Western Australia for the degree of Master of Science by Research. It has not previously been submitted for a degree at this or any other university. As stated in my Acknowledgements, my research has been assisted by interactions with a number of people, however any work that was shared with supervisors or other collaborators is mentioned below:

Chapter Two: The research completed in this chapter was carried out as part of the larger research project - Riches to Rags: does elevated productivity drive ecosystem decay in adjacent natural habitats. I was fully involved in developing the study design for this project, which I used to answer the questions addressed in chapter two. Collaborators on this project include Raphael Didham, Elizabeth Deakin, Gary Barker, Jason Tylianakis and Louis Schipper. All the field and lab work for this chapter was carried out alongside my fellow student on the project, Elizabeth Deakin. I developed the research questions, carried out the analysis and wrote the chapter with the support of my supervisors Raphael Didham, Rachel Standish and Gary Barker.

Chapter Three: The experiment in this chapter was conducted in one of the forest remnants used in the wider project but was separate from the main aims of the project. I developed the research questions, designed the experiment, carried out the analysis and wrote the chapter with the support of my supervisors Raphael Didham, Rachel Standish and Gary Barker.

Abstract

Land-use intensification is increasing worldwide as the need for resources grows along with the human population. The increased inputs and animal stocking rates that are part of increasing yields in production systems have negative impacts on farmland. However, farmer inputs are not static and can spill-over into adjacent natural systems, sometimes with harmful consequences. Actions taken to spare land for conservation will be compromised if spill-over from surrounding land-use inhibits recovery of the system. In the first half of my thesis I investigated the relative benefits of livestock exclusion for conservation of native forest remnants embedded within production landscapes of varying land-use intensity in the Waikato region, New Zealand. I measured detritivore invertebrate communities and leaf-litter decomposition rates in 11 fenced and 10 unfenced native forest remnants on farmland that varied in land-use intensity. Livestock exclusion was highly beneficial to detritivore communities under all land-use intensities. But surprisingly, the observed variation in detritivore community composition was independent of changes in land-use intensification in both fenced and unfenced remnants and therefore the relative benefit of fencing did not change with land- use intensity. These results have positive implications for land spared for conservation in New Zealand. I tested the mechanistic drivers of livestock trampling impacts on land snail communities in the second half of my thesis, using an artificial trampling experiment conducted in a fenced forest remnant. I used a factorial combination of litter manipulation and trampling treatments to partition different causal drivers of livestock impacts on land snail communities, and relate treatment differences to covariance in five environmental variables which are impacted by livestock. Even the lowest intensity treatment caused severe changes to snail communities. The underlying drivers varied, but are primarily due to changes in litter mass and the effects of unknown mediating variables that were not measured in the experiment. The results suggest that even a minimal amount of stock access will cause significant impacts on snail communities, and should be discouraged. The results also further support the need to maintain livestock exclusion as a priority conservation management action for forest remnants on farmland.

Acknowledgements

Firstly I’d like to thank to my supervisors Raphael Didham, Rachel Standish and Gary Barker. Raph, I have learnt an immeasurable amount from working with you and you pushed me and this thesis further than I ever could have. Also, thanks for giving me the opportunity and supporting my transfer to UWA, it has been an amazing opportunity for me, and I have got so much more out of my MSc because of it. Rachel, thanks a lot for getting so involved, especially after having to jump in part way through, I appreciate the time you take to think over my work and your support throughout this. Gary, thanks for your advice in developing my research, particularly the experiment and for your support over the summers I’ve been based at Landcare Research. And thank you so much for getting through the snail identifications for me, this research could not have been done without you.

I thank my fellow student on the project, and co-author on chapter 2 of this thesis, Liz Deakin, right from the beginning we helped each other through and I’m extremely grateful for that. We went through hell but we survived!

I thank the collaborators on the wider Marsden project, Jason Tylianakis and Louis Schipper for their input and advice.

This research was possible due to financial support from the School of Animal Biology at the University of Western Australia and the Marsden Fund in New Zealand. My stipend was also provided by a University of Western Australia University Postgraduate Award.

Numerous people have helped me in the field and lab. Foremost, I would like to thank our project technician Scott Bartlam, none of this could have been done without him. He went out of his way and far beyond what was expected to help make my work the best it could be. He also taught me numerous skills that will be invaluable to me in my future research. Thanks to our favourite field assistant TK (Terrekia Madden), I know it was killer work some times, but the amount of effort put in was greatly appreciated. I thank Daniel Arnold, for getting through the mechanical hoof experiment with me. I thank Hannah Franklin for help in the field and the lab, and more than that, thanks for helping me get through the summer. Thanks to Josh Van Vianen, Marion Theile, Jayesh Ravji, and Lowell Abellonosa for help sorting invertebrates and Louise Fisk for doing a great job of the soil analysis for us.

I’d like to thank the staff at Landcare Research Hamilton, the support I had during the two summers I was based there was invaluable. Everyone was always so willing to help out if they could. In particular, Marc Dressor thank you for the help designing the mechanical hoof.

I am grateful for all my friends that have supported me along the way, even if it has just been to listen to me complain over and over and over (Daniel Paine), or do overnight rushed jobs for me (Richard Langrish). I’ve been lucky enough to go through my MSc with a great bunch of people from both UC and UWA, providing ongoing support and many of whom I know will remain friends for life. Special thanks should go to Andrew Barnes for the stats lessons, and my lab group at UWA for their advice, support and just being genuinely lovely people!

Last but definitely not least, my family. Mum you are amazing; I couldn’t have done any of it without you. My sister, thanks for the support Nae, you’ve been really awesome through this. Mostly I’m grateful for all the other stuff, but thanks again for helping me get through those samples both of you-I would never have finished them otherwise. Thanks Dad for being there, and for all the advice throughout, and to both you and Ginni for help with the final editing. To my nieces Zoey and Millie, thanks for being you.

Table of Contents

Chapter 1. General Introduction ...... 1

A brief history of global land-use intensification ...... 1

The effects of land-use intensification on biodiversity ...... 2

Multiple drivers of biodiversity loss in habitat remnants embedded within production landscapes ...... 5

Balancing biodiversity conservation and agricultural production ...... 6

Management actions to conserve and restore biodiversity in production landscapes ...... 10

Thesis Aims and Objectives ...... 11

Chapter 2. Do spill-over effects from high intensity agriculture compromise land spared for conservation? ...... 17

Introduction ...... 17

Methods ...... 21

Results ...... 32

Discussion ...... 43

Appendix 2.1 ...... 49

Appendix 2.2 ...... 51

Appendix 2.3 ...... 52

Appendix 2.4 ...... 55

Chapter 3: Minimal livestock trampling has severe impacts on land snail communities in native forest remnants ...... 57

Introduction ...... 57

Methods ...... 61

Results ...... 69

Discussion ...... 76

Appendix 3.1 ...... 81

Appendix 3.2 ...... 82

Appendix 3.3 ...... 83

Appendix 3.4 ...... 84

Appendix 3.5 ...... 86

Chapter 4: The conservation management implications of land-use intensification in New Zealand ...... 87

The ecological benefits of livestock exclusion do not scale with land- use intensification ...... 88

Is land sparing a viable option for balancing conservation and production in New Zealand? ...... 90

Placing the effectiveness of land-sparing for conservation in a global context ...... 93

Conclusions ...... 97

References ...... 99

Chapter 1. General Introduction

A brief history of global land-use intensification

Land-use intensification is increasing worldwide as the need for resources grows with increasing human population size (Matson et al. 1997, Tilman et al. 2002, Fischer et al. 2008, Kleijn et al. 2009). Intensification is typically defined as the increasing yield per unit area in a production system (Turner and Doolittle 1978, Kleijn et al. 2009), and it is associated with increased inputs into the system (e.g. fertiliser, water and stock feed) and increased animal stocking rates. Although the rate of expansion of agricultural land has slowed considerably over the previous three decades (and even decreased in some areas), the rate of increase in yield per unit area has increased rapidly (Rowarth et al. 2006) and has even surpassed the rate of human population growth on a global basis (Naylor 1996, Matson et al. 1997). In Scotland, for example, during the period between 1967 and 1999 wheat yields increased by 201% (Benton et al. 2002) and in Eastern Colorado corn yields have increased by 400 to 500% since 1940 (Matson et al. 1997). Increasing yields were likely achieved through an increase in the use of water, fertiliser, energy, pesticides, stock feed, high- yielding crop varieties and mechanization. Worldwide between 1961 and 1999 the overall crop yield per unit area increased by 106%, but this was coupled with a 97% increase in irrigated land, an 854% increase in pesticide production, and a 638% and 203% increase in the use of nitrogenous and phosphorus fertilisers respectively (Green et al. 2005).

In New Zealand, rapid expansion of agriculture began in the 1800s following European settlement, but since the 1970s expansion has slowed as the land available for conversion to agriculture has diminished (MacLeod and Moller 2006). As with global trends this decrease in expansion rate has been coupled with a dramatic increase in agricultural intensification throughout the country. The continuous trend towards increasing intensification looks set to continue in New Zealand for the foreseeable future unless there are major changes in agricultural policy (MacLeod and Moller 2006, Haggerty et al. 2009). Increasing yields in agriculture in New Zealand have been linked with a twofold

increase in non-nitrogenous fertilisers and a 60-fold increase in nitrogenous fertilisers from 1961 and 2001, and a 4 % per annum increase in land under irrigation (MacLeod and Moller 2006). Conversion of one form of agriculture to another more extractive form (typically to increase profits) has also resulted in increases in intensification and this is becoming a major issue in New Zealand with an increasing amount of land being converted from beef or sheep farming to higher intensity dairy farming (MacLeod and Moller 2006, Lee et al. 2008). The total number of dairy cows in New Zealand increased by 34% between 1994 and 2002, and the land used for dairying increased by 16% (PCE 2004), which represents a general step up to a higher input-higher output land use. This transformation is being driven by a drop in financial returns for sheep and beef and an increase in dairy-company share prices. Increasing intensification is especially pertinent in New Zealand as agriculture is a key component of the economy, accounting for more than 65% of exports and 17% of GDP (Rowarth et al. 2006). At the same time, biodiversity is also highly valued, and contributes to the ability to attract income from tourism, which is also a vital component of the NZ economy (10% GDP). Therefore, there is a growing concern about the long-term sustainability of increasing intensification, how it affects biodiversity, and in particular, the nature of the trade-off between production and conservation.

The effects of land-use intensification on biodiversity

Intensification can alter resource availability and biotic interactions in ecosystems (Matson et al. 1997), leading to decreases in native biodiversity in agricultural landscapes (Crawley et al. 2005). The two key drivers of these ecological responses to land-use intensification are fertiliser inputs and livestock densities, which are associated with grazing and trampling impacts.

Both experimental (Suding et al. 2005, Clark et al. 2007) and observational (Stevens et al. 2004, Dorrough et al. 2006) studies have shown that increases in nutrient inputs cause species declines. Nutrient addition experiments have shown that increased nutrient availability and the resulting increases in productivity can cause dominance by one or a few animal and plant

species leading to losses in biodiversity through competitive exclusion (Rosenzweig 1971, Grover and Chrzanowski 2004, Crawley et al. 2005). The long-running Park Grass experiment in the UK demonstrated that any type of limiting nutrient could cause plant species richness to decrease, and that the greatest losses were when nitrogen and phosphorus were applied together and soil pH was reduced (Crawley et al. 2005). Furthermore, in a survey of 68 acid grassland sites across Great Britain, Stevens et al. (2004) found that for every 2.5 kg/ha/year of N deposition there was an average reduction of one species per 4 m2 quadrat. Although the majority of the studies investigating the impacts of nutrient addition have been focused on plant species, invertebrates have also been shown to be sensitive to changes in nutrient availability (Fenner and Palmer 1998, Boschi and Baur 2007a). For example, an assessment of snail communities in pastures under differing management practices in the Swiss Jura mountains found that cattle pastures without fertiliser addition had higher snail species richness and a greater number of rare snails than pastures where fertiliser was applied annually (Boschi and Baur 2007a).

In animal production systems, another important component of land-use intensification is increasing livestock density, which has both direct and indirect impacts on ecosystems. The direct negative impacts associated with livestock trampling and grazing include soil compaction, reduction in litter accumulation, destruction of topsoil structure, changes in nutrient levels in the soil and reduction in plant biomass (McGlone 1989, Lavado et al. 1996, Jeddi and Chaieb 2010). In an investigation of the impacts of long term cattle grazing on litter and soil organic matter in mixed prairie and fescue grassland ecosystems, Naeth et al. (1991) determined that the proportion of bare ground increased, while the depth of fallen litter and live vegetation cover decreased, with increasing grazing intensity. In turn, these types of livestock impacts can lead to soil erosion, increased run-off, altered soil microbial communities, changes in invertebrate community structure and vegetation composition (Bromham et al. 1999, Pietola et al. 2005). Perhaps more importantly these direct effects of livestock grazing can lead to cascading indirect effects on ecosystem processes such as nutrient cycling (Biondini et al. 1998). For example, Seagle et al. (1992) showed that removal of grazing from the Serengeti grasslands led to an

increase in standing dead biomass and nitrogen immobilization in litter, and Lindsay and Cunningham (2009) observed that removal of grazing increased decomposition rates in Australian woodlands.

Surprisingly, the majority of these land-use intensification impacts have only been investigated in terms of their local on-site effects. For example, there is ample evidence in the literature of direct links between land-use intensification and biodiversity decline on farmland (Krebs et al. 1999, Benton et al. 2002, Kleijn et al. 2009). By contrast, there is a limited amount of research examining the off-site effects of land-use intensification, and what there has been has almost exclusively investigated the impacts on aquatic systems. However, it is well recognised that nutrients and other resources added into agricultural systems may move or “spill-over” into adjacent natural systems through processes such as down-slope leaching (Matson et al. 1997) and aerial drift (Duncan et al. 2008) and yet there has been minimal research considering the off-site effects of intensification on terrestrial ecosystems that are embedded within or adjacent to production systems (Moller et al. 2008). The movement of livestock from pasture into adjacent natural systems also leads to nutrient enrichment, and can further degrade natural systems through grazing and trampling impacts (Duncan et al. 2008, Prober et al. 2011). As a result it is possible increases in agricultural intensification could increase the spill-over effects on habitat remnants embedded within production landscapes. Increasing intensification of land use is likely to be an important driver of global biodiversity decline in the near future (Sala et al. 2000, Millenium Ecosystem Millennium Ecosystem Assessment 2005), both directly through species loss but perhaps more importantly, indirectly through the impact of intensification on ecosystem processes. Therefore, it is important that this lack of knowledge on the impacts of agricultural intensification on biodiversity in production landscapes is investigated.

Multiple drivers of biodiversity loss in habitat remnants embedded within production landscapes

The worldwide increase in land-use intensification can exacerbate the existing threats to biodiversity in habitat remnants embedded within production landscapes. Many of these remnants are already degraded by habitat fragmentation, one of the keydrivers of biodiversity loss worldwide (Sala et al. 2000). However, the ecological impact of fragmentation on the remaining habitat remnants is widely recognized to be dependent on the surrounding matrix (Ricketts 2001), which suggests that changes in land-use intensification in the surrounding matrix could interact synergistically with the effects of fragmentation. Matrix habitats influence habitat remnants through affecting the ability of individuals to disperse between suitable remnants, altered species interactions and spatial subsidies (Fagan et al. 1999, Ewers and Didham 2006a) and these effects are likely to be expressed as altered edge response functions in the remnants.

Edge effects are one of the key impacts to remnants in fragmented landscapes and consequently there is a large body of research on the general principles that determine the magnitude and extent of edge response functions (Figure 1.1) (Saunders et al. 1991, Harper et al. 2005, Ewers and Didham 2006a, Ewers and Didham 2008). However, there are very few studies that have investigated how landscape-level processes, such as variation in land-use type within the matrix, might alter edge responses (Campbell et al. 2011). The limited research thus far though has shown that edge effects are highly dependent on the surrounding matrix (Cronin 2003, Campbell et al. 2011) which provides support for the idea that variation in land-use intensification could cause changes in edge responses in habitat remnants embedded within production landscapes, though this has not been investigated. The ability to predict edge influences in forested ecosystems is an important consideration in the effort toward effective conservation outcomes in fragmented landscapes (Harper et al. 2005).

Figure 1.1 Edge effects can be measured both in terms of edge extent (EE) and edge magnitude (EM) on both the remnant and matrix side of the vegetation boundary (edge). Modified from Didham (2010).

Balancing biodiversity conservation and agricultural production

The severe impact of land-use intensification on farmland biodiversity and the potential for agricultural impacts to exacerbate biodiversity loss in adjacent natural systems raises concerns about the sustainability of high-intensity agriculture. However, there is still a need to address the issue of how to continue feeding a growing human population. A recurring theme in the literature regarding the potential options for mitigating the impacts that will result from increases in production is the land-sparing versus land-sharing debate (Green et al. 2005, Fischer et al. 2008, Ewers et al. 2009, Phalan et al. 2011). As the worldwide food demand continues to increase, the adverse impacts of farming look set to increase along with it. Therefore, land-sparing and land- sharing are essentially two competing solutions to the growing need to balance conservation and land-use intensification. Land-sharing is the notion that more environmentally-friendly farming might be achieved by decreasing agricultural yield and increasing native populations on farmland, but at the expense of increasing land area required for agriculture. Land sparing, by contrast, is the notion that land is spared for conservation, but at the expense of greatly increasing yield on current farmland. 6

The land-sharing approach has had particularly strong support in Europe, with over 25% of farmland in the European Union under some form of agri- environment scheme (Green et al. 2005, Roth et al. 2008). The agri- environment schemes (AESs) in Europe essentially provide support to landowners to manage their farms for the benefit of the environment (Kleijn et al. 2001). Management options could include for instance the implementation of practices that reduce environmental risk (e.g. reduced fertiliser or pesticide inputs) and using practices that help protect biodiversity (e.g. leaving crop stubble over winter to provide food for birds) (Kleijn and Sutherland 2003, European Commission 2005, Green et al. 2005). Farmers join on a voluntary basis, but must adhere to a particular set of management prescriptions (Kleijn et al. 2001). Many long-established agro-ecosystems in Europe have comparatively high biodiversity, and suffer dramatic declines with increasing land-use intensification (Krebs et al. 1999, Benton et al. 2002, Kleijn et al. 2009), which justifies the land-sharing approach to conservation in this region. The degree to which farmland harbours high biodiversity values in less developed regions is not as well studied, but there is some evidence it could in some countries. For example, approximately half of Costa Rica’s native forest species of mammals, birds, butterflies and moths are present in agricultural areas (Green et al. 2005). Although the effectiveness of the AESs in Europe has been debated, there is increasing evidence that they are a valuable tool for protecting and promoting biodiversity (Mayer et al. 2008, Roth et al. 2008) and their implementation is at present considered an essential policy tool through which to reverse biodiversity declines.

In contrast to land-sharing as an option to alleviate the impacts of future increases in production, the land-sparing argument actually promotes the intensification of agriculture, but with the premise that no additional land be converted to agriculture. Land-sparing is widely supported in the agricultural and economic development literature (Waggoner 1995, Avery 1997), as increasing yields from technological innovations have been credited with avoiding the need to convert large areas of unfarmed land to agriculture (Ewers et al. 2009). Advances in irrigation technology in particular, through systems which use less water at lower pressure (therefore requiring less energy), along

with increasingly effective fertiliser and pesticide use have been vital to increases in yield. These advances have led to the prediction that growing food demands can be met without increasing the amount of land in agriculture, at least for the foreseeable future (Waggoner 1995). Without the large increases in yields seen over recent decades, calculations for the United States, China and India show that to produce the same quantity of food as currently grown would require two to four times more crop land than there is at this time (Green et al. 2005). On the other hand, there is little support for a land-sparing approach to mitigate the effects of increased production amongst conservationists (Donald 2004) and recent studies suggest that there is only relatively weak evidence for biodiversity benefits from sparing land for conservation while increasing yields elsewhere, as past increases in yield have not demonstrably spared land for conservation (Ewers et al. 2009).

In New Zealand, conservation efforts have focused predominantly on large, officially-protected natural areas (Norton and Miller 2000), which has led to protection of approximately 30% of New Zealand’s land area. This would suggest that New Zealand should be a model example of the land-sparing approach to reducing biodiversity loss. However, the protected areas are not representative of the full range of ecosystems native to New Zealand. Areas that are the most intensively used by humans are under-represented in the conservation estate (only 18% of lowland areas, under 500m a.s.l., in New Zealand are protected) (Awimbo et al. 1996, Norton and Miller 2000) and areas of little economic value such as mountainous regions make up the majority of protected areas. In lowland areas, which would have been predominantly forested in prehuman times (McGlone 1989), there are now only small forest remnants remaining in most regions (Smale et al. 2008), and the majority of these are on private (mostly unprotected) land. These remnants of indigenous forest are very important as reservoirs of indigenous biodiversity, and provide critical connectivity between larger tracts of forest. The extent of biodiversity loss in lowland New Zealand is severe, therefore the conservation value of even the smallest forest remnants in lowland production landscapes is extremely high, and for many species these remnants are their only habitats (Norton and Miller 2000). For example, approximately 20% of New Zealand’s threatened

vascular plants are found only on private land and for another 60% a high proportion of their population is on private land (Norton 2000). If New Zealand is to restore and conserve a more representative range of lowland indigenous ecosystems, conservation has to occur on private land (Davis and Cocklin 2001).

The high biodiversity value of small forest remnants on private land is becoming more apparent to local authorities and landowners in New Zealand, as evidenced by the increasing number of small privately-owned remnants that have some form of conservation management or protection. For example, as of 2010 the New Zealand Queen Elizabeth II National Trust (QEII Trust 1984), which allows landowners to place a conservation covenant on land in perpetuity, had nearly 109,000 ha protected through over 3,800 landowners (Anon, 2010). The New Zealand government also provides funds to purchase or protect land through the Nature Heritage Fund and Nga Whenua Rahui Trust, and there are also numerous private organisations that purchase land for conservation purposes (Norton and Miller 2000). While this trend is encouraging, protection alone may not be enough in many lowland forest remnants if spill-over effects from the surrounding land-use have an impact on the ability of remnants to maintain biodiversity (Figure 1.2). This is the crucial gap in understanding regarding the true conservation value of land-sparing and consequently the relative conservation benefits of land-sparing vs. land-sharing, as there is little knowledge of how the intensification of farming will affect land adjacent to agriculture. If intensive farming has spill-over effects on surrounding land which is ‘spared’ for conservation then this would weaken any arguments made for the land-sparing approach.

Figure 1.2 Figure depicting the predicted differences in spill-over effects possible from differing levels of agricultural intensification in a) large forest reserves and b) fenced and c) unfenced remnants embedded within production landscapes. Pink arrows depict inputs from surrounding landscape, which in fenced remnants could be nutrients from overland flows and aerial drift, and in unfenced remnants stock from the surrounding pasture and higher nutrient flows.

Management actions to conserve and restore biodiversity in production landscapes

If there are spill-over effects of increasing agricultural intensification on adjacent remnants, then management actions to conserve and restore native forest remnants will become increasingly important. Worldwide, fencing to exclude domestic livestock is an increasingly common management option for the conservation and restoration of native remnants embedded within production landscapes. Significant restoration gains from the exclusion of livestock have been seen globally, and although other management options are often needed to facilitate recovery to the desired state (Prober et al. 2011) it is commonly argued that stock exclusion by fencing is the first requirement of native remnant management (Pettit et al. 1995, Spooner et al. 2002, Smale et al. 2008). Stock exclusion alone led to higher tree and shrub recruitment within 2-4 years of fencing in 47 sites in New South Wales, Australia (Spooner et al. 2002). In the arid Tunisian steppe, exclusion of livestock grazers led to an increase in vital nutrients, water infiltration rate and basal soil respiration, indicating that it is an important management tool to combat desertification in arid and semi arid

ecosystems (Jeddi and Chaieb 2010). Other benefits from livestock exclusion include reduction in soil compaction (Spooner et al. 2002), increases in litter accumulation (Yong-Zhong et al. 2005), reduced weed abundance, and increased native vegetation cover (Prober et al. 2011).

Livestock exclusion is strongly recommended as the priority initial step in the restoration of forest remnants in New Zealand (Porteous 1993). As part of its active management of over 3500 remnants in New Zealand, QEII provides funds to landowners to assist with the cost of fencing remnants. Also, of the two most common management actions applied to the conservation of native remnants in New Zealand (mammalian pest control and livestock exclusion), livestock exclusion has the greatest benefit. Recent research investigating the benefits of livestock exclusion from lowland native forest remnants in Waikato Region, New Zealand showed that livestock exclusion had substantial positive effects on the remnants, including decreases in soil compaction, increases in native plant regeneration, and reduction in the dissimilarity of invertebrate community composition between remnants andnearby forest reserves (Didham et al. 2009, Burns et al. 2011, Dodd et al. 2011).

However, fencing to exclude livestock only mitigates one major potential impact from land-use intensification on native remnants embedded within production landscapes. The increases in farm inputs (such as nitrogenous fertilisers) associated with increased land-use intensification will still impact native communities if the inputs spill-over into the adjacent systems, in which case the relative benefit of fencing may be highly dependent on the intensity of surrounding land-use.

Thesis Aims and Objectives

Overall Aim

The aim of my research is to test the impacts of agricultural intensification on native forest remnants embedded within production landscapes. I will also quantify the relative benefit of conservation management actions for the conservation of native forest remnants subject to different degrees of land-use intensification. Globally, forest remnants play an important part in the

conservation of biodiversity, providing critical habitat (Harris and Burns 2000, Stevenson 2004) and landscape connectivity (Lumsden and Bennett 2005, Manning et al. 2006, Close et al. 2008) for a wide range of threatened species as well as many locally uncommon species. Therefore, understanding how land-use intensification affects remnant natural ecosystems embedded within production landscapes will be crucial if we are to conserve as many of our indigenous ecosystems as possible, while still retaining agricultural productivity.

I will investigate this through two key questions:

1. Does the relative benefit of livestock exclusion differ with increasing land-use intensification?

2. What are the mechanisms underlying livestock trampling impacts on native remnant communities?

I will address these questions by measuring variation in invertebrate biodiversity and leaf-litter decomposition rates in native forest remnants adjacent to farmland subject to different degrees of land-use intensity. I will focus on invertebrates because they represent the dominant component of biodiversity in New Zealand forest systems, and changes in the diversity and composition of invertebrate communities can have considerable effects on key ecosystem services and processes such as litter decomposition, nutrient and water cycling, maintenance of soil health and structure, and pest control (Lavelle et al. 2006). Decomposition rates will be an important indicator of how increasing land-use intensification is affecting a key ecosystem process in native forest remnants. The process of leaf litter decomposition is a vital ecosystem process that is carried out by invertebrates, fungi and microbes. It is crucial for soil formation and the cycling of nutrients. Although definitive answers on diversity-function relationships are still being debated (Lawton 1994, Huston 1997, Striebel et al. 2009), invertebrates promote increased biomass of microorganisms associated with organic debris, break up organic material and aid in converting organic material to inorganic matter. Therefore, changes in the abundance and diversity of invertebrates in leaf-litter can potentially be correlated with leaf-litter decomposition rates (Lindsay and Cunningham 2009).

As well as investigating detritivore ordinal diversity to gain an understanding of general trends in higher-taxon composition across the sampled invertebrate fauna, I will specifically focus on the diversity and distribution of land snails (Phylum: Mollusca, Class: Gastropoda). The New Zealand land-snail fauna is among the most species-rich in the world, with approximately 1400 species (Barker and Mayhill 1999). The densities of land snails in forest sites are also extremely high on a global scale. In some areas of the country, communities of 30-70 species can occur within areas of only a few square metres. The remarkable patterns of diversity in the New Zealand land snail fauna, along with the limited mobility and sensitivity to land-use changes seen in snails elsewhere (Baur and Baur 1995, Boschi 2007), make them an ideal study group to answer the questions I have posed.

Does the relative benefit of livestock exclusion differ with increasing land- use intensification?

If there are spill-over effects of land-use intensification on remnants embedded in production landscapes then these will have implications for the effectiveness of conservation management actions (Figure 1.2) These spill-over effects are most likely to be observed as changes in edge response functions as there is higher likelihood of spill-over at the edge of remnants. Actions taken to spare land for conservation will be compromised if there is spill-over from the surrounding land-use that degrades or inhibits recovery of the system. The intensity in the surrounding land-use would then need to be considered to effectively conserve and restore habitat remnants.

To determine the impacts of agricultural intensification on the effectiveness of livestock exclusion as a conservation management action I collectedleaf-litter samples, and placed leaf-litter decomposition bags across edge gradients in unfenced and fenced remnants embedded within a production landscape varying in land-use intensity.

In Chapter 2, I hypothesise that land-use intensification will decrease invertebrate biodiversity and decomposition rates and increase the slope of edge response functions inadjacent forest remnants. Consequently, the relative

benefit of livestock exclusion on forest remnants will increase with increasing land-use intensification (Figure 1.3 H1). Alternatively, because of the movement of nutrients from the farmland into the remnants, through aerial drift (Close et al. 2008) and down-slope leaching (Matson et al. 1997), removal of livestock grazing alone in high intensity systems might not be sufficient to ensure remnant recovery from grazing disturbance (Figure 1.3 H2).

Figure 1.3 Chapter 2 hypotheses. H0, The benefit of livestock exclusion does not scale with land-use intensification. H1, The relative benefit of livestock of exclusion increases with increasing land-use intensification. H2, The benefit of livestock exclusion is highest at intermediate land-use intensity.

What are the mechanisms underlying livestock trampling impacts on native remnant communities?

One important component of increasing land-use intensification is increased livestock density. Livestock grazing has direct negative impacts on native forest remnants through soil compaction, moisture loss, reduction in leaf-litter accumulation and changes in structural complexity of leaf litter. Previous research has shown that livestock trampling results in strong negative effects on the diversity and abundance of land snails (Didham, Laliberte and Barker, unpub), however it is unknown how trampling causes these changes to land

snail communities and if there are thresholds under which trampling does not impact land snails.

To discriminate the mechanistic drivers of livestock impacts on snail communities I conducted a factorial field experiment, manipulating leaf litter mass (to simulate changes in litter mass due to grazing) and soil compaction using litter removal and a mechanical hoof. I also measured various environmental variables at the experiment sites that were the most likely underlying drivers of snail community composition.

In Chapter 3 I will test the hypothesis that changes in the litter mass and complexity resulting from trampling will have more impact on snail communities than any changes in soil compaction. Also, I hypothesize that increases in trampling events will lead to negative impacts on land snail communities.

Significance

The results of this research will be important for determining if the dramatic increases in land-use intensification happening worldwide are not only having extensive on-site effects but also, negative off-site effects on native remnant vegetation embedded within production landscapes. This will enable conclusions concerning the effectiveness of the apparent land-sparing trajectory New Zealand is constrained to for the conservation of native biodiversity. Furthermore, conclusions drawn in New Zealand may be relevant to agricultural landscapes elsewhere that have similar land-use histories and approaches to conservation.

Chapter 2. Do spill-over effects from high intensity agriculture compromise land spared for conservation?

Introduction

With the human population projected to exceed 9 billion and demand for food projected to double by 2050 (Green et al. 2005, Fischer et al. 2008, Clough et al. 2011), the need for increased agricultural production is only going to become more critical. However, the dramatic worldwide increase in agricultural intensification that has been required just to keep pace with current human population growth is already having severe negative impacts on biodiversity and ecosystem functioning (Tilman et al. 2002, Foley et al. 2011). There are increasingly well-documented direct links between declining biodiversity on farmland and factors such as increased pesticide use, irrigation, fertiliser application, animal stocking rates, which are intimately tied to intensification of agricultural systems (Green et al. 2005, Kleijn et al. 2009). Moreover, as land- use intensifies these impacts are becoming more severe and as such land-use intensification is predicted to be one of most significant drivers of biodiversity loss in the near future (Sala et al. 2000, Millennium Ecosystem Assessment 2005, Foley et al. 2011).

The severe impacts of land-use intensification on farmland biodiversity raise serious concerns about the sustainability of high-intensity agriculture. Although there is a general consensus that hard trade-offs between production and environment will be impossible to avoid if we are to ensure food security into the future (Foley et al. 2007), exactly what the best approach is to manage these trade-offs so that we minimise environmental degradation and biodiversity loss is a topic of contentious debate (Green et al. 2005, Clough et al. 2011, Phalan et al. 2011). On the one hand, the ‘land sparing’ ethic promotes maximum intensification of farming practices to increase yield on current farmland, coupled with the notion that such intensification will spare biodiversity on adjacent land that is set asidefor conservation (Fischer et al. 2008). On the other hand, the ‘land sharing’ ethic promotes more environmentally-friendly farming practices that balance greater populations of native species on 17

farmlandagainst lower agricultural yield, but at the expense of increasing the total land area required for agriculture (Clough et al. 2011). Landsparing and landsharing are essentially two extremes of a continuum of competing solutions to the growing need to balance conservation and production, and the pros and cons of the two approaches have been debated extensively in the literature (Green et al. 2005, Fischer et al. 2008, Ewers et al. 2009, Phalan et al. 2011).

Most conservation biologists arguing in favour of land-use intensification are not doing so because they support biodiversity loss on farmland, but because they believe that the net benefits of local intensification for regional or global biodiversity are greater than converting an even larger area of land into low-intensity agriculture. However, this argument has been criticised for at least two major reasons. First, land-use intensification in one location has not been shown to lead to an increase in the land spared for conservation in other locations (Ewers et al. 2009), and second because losses of biodiversity and ecosystem services on intensively-farmed land do not necessarily outweigh the benefits of setting aside land for conservation (Clough et al. 2011). What has largely been ignored is a third, potentially more important, limitation on the general utility of land sparing for conservation. Land sparing can only be an effective method for balancing production-biodiversity trade-offs if the requisite increases in local intensification do not have spill-over effects into the surrounding land that is spared for conservation. If there are substantial spill- over effects from intensive agriculture then protection alone may not be enough to conserve biodiversity on adjacent lands. Substantial spill-over would severely undermine land sparing as a possible option for balancing conservation and production in agricultural landscapes in the future.

Although potential spill-over from intensive agriculture has not yet been recognized as a major issue in the land sparing versus land sharing debate, there is substantial evidence from recent ecological studies that spillover of nutrients, resources and organisms between adjacent ecosystems can have large-scale impacts on the dynamics of recipient ecosystems (Polis et al. 1997, Rand et al. 2006, Gladbach et al. 2010). For example, there is evidence forincreases in the biological control of agricultural pests by natural enemies as the area of natural source habitats in the surrounding landscape increases

(Landis et al. 2000, Rand et al. 2006). Equally, in agricultural systems, farmer inputs of nutrients, chemicals, water and livestock are not static and can move or spill-over from production land into adjacent natural systems (Matson et al. 1997), sometimes with deleterious consequences. Spill-over can occur in a number of forms, such as movement of nutrients from fertiliser inputs via aerial drift or down-slope leaching, and from livestock that roam into adjacent natural areas for shelter and food (Matson et al. 1997, Duncan et al. 2008, Prober et al. 2011). Therefore, agricultural intensification has the potential to increase anthropogenic spill-over effects into semi-naturalhabitat remnants embedded within production landscapes. Surprisingly, though, there has been relatively little research investigating these off-site effects of land-use intensification (Moller et al. 2008). Where research has occurred it has primarily been focused on the measurement of agricultural impacts on biodiversity and water quality in spatially-coupled aquatic systems rather than in embedded terrestrial systems (Harding et al. 1998, Berka et al. 2001, Ewers et al. 2009).

The off-site effects of high-intensity farming are most likely to manifest through the increasing magnitude or extent of edge effects into adjacent semi- natural habitat remnants. There is theoretical and empirical evidence that the structure and dynamics of the surrounding land-use matrix have significant ecological effects on biodiversity and ecosystem functioning within habitat remnants (Ricketts 2001, Murphy and Lovett-Doust 2004). For example, in the mid-west USA, Cronin (2003) found that densities of female parasitoid wasps were 59% lower on the edge than within the interior of native prairie cord grass patches embedded in a mudflat matrix, whereas there was no edge response within patches embedded in a matrix of native grasses. Similarly, Campbell et al. (2011) , found that variation inland-use type (i.e., pasture or plantation forest) adjacent to native forest remnants in New Zealand altered the edge response functions for over 80 % of all beetle species tested. These studies on organism response to variation in matrix land-use type provide some support for the idea that variation in land-use intensification in the surrounding production matrix might act synergistically with the impacts of habitat fragmentation to exacerbate edge effects in habitat remnants embedded within production landscapes, although this has never been explicitly investigated.

In this study, I test whether small forest remnants spared for conservation purposes within agricultural landscapes in New Zealand experience more severe edge effects from the spill-over of anthropogenic impacts from adjacent high-intensity compared with low-intensity farming systems, and whether the relative benefits of conservation management actions to mitigate spill-over effects vary with surrounding land-use intensification. New Zealand production landscapes are the ideal study system in which to test the potential off-site impacts of land-use intensification on land spared for conservation for two main reasons. First, there is a historical dichotomy in New Zealand land use between conservation and production, because even though over 30% of the country’s land area is under formal conservation protection, the majority of this is in unproductive upland and montane areas, and the remaining 70% of the country is almost entirely given over to production (Norton and Miller 2000). Second, there are relatively few initiatives towards wildlife-friendly farming in New Zealand and it has traditionally played a negligible role in agricultural reform. Moreover, there are typically low levels of native biodiversity on farmlands developed in the historically-forested lowlands of New Zealand, so it is unlikely that wildlife-friendly farming would have the same benefits seen elsewhere in the world even if it were widely implemented (e.g., Europe: Kleijn et al. 2009). Consequently, great conservation importance is placed on protecting biodiversity in small remnants of native forest within lowland farming landscapes where there are few large nature reserves (Norton and Miller 2000, Didham et al. 2009). Spill-over effects from surrounding production lands would greatly compromise any efforts to conserve these small native forest remnants, yet there has been no research to determine if the intensity of land-use practices in the surrounding farmland are impacting the effectiveness of conservation management actions applied to forest remnants in New Zealand.

I measured detritivore invertebrate communities and leaf-litter decomposition rates in 11 fenced and 10 unfenced native forest remnants embedded within production landscapes that varied in land-use intensity from zero fertiliser inputs and low stocking rates, to production landscapes with very intensive farming systems characterised by high inputs and high stocking rates. I focused on detritivorous invertebrates because invertebrates are a dominant

component of forest biodiversity in New Zealand, and are important drivers of leaf-litter decomposition and nutrient cycling processes in forest ecosystems. Comparing the community composition, species richness, and abundance of detritivore communities as well as an associated ecosystem function across the remnants will help determine the impact of land-use intensification on adjacent natural systems, and subsequently help ascertain the true conservation value of fenced forest remnants. Ultimately these data will be used to assess the potential for land-sparing as a viable option for balancing conservation and production in New Zealand.

Methods

Study Area

The study was conducted on farmland in the Waipa District located in the Waikato Region of the North Island, New Zealand (Figure 2.1). A majority of land in the Waikato Region was cleared of native forest and scrub cover, and converted to pastoral production in the early 20th century. For example, of the 145,000 ha of forest in the Waipa district that existed prior to human settlement, only 6% now remains (Ewers et al. 2006). Because vegetation clearing was selective, most of the remaining vegetation is in areas that were considered less valuable for agriculture such as gullies, steep slopes and rocky terrain (Burns et al. 2011). Furthermore, a high proportion of remaining forest remnants are small (<5 ha) dominated by tawa (Beilschmiedia tawa), on moderately rolling hill country (100–400 a.s.l.) (Didham et al. 2009). Grazing livestock (predominantly cattle and sheep) have had unrestricted access to most forest remnants, but in the last 10–20 years it has become increasingly common for farmers to fence remnant vegetation to exclude livestock. The Waikato Region follows the general trends seen in the rest of New Zealand over the past 40 years, with rapidly increasing intensification of land-use and a shift to higher input-higher output farming systems, dominated by dairy farming (PCE 2004, MacLeod and Moller 2006).

R NEW ZEALAND

Figure 2.1.Map of the study area in the Waipa district, Waikato Region, New Zealand, showing the spatial location the 21 forest remnants and three forest reserve reference sites (R) in red. Map courtesy of Liz Deakin.

Study design

The Land Environments of New Zealand (LENZ) classification (Leathwick et al. 2003) was used to identify remnants across the Waipa District with similar underlying geomorphology, soil types, and vegetation cover dominated by tawa (Beilschmiedia tawa) forests. Spatial data layers were then created in geographic information systems software geographic(ArcGIS Version 9, ESRI 2009) to select candidate forest remnants (50-100) to visit and assess their suitability as study sites. This selection was constrained using slope (<20°) and remnant area (>1 ha) criteria. Site visits and interviews with the landowners were carried out to further determine the suitability of individual forest remnants for the project, based on a set of criteria including size, whether or not they were unfenced or fenced (for a minimum of 10 years), availability of pasture upslope of the remnant (to keep consistent the effects of overland flows of nutrients) and slope in the sampling zone. From this pool of suitable remnants, a final selection was made so that fenced and unfenced remnants were distributed across farms with a wide range of farming types likely to vary in land- use intensification. Landowners were then asked to fill out a more detailed questionnaire relating to their farming practices, including information on stocking rates, fertiliser inputs and use of remnants for stock grazing and shelter, and detailed soil nutrient geochemistry analyses were undertaken.From the resulting information, 11 fenced and 10 unfenced forest remnants were finally selected for study (Table 2.1, Figure 2.1).

Three large forest reserves in the same district were selected as reference sites. These were TeMiro Scenic Reserve (402.8 ha), Maungakawa Reserve (965 ha) and TeTapui Reserve (1377 ha). By comparison with the study remnants, the reference sites were relatively unaffected by surrounding land-use, and while they not pristine they are the best examples of intact tawa forest in the district.

Sampling design Within each of the 21 forest remnants, a 20 m wide × 46.5 m long zone was marked perpendicular to the forest edge in which all sampling was conducted.

In this zone, five sampling points were established at 0, 3, 9, 27 and 46.5 m on a log3 scale from the forest edge to the forest interior. A logarithmic series of sampling distances was chosen because the rate of change in invertebrate communities is frequently greatest close to the edge (Didham et al. 1998, Ewers et al. 2007). As the target organisms for the study were leaf-litter dwelling invertebrates, the edge (0 m) was defined as the edge of the leaf-litter accumulation zone, which in most cases was the canopy drip line (Murcia 1995) but in some fenced remnants where the drip line fell outside of the fence then ploughing and grazing meant that it was more appropriate to define the edge as the fence line. The maximum distance sampled along the edge gradient reflects the maximum distance from edge to interior in the smallest remnants.

The same sampling design was employed in the three reference forest sites, except that the log3 edge gradient was extended to eight sampling points, including 81 m, 243 m and 420 m inside the forest, in order to better describe interior forest conditions.

Quantifying land-use intensity

Detailed information on farming practices on the land surrounding the 21 native remnants was gathered via semi-structured interviews with farmers. I asked for details of exact amounts of all types of fertiliser used, and a detailed history of stocking rates in the pasture immediately upslope of each remnant. With this information I calculated the rates of nitrogen (kg), phosphorus (kg) and lime (tonne) application per ha per year, which I determined through the concentration of each nutrient in each type of fertiliser used.

To complement the information obtained via farmer interviews, I measured the chemical and physical properties of soils under the pasture upslope from each remnant. At 46.5 m into the pasture directly upslope from the remnant, which was the location that mirrored the maximum distance sampled inside the forest remnants, I collected 18 soil cores 2 cm in diameter to a depth of 10 cm at random points along a 20 m sampling line running parallel to the remnant edge. The 18 cores were randomly allocated to one of three bulked replicates of six cores each so as to minimise spatial heterogeneity across

individual cores, while taking into account the logistical constraints of analytical costs. Each of the three bulked replicates was analysed for pH, total C, total N, C:N ratio, delta 15N, Olsen P, total P, total Cd, and total U using standard laboratory protocols (see Appendix 2.1 for details). In addition, three replicate 9.8 cm diameter soil cores were taken to a depth of 7.5 cm in order to measure soil bulk density at randomly-selected points along the same sampling line as the soil cores were taken. The soil nutrient geochemistry measures derived from the soil cores were then standardised volumetrically using the average of the three bulk density measurements

The nine soil nutrient geochemistry measures and the four farmer input measures (i.e. stocking rate, N input, P input, and lime input) were then incorporated into a principal components analysis (PCA) to create a composite index of land-use intensification (Figure 2.2a). In the PCA ordination, axis 1 explained 35.84 % of the variance in component measures of land-use intensity across farms, and axis 2 explained a further 17.24 % (Figure 2.2a). The axis 1 score of each remnant was subsequently used as an index of land-use intensity (re-scaled so all values were between 0 and 10) (Table 2.1). Increasing values of the land-use intensity index equate to increasing levels of total N, total C, C:N ratio, Delta 15N, Olsen P, total P, Cd, U, N input, P input and stocking units, and decreasing values of pH and lime input (Figure 2.2b). The five variables that contributed the most to site ordering along axis 1 were total P (16.0 %), U (12.9 %), N input (11.5 %), Cd (11.5 %), and Olsen P (11.0 %), and the five that contributed most to site ordering along axis 2 were pH (27.8 %), delta 15N (18.0 %), total N (16.6 %), total C (15.0 %) and stocking units (9.6 %). There were no significant correlations between potential confounding patch-level variables (elevation, size of remnant, age of fencing, slope of the pasture above each remnant, or type of farming) and the composite index of land-use intensity (Appendix 2.2), allowing robust inference of land-use intensity effects independently of known (potentially confounding) gradients of other extrinsic variables.

Table 2.1 Characteristics of the 21 forest remnants and 3 forest reserves sampled in the study, including remnant area (ha), number of years fenced, elevation a.s.l. (m) (measured at the forest edge sampling point), slope (˚) (measured from the forest edge, 0m, to 46.5m upslope into the pasture), farming type (DG = dairy grazing; S = sheep; B = beef; DM = dairy milking; BL = bulls) and a land-use intensity (LUI) index (axis 1 scores of PCA anaylsis of farmer inputs and farmland soil geochemistry measures, scaled in ordination units).

Remnant area Years fenced Elevation Slope Farming type LUI Fenced F1 16.0 18 282 8 DG 0.50 F2 4.7 30 210 10 S + DG 1.37 F3 10.0 20 174 5 B + DG 2.50 F4 10.3 30 353 16 S + B 2.92 F5 2.5 30 250 15 S + DG 3.07 F6 3.7 18 237 4 DG 4.27 F7 2.7 30 161 23 BL 5.19 F8 8.6 30 257 29 BL 5.98 F9 2.9 12 340 14 B + DG 6.57 F10 6.0 14 221 3 DM 7.10 F11 4.4 10 227 10 DM 9.94 Unfenced U1 16.0 0 265 12 S + B 1.57 U2 3.2 0 247 17 S + B 1.97 U3 2.2 0 217 15 S + B 2.32 U4 2.1 0 207 6 S + DG 2.39 U5 4.0 0 270 16 S + B 3.27 U6 2.3 0 292 20 DG 3.41 U7 9.5 0 300 16 B + DG 3.57 U8 2.4 0 305 18 S + B + DG 4.15 U9 4.3 0 305 20 S + B + DG 4.69 U10 3.0 0 228 3 S + B 6.22 Reserves R1 965.0 30+ 410 -15 S + B 1.84 R2 409.0 30+ 322 -16 S + B + DG 3.22 R3 1377.0 30+ 280 -16 S + DG 4.41

(a)

(b)

Figure 2.2 (a) PCA ordination of nine measures of soil nutrient geochemistry (pH, total C, total N, C:N ratio, delta 15N , Olsen P, total P, total Cd, and total U) and four measures farmer inputs (stocking rate, N input, P input, and lime input) onto pastoral grazing land surrounding the 21 forest remnants and three forest reserve sites (Table 2.1). Axis 1 scores were subsequently used to define a land-use intensity gradient across sites. (b) Factor loadings (correlations) of each factor (nine soil nutrient measures and four farmer input measures) in the PCA ordination. Increases in the land-use intensity gradient equate to increases in the factors with positive axis 1 scores and decreases in the factors with negative axis 1 scores. 27

Sampling for leaf-litter invertebrates

Four leaf-litter samples were collected at each of the five sampling distances in each of the 21 remnants, as well as at the eight sampling distances in each of the three reference sites (516 samples in total). At each distance from edge, the four samples were collected at randomly-selected points within the 20 m wide sampling zone parallel to the forest edge. Sampling was stratified over the course of the sampling period to account for seasonal variation, with two of the four leaf-litter samples collected at each site between the 18th January and 19th February 2010, and the remaining two collected between 17th February and 26th March 2010. On any given sampling date, the two samples were collected from all five (or eight) sampling distances within a single remnant (or reserve) on the same day. Within logistical constraints of farm access on a given day, the remnants sampled on any given date of collection were also randomly assigned.

Leaf-litter samples were hand collected from a 33 cm-diameter circular frame (0.086 m2) (Didham et al. 2009). All leaf litter and friable humus was scraped from inside the frame as rapidly as possible to reduce invertebrate escape, and placed into a large bag containing a sieve. Samples were sieved at the field site over a 10 mm mesh to obtain a fine “bottom fraction” of sieved litter containing invertebrates, and a coarse “top fraction” containing a negligible number of invertebrates.

The invertebrates were subsequently extracted from the sieved “bottom fraction” of leaf litter in the laboratory using Berlese funnels (BioQuip® collapsible bag design #2832, Rancho Dominguez, California) over a 72 hour period (Wheeler and McHugh 1987). Invertebrates were sorted, identified and counted to Phylum, Class and Order. Detritivores considered further in this thesis were the five taxonomic groups, Mollusca (only those extracted from the Berlese funnels), Amphipoda, Annelida, Diplopoda, and Isopoda.

After sieving and invertebrate extraction the top and bottom fractions of litter were oven-dried at 60 ˚C for 48 hours (McClaugherty et al. 1985) and weighed to obtain a combined estimate of litter mass. The bottom fraction of litter was then hand searched for land snails using a stereo microscope, as land snails are not reliably extracted using the Berlese funnels. All land snails, 28

including juveniles, were identified to species where possible, by reference to authoritatively-identified material (including vouchers for undescribed species) held in the Museum of New Zealand Te Papa Tongarewa (NMNZ). Nomenclature was standardised to Spencer et al. (2009).

Measuring rates of leaf-litter decomposition

Leaf-litter decomposition was measured using the litter bag technique (Falconer et al. 1933). The leaf-litter (hereafter litter) bags were 10 cm x 10cm and made from nylon mesh with a 2 mm x 2 mm mesh size, and numbered with a metal tag attached with wire. Each bag contained a known dry weight (2.0 +/- 0.05 g) of tawa leaves. Fresh leaves were collected from tawa trees within TeMiro Reserve (one of the reserve sites), then dried to a constant weight at 60˚C before being put into the bags. Four litter bags were placed at random points within a 20 m zone parallel to the forest edge at each of the five sampling distances in each remnant and eight sampling distances in each reserve (516 bags in total). When the litter bags were placed in the remnants, forest floor leaf litter was carefully removed and the litter bag placed on the forest floor, in contact with the soil, and then the forest floor leaf litter was placed on top of the bags. The litter bags were attached with nylon fishing line to a nearby tree stump or large log to prevent displacement by livestock. All the litter bags were put out into the remnants between the 18th January and 28th January 2010, and then harvested between the 20th June and 23rd June 2010. In the laboratory, litter bags were visually inspected and any debris or soil that was attached to the outside of the bag was carefully removed. The tawa leaves were then removed from the litter bag, placed in a paper bag, and oven dried to a constant weight at 60˚C (McClaugherty et al. 1985) and weighed. Previous research using tawa leaf litter bags in similar forest remnants in the Waikato suggested that 6 months was a suitable amount of time to determine variation in litter decomposition rates along forest edge gradients, although patterns in relation to land-use intensification were not specifically addressed in that work (Barker, Watts and Didham unpub).

Statistical analyses

Nine response variables were measured to describe variation in land snail community composition, species richness and abundance, detritivore community composition and abundance, and litter decomposition rates within and between remnants. I then tested the effects of land-use intensification, livestock exclusion and edge effects on each of the response variables.

Response variables The number of individuals of land snails per sample (from both Berlese extraction and hand sorting) combined (hereafter referred to as snail abundance) and the amount of litter per sample (litter mass) were converted to no. and mass m-2 respectively before analysis. The number of snails per kilogram of litter mass (snail abundance kg-1) was also calculated as the amount of litter was predicted to be one of the key components of microhabitat structure likely to affect land snail abundance (Barker and Mayhill 1999).

As well as absolute land snail species richness, rarefied species richness was calculated for each sampling distance in each remnant to control for the large variation in abundance observed among samples. Rarefied species richness (species richness standardised for number of sampled individuals) was calculated using Biodiversity Professional software (BD Pro, version 2.0; (McAleece et al. 1997)). The completeness of the sampling effort and species richness estimations were evaluated for the land snail community using sample- based species accumulation curves, re-scaled to both number of individual snails sampled and litter mass sampled using EstimateS version 8.2.0 (Colwell 2005).

Detritivore community composition (abundance of detritivore taxonomic groups in Berlese extraction) and land snail community composition (abundances of land snail species in Berlese extraction and hand sorting, combined) were compared within and among sites using non-metric multi- dimensional scaling (NMDS) on square root transformed data using the Bray Curtis similarity index in Primer v.6 (Clarke 2006). The similarity of community composition between remnant sites and reference sites was then determined

from the underlying similarity matrix using the average pairwise similarities between each edge distance in each remnant and the interior (420 m) forest site at each of the three reserves. Variability or dispersion of land snail and detritivore community composition data between fenced, unfenced and reference sites was also tested in Primer using permutational analysis of multivariate dispersions (PERMDISP, (Anderson 2006)

Leaf-litter decomposition rates were calculated as the initial dry weight of the tawa leaves in the litter bags minus dry weight at harvest. Results are presented as proportion mass loss over the 6 month period.

Determining invertebrate community response to land-use intensification, livestock exclusion and edge effects I tested the influence of land-use intensification, livestock exclusion, and distance from edge on invertebrate communities and decomposition rates using Generalized Linear Mixed Models (GLMMs). Land-use intensification, distance from edge (with linear and non-linear terms as appropriate), livestock exclusion (fenced or unfenced) and their interactions were included as fixed effects, and remnant identity was included in the model as a random effect to account for stochastic variation in remnant-level characteristics that were not controlled by site selection procedures. I hypothesised that increases in land-use intensity would increase spill-over effects in adjacent forest remnants leading to decreased detritivore diversity and decomposition rates, particularly at the forest edge, resulting in increases in the slope of the edge response functions. In the GLMMs, a Poisson distribution was specified for count data (absolute number of land snail individuals, absolute number of detritivore individuals, number of land snail individuals kg-1, absolute number of snail species, rarefied number of snail species, and litter mass (g)) and a binomial distribution was specified for proportion data (land snail community similarity to that in reference sites, detritivore community similarity to that in reference sites and litter decomposition rate). For models where there was significant over-dispersion of the data, a Poisson-log normal distribution was applied by adding an observation level vector to the model as a random factor (Elston et al. 2001).

To determine which of the factors in the model were the most important determinants of each response variable I took an information-theoretic approach to model simplification (Burnham and Anderson 2002). This approach allows comparison of multiple models with reduced subsets of fixed factors and their interactions, enabling the most parsimonious model to be identified. This is achieved through fitting each model to the data and then ranking them by AICc scores (small sample size corrected AIC scores). The model with the smallest AICc score is considered the model with the highest likelihood of explaining the response variable. Models with AICc scores that are within two units of each other are considered to have equal likelihood of being the model that has the most explanatory power. I also calculated the relative probability of each model being the best model by calculating their Akaike weights. The Analyses were performed in R version 2.13.1 using the nlme and lme4 packages (R Development Core Team 2008).

Determining edge response functions To determine the nature of interactions between land-use intensification and distance from edge in the GLMMs, edge response functions were calculated to enable the comparison of the slope of the functions among remnants. The slope of the response function determines the rate of change in the response variable from edge to interior (e.g. a positive slope would indicate that the response variable increased from edge to interior). The response functions were calculated using the statistical approach of Ewers and Didham (2006b), which calculates the best-fit response function out of five response functions of increasing complexity (null, linear, power, logistic and unimodel).

Results

Variation in detritivore invertebrate communities within and among sites

A total of 15 901 detritivore invertebrates in five higher taxonomic groups were extracted from 516 leaf-litter samples. Sample abundances ranged from 0 to 290 individuals (mean ± 95% CL = 27.9 ± 3.2 individuals). The five taxonomic groups were Mollusca (892; land snails extracted through hand sorting were not 32

included in this total), Amphipoda (7030), Annelida (1829), Diplopoda (4699), and Isopoda (1451).

An ordination of community similarity between sites showed differences among detritivore invertebrate communities in fenced, unfenced and reserve sites (stress=0.15; Figure 2.3 a). Also, the variation across the unfenced remnants was much higher than in either the fenced sites (PERMDISP, t- statistic=6.96, p-value=0.001) or the reserve sites (PERMDISP, t-statistic =7.75, p-value=0.001) (Figure 2.3 a).

Only 892 land snails were obtained from Berlese extraction of the 516 leaf-litter samples, but hand-searching of the dried residues from these samples identified a further 14 039 individuals, giving a combined total of 14 921 land snails in a total of 85.5 kg dry mass of litter samples across all sites. The number of individuals per sample ranged from 0 to 386 (mean ± 95% CL = 28.9 ± 4.5 individuals), and the number of species per sample ranged from 0 to 74 (mean ± 95% CL = 11.9 ± 1.3 species). A total of 141 species were identified, representing 12 families (Appendix 2.3)

Sample-based species accumulation curves calculated for fenced remnants (n=220 samples), unfenced remnants (n=200 samples) and forest reserves (n=96 samples), and rescaled to amount of litter collected (Figure 2.4 a), suggested that the level of sampling effort imposed almost fully captured the richness and composition of land snail communities in fenced remnants and reserves, but sampling was incomplete for unfenced remnants where land snail densities per unit mass of litter were substantially lower. For an equivalent mass of litter sampled (16024 g), there was a highly significant difference in the number of species sampled, with fenced remnants having 30 % (32) more species and reserves having 44 % (49) more species than the number sampled in the unfenced remnants (Figure 2.4 a).

While the number of samples collected was similar between fenced and unfenced remnants, the total number of land snail individuals found in the fenced remnants was approximately five times the number of individuals found in the unfenced remnants (Figure 2.4 b). This result is particularly apparent when comparing the average density of individuals in forest reserves at 1083 m- 2, with the average densities in fenced remnants at 271 m-2 and in unfenced 33

remnants at only 59 m-2. Nevertheless, the species accumulation curves rescaled to number of individuals sampled, suggested that there were no significant differences in estimated values of species richness at a common sample abundance (1008 individuals) (Figure 2.4 b). Taken together (Figure 2.4 a, b), these patterns suggest that although there are large differences in the number of individuals per sample and per kilogram of litter mass in fenced, unfenced and reserve sites, the fundamental underlying distribution of common and rare species (the community species-abundance distribution) must be unaltered across sites, with species loss from unfenced reserves proportional to their population abundance.

As with the patterns observed for detritivore communities in general (Figure 2.3 a), land snail community composition varied dramatically between reserves, fenced remnants, and unfenced remnants (Figure 2.3 b). Land snail communities within the unfenced sites, in particular, were very distinct from those in the reserve sites, and finally, variability across the unfenced remnants was much higher than variability in either the fenced sites (PERMDISP t- statistic=5.36, p-value=0.001) or the reserve sites (PERMDISP t-statistic=10.16, p-value-0.001)(Figure 2.3 b).

(a)

(b)

Figure 2.3.Variation in (a) detritivore and (b) land snail community composition within and among remnant sites in an NMDS ordination (based on square root transformedabundance data and a Bray-Curtis distance metric).Unfenced remnant sites are denoted by open circles, fenced remnant sites by black circles, and reserves by crosses. Stress value from 2D NMDS.

(a)

(b)

Figure 2.4 Comparison of land snail species richness among remnant types (fenced, unfenced, reserve), while controlling for variation in sampling effort, shown as sample-based species accumulation curves rescaled toamount of litter sampled (a) and number of individualscollected (b). Fenced remnants are shown by the solid red line, unfenced remnants by the green long dash line, and reserves by the black short dash line. The vertical dotted line represents a common sampling effort of (a) 16024 g of litter sampled, and (b) 1008 individuals. Error bars have only been added for every 20th sample for clarity.

Livestock exclusion benefits invertebrate detritivore communities in native forest remnants

After model simplification in the GLMM analyses, livestock exclusion was consistently the most important variable explaining variation in detritivore density per unit area and detritivore community composition, as well as land snail density per unit area, species composition andspecies richness across forest remnants (Figures 2.5 a, b, c, d, e, g, Table 2.2 a, b, c, d, e, g and Appendix 2.4 a, b, c, d, e, g, respectively). Total detritivore density was on average 2148 individuals m-2 in fenced forest remnants, but only 550 individuals m-2 in unfenced forest remnants (Figure 2.5 a); a 74.4% reduction in average density. Detritivore community similarity to that measured in reference sites was on average 60% for fenced forest remnants (across all edge distances), but only 42% for unfenced forest remnants (Figure 2.5 b). Moreover, in the species- level analyses, land snail density in fenced forest remnants was on average 271 individuals m-2 compared with 59 individuals m-2 in unfenced remnants (Figure 2.5 c); a 78.2% reduction in average density. Also, land snail community similarity to that measured in reference sites was on average 33% in fenced forest remnants, and only 11% for the comparable data in unfenced remnants (Figure 2.5 d). In contrast, there was no significant impact of livestock exclusion on land snail density per unit litter massand rates of leaf-litter decomposition (Figure 2.5 f, h, Table 2.2 f, h).

Although livestock exclusion effects were evident predominantly at the ‘whole- remnant’ level for most response variables, there was also a significant fencing by distance-from-edge interaction term in two most parsimonious (final) GLMM models for land snails (response variable = rarefied species richness; Table 2.2 g). The slope of edge responses were consistently lower for rarefied species richness of land snails in unfenced remnants than the slope of the edge responses for land-snail rarefied species richness in fenced remnants, suggesting there was less change in land-snail rarefied species richness with distance from edge in unfenced compared with that in fenced remnants. None of the other response variables showed statistically-significant differences in edge responses between fenced and unfenced remnants (Table 2.2 a, b, c, d, e, f, h). However, the main effect of distance from edge contributed significantly

to the best-fit GLMMs (AICc<2) for detritivore density per unit area, and land snail density per unit area, community composition, density per unit litter mass and species richness (Table 2.2 a, c, d, f, g). These response variables increased with increasing distance from edge (Appendix 2.4 a, c, d, f, g). Decomposition rate and detritivore community composition were unaffected by distance from edge (Table 2.2 b, h and Appendix 2.4 b, h).

Detritivore and land-snail communities are only weakly impacted by increasing land-use intensity

Land-use intensification did not contribute significantly to the best-fit GLMMs (AICc<2) for detritivore density, detritivore community composition, land snail density, land-snail species richness, or litter decomposition (Table 2.2 a, b, c, e, f, h), but did contribute significantly to one or more best-fit models for land-snail community composition and land-snail rarefied species richness (Table 2.2 d, g). For land-snail rarefied species richness, land-use intensification contributed significantly to only one of the four most parsimonious GLMMs that had equivalent explanatory power (Table 2.2 g, AICc=1.33, Akaike weight=0.12). This tends to suggest that land-use intensification has, at best, only a weak negative effect on land-snail rarefied species richness (Appendix 2.3 g), and this effect potentially co varies with either livestock exclusion or distance-from- edge in the model. Similarly, for land snail community composition only one of two final best-fit GLMMs included land-use intensification as an explanatory factor (Table 2.2 c, AICc=0.46, Akaike weight=0.34), and the other model did not (Table 2.2 c, AICc=0, Akaike weight=0.43). Again, this tends to suggest that the effect of land-use intensification on land-snail community composition is weak compared with the effects of fencing and distance-from-edge, with the similarity of land-snail community composition to that in the reference site declining along the gradient of increasing land-use intensification (Appendix 2.3 d).

Interactions between land-use intensification and edge responses

There were no significant land-use intensification by distance-from-edge interaction effects in any of the most parsimonious best-fit GLMMs (Table 2.2a- f, h; Figure 2.6a-f,h), except land-snail rarefied species richness (Table 2.2g, Figure 2.6g). Even in the latter case, only one of the four models of land-snail rarefied richness with equivalent explanatory power included the interaction term for land-use intensification by distance-from-edge effects (Table 2.2g, AICc=1.33, Akaike weight=0.12). The interaction term suggests that land-use intensification alters the slope of the edge response functions for land-snail rarefied species richness, with a greater relative difference in land snail rarefied richness between edge and interior for both fenced and unfenced remnants with increasing land-use intensification (Figure 2.6g).

Benefits of livestock exclusion are consistent across the land-use intensity gradient

There were no significant interaction effects between land-use intensification and livestock exclusion in any of the best-fit GLMMS (AICc =<2) for detritivore density per unit area or detritivore community composition, or for land snail density per unit area, community composition, richness and density per unit litter mass, (Figure 2.5 a-h, Table 2.2 a-h and Appendix 2.3 a-h). This suggests that the relative ecological benefits of livestock exclusion are equivalent across the gradient of land-use intensification.

100 3000 (a) (b) 2500 80

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Land snail species rarefied richness rarefied species snail Land 0 2 4 6 810 0246810 Land-use intensification Land-use intensification

Figure 2.5.Comparison of ecological responses in 11 fenced versus 10 unfenced forest remnants across the land-use intensification gradient. (a) Overall detritivore abundance m-2, (b) Overall detritivore community similarity to reference sites (community composition), (c) Landsnail abundance m-2, (d)Land snail community similarity to reference sites (community composition), (e) Landsnail species richness, (f) Landsnail abundance kg-1 litter mass, (g) Landsnail rarefied species richness, (h) Litter decomposition rate. Closed circles are fenced sites, open circles are unfenced sites. The solid line is the average of the response variable across all fenced sites, and the dashed line is the average of the response variable across all unfenced sites.

Table 2.2 AICc table for comparison of fit of GLMMs for each response variable. The top four models with the lowest AICc scores are shown for each response variable, with K (number of parameters in the model), AICc (AIC adjusted for bias due to small sample size), AICc (change in AICc, models with AICc< 2 have an equal likelihood of being the model that best explains the response variable (GLMMs in bold)), and Akaike weight (relative probability of each model being the best model). L is the land-use intensification factor, F the fencing factor, and D the distance from edge factor in the GLMMs.

Response Variable K AICc AICc Akaike weight (a) Detritivore abundance m-2 F + D 5 821.62 0.00 0.65 D 4 825.24 3.62 0.11 L * F + D 7 825.54 3.92 0.09 L + F + D 6 826.47 4.85 0.06 (b) Detritivore community composition F 4 468.97 0.00 0.66 L * D + L * F * D 11 471.66 2.68 0.17 L * F + L * F * D 12 473.70 4.72 0.06 L * D + F * D + L * F * D 12 473.70 4.72 0.06 (c) Landsnail abundance m-2 F+ D 5 590.51 0.00 0.36 D 4 591.05 0.53 0.28 L + F + D 6 592.71 2.20 0.12 L + F 5 593.24 2.73 0.09 (d) Landsnail community composition F + D 6 369.98 0.00 0.43 L + F + D 7 370.45 0.46 0.34 L * F + D 8 372.33 2.34 0.13 F * D 8 375.30 5.31 0.03 (e) Landsnail species richness F 3 584.64 0.00 0.57 L + F 4 586.78 2.14 0.20 D 3 588.11 3.47 0.10 L 4 590.28 5.64 0.03 (f) Landsnail abundance kg-1 D 4 503.03 0.00 0.77 L + F 5 506.79 3.76 0.12 F * D 8 508.71 5.68 0.05 F + D 6 509.52 6.49 0.03 (g) Landsnail rarefied species richness F 3 344.75 0.00 0.24 F + D 4 345.38 0.63 0.17 L * D + F * D 7 346.09 1.33 0.12 F * D 5 346.70 1.95 0.09 (h) Decomposition rate D 3 8.27 0.00 0.22 F 3 8.39 0.11 0.20 L 3 8.39 0.12 0.20 F + D 4 10.36 2.09 0.08

1200 10 ) (a) (b) -2 1000 8

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0 -2 etritivore abundanceetritivore m Slope of edge response Slope of edge response etrivore community similarity) (d -4 -200 (d

400 15

) (c) (d)

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and snail abundance kg-1) -1 and snail species richness) (l (l

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-1 ecomposition rate) -0.1

(d Slope of edge response Slope of edge response -2 -0.2 and snail species rarefied richness) (l 0246810 0246810 Land-use intensification Land-use intensification

Figure 2.6.Comparison of the slopes of edge response functions across the land-use intensity gradient for each fenced and unfenced site for eight response variables. The slope measurements are from the linear response function in all cases (87% of best fit response function were linear), determined from the statistical approach of Ewers &Didham (2006) (pg.33). Response variables are (a) Overall detritivore abundance m-2, (b) Overall detritivore community similarity to reference sites (community composition), (c) Landsnail abundance m-2, (d) Land snail community similarity to reference sites (community composition), (e) Landsnail species richness, (f) Landsnail abundance kg-1 litter mass, (g) Landsnail rarefied species richness, (h)Litter decomposition rate. Closed circles are fenced sites, open circles are unfenced sites.

Discussion

There is extensive evidence for the negative impacts of agricultural land uses on farmland biodiversity (e.g. Krebs et al. 1999, Kleijn et al. 2009), and recent evidence suggests that these impacts scale disproportionately with increasing land-use intensification on farms. However, no comparable studies that I am aware of have tested the off-site impacts of land-use intensification on habitat remnants embedded within farmland. For detritivore communities, I show that there are severe effects of agricultural practices on forest remnants embedded within production landscapes, but these impacts do not scale with increasing land-use intensification. Instead, livestock impacts on detritivore biodiversity and community structure in forest remnants were high at all levels of livestock density and intensification of surrounding agricultural practices, such that the ecological benefits of fencing to exclude livestock accrued independently of surrounding land-use intensity. The fact that land-use intensity did not compromise the effectiveness of fencing efforts (at least for invertebrate detritivores) is good news for conservation managers, as this action is often the first step towards the conservation of native forest remnants embedded within production landscapes (Porteous 1993, Prober et al. 2011). More broadly, this study provides evidence to suggest that the historical land-sparing ethic in New Zealand can be a viable option for balancing conservation and production under increasing land-use intensity in this system, but only if livestock are excluded from remnant vegetation adjacent to farmland.

Livestock exclusion benefits detritivore communities independent of surrounding land-use intensity

Fencing for livestock exclusion was the dominant driver of differences in remnant detritivore communities irrespective of surrounding land-use intensification. Fenced remnants had higher land-snail abundance, greater land- snail species richness, and community composition more similar to that measured in reference sites compared with the same data for unfenced sites under all land-use intensities (for both land snails and detritivorous invertebrates overall). This pattern was most dramatic when considering land snail

abundance, with fenced remnants having on average 212 more snails m-2 than unfenced remnants. Surprisingly, of all the response variables measured, decomposition rates did not vary significantly between fenced and unfenced remnants. The lack of response in decomposition rates is most likely because the litter had decomposed to the recalcitrant limit at all sites, so that it was impossible to discern differences among sites. This conclusion was reached due to the minimal variation in decomposition rates across all sites, also, the decomposition rates that were recorded were similar to the highest rates seen in the previous research used to inform this study. Conditions were wetter than those experienced during the research used to inform the length of the litter decomposition study, which would have led to conditions favouring faster rates of litter decomposition.

Surprisingly, the observed variation in detritivore community composition was independent of changes in land-use intensification in both fenced and unfenced remnants and therefore the relative benefit of fencing did not change with land-use intensity. Even if there were no nutrient impacts from surrounding agricultural land-use on invertebrate communities, I would have expected variation in stocking rates incorporated into the land-use intensity gradient to have an impact on the invertebrate fauna in unfenced remnants, but this was not the case. The severe degradation across all unfenced remnants irrespective of stocking rate could potentially be due to the cumulative impact of long-term livestock access, as many of these remnants will have had stock access since the land was first cleared for farming (over 100 years). Another explanation for the benefits of fencing independent of land-use intensification could be that even the smallest amount of stock access has large impacts on detritivore communities. I will consider this possibility in the next chapter.

Detritivore edge response functions are weakly dependent on land-use intensification

I hypothesised that native forest remnants embedded within production landscapes would have more severe edge effects when adjacent to high intensity farming systems, but for the majority of response variables tested this was not the case. A weak interaction between land-use intensification and 44

distance-to-edge for land-snail rarefied species richness was the only indication of land-use intensity impacting edge response functions in this study. In the case of land-snail rarefied species richness, increasing land-use intensification was associated with increasing slopes of the edge response functions. This result would tend to suggest that edges are more degraded in remnants adjacent to high-intensity farms, leading to land-snail edge responses being steeper with increasing land-use intensification. The impacts of land-use intensity on remnants are more likely to be seen closer to the edge where there is a greater spill-over of both livestock and nutrients into remnants from the surrounding farmland. Even in cases where spill over is observed throughout remnant edges and interiors, the intensity of impact is still likely to decrease with distance from edge. In the present study, however, it is difficult to distinguish between these effects due to the weakness of the observed land use by edge interaction effect and high variability in the dataset. Moreover, there is a chance that these results could have been confounded by the fact that fenced remnants spanned a wider range of land-use intensities than the unfenced remnants.

Potential caveats for interpretation of land-use intensification effects

The limited responses of detritivore communities to variation in land-use intensification seen in this research, particularly in fenced remnants, would seem to suggest that there are minimal spill-over effects from fertiliser inputs onto farmland. The extensive literature showing nutrient impacts on plants (Stevens et al. 2004, Crawley et al. 2005) and invertebrates ((Fenner and Palmer 1998, Boschi 2007), as well as literature concluding that nutrients move from one system to another (Matson et al. 1997), would predict different results to those that I observed, and this may be the case when looking at organisms other than leaf-litter invertebrates. Plants, for instance, are directly affected by nutrients in the soil and are therefore more likely to respond to small changes in nutrient availability. In contrast, invertebrate responses are likely to be indirect via their association with plants and there is evidence linking changes in plants due to soil nutrient availability to changes in herbivore communities (Altieri and Nicholls 2003, Schadler et al. 2007). There is less research however linking 45

these processes to changes in detritivore communities. One study even showed impacts of nutrient input on detritivore predators but minimal impacts on the detritivores themselves (Fountain et al. 2008). Further research on other taxa and also soil nutrient geochemistry in the remnants (to determine nutrient spill- over) is ongoing and should provide more insight into these research questions.

It is also possible that different results might have been observed in remnant vegetation embedded in production landscapes elsewhere, as we only captured a snapshot of the full extent of the land-use intensity gradient. For example, in other agricultural systems the amount of fertiliser applied can be three to four times as much as that applied to the farms at the high-intensity end of our gradient. Increases in the nutrients available to leach into adjacent systems could therefore increase the likelihood of spill-over effects.

Conservation management implications

These results reaffirm fencing as a priority first step in the conservation and restoration of degraded forest remnants in New Zealand (Porteous 1993, Dodd and Power 2007), as promoted by a number of conservation groups (e.g. QEII trust, Nature Heritage fund, Nga Whenua Rahui trust). Remnants with adequate livestock exclusion have a very high conservation value, independent of surrounding land-use practices. For example, the number of species of land snails in fenced remnants was almost as high as that observed in the large forest reserves. In contrast, unfenced remnants are typically severely degraded, as was the case in this study where the unfenced remnants had an average of 72% fewer land snail species per remnant than fenced remnants.

If setting aside land for conservation (land-sparing) through livestock exclusion has positive benefits even under high land-use intensities this would provide more support for a land-sparing approach to balancing conservation and production in New Zealand terrestrial systems. More generally, it also challenges one of the key mechanisms under which land sparing would be ineffective, at least for this study system. In contrast, of course, there is evidence of severe spill-over effects from farmland into adjacent aquatic ecosystems (Berka et al. 2001, Quinn and Stroud 2002, Tilman et al. 2002,

Gucker et al. 2009). For example, Waikato hill country catchment pasture streams had higher temperatures, sediment and nutrient concentration and lower water quality than native forest streams (Quinn and Stroud 2002).Clearly there are trade-offs between conservation and production that need to be considered in any plans to increase land-use intensification in the future. It is also possible that while current high-intensity farming practices (i.e. dairy farming) appear to have limited impacts on nearby terrestrial invertebrate communities, further increases in land-use intensity may push the system over a threshold and so compromise the effectiveness of land-sparing for conservation. This could be the case in other agro-ecosystems outside of New Zealand, where fertiliser inputs are sometimes considerably higher than the inputs seen on the farms in our study area. Elsewhere in New Zealand there is also evidence that the current trends of increasing intensification (seen over the previous 50 years) are likely to continue for at least the near future. The dairy sector, for example has a stated target of increasing total productivity by 50% between 2004 and 2014 (MacLeod and Moller 2006).

Also,the effectiveness of land-sparing for conservation is highly dependent on there being strong societal support for conservation, so that decisions to set land aside for conservation are not reversed in the future. The viability of land sparing as an option for balancing production and conservation is further supported in agro-ecosystems where there is minimal biodiversity on farmland, as the increases in land-use intensification needed to offset the loss to production of land set aside for conservation are less likely to cause further declines in biodiversity. This appears to be the case in relatively recent agro- ecosystems such as those in New Zealand and Australia, where native biodiversity has only been exposed to agricultural species and practices for a short time and therefore native species are more sensitive to agriculture and less likely be present on farmland even under low intensification (Milchunas et al. 1988, Landsberg et al. 2003, Diaz et al. 2007). In other parts of the world with long histories of agriculture (e.g. Europe), decreases in land-use intensification are more likely to benefit native species on farms (Kleijn et al. 2009). Certainly there is evidence that land-sharing can benefit species conservation in countries that have adopted this approach (Green et al. 2005).

Conclusions

Overall, this research has positive implications for the conservation management of habitat remnants embedded within production landscapes under current land-use intensities, but further research is clearly needed across a much wider range of taxa that might be susceptible to agricultural impacts. Although this is a positive outcome under current land-use intensities it cannot be presumed that further intensification of agriculture in the future will not result in threshold effects on agricultural spill-over that compromise land spared for conservation.

Appendix 2.1

Methods for analysing soil samples used to create land-use intensification gradient. To calculate the pH, 10 g of air-dried soil was mixed with 25 ml of deionised water (1:2:5 soil to water ratio) then left to settle overnight. The next day the pH of the supernatant was measured with a combination electrode. Total carbon (C) and nitrogen (N) were determined by dry combustion on finely ground, air dried soil, using a LECO TruSpec CNS determinator (TruSpec, St Joseph, MI, USA). Delta 15N was analysed on ground, air dried soils using a Dumas elemental analyser (Europa Scientific ANCA-SL) interfaced with a Europa Scientific 20-20 Stable Isotope Analyser. Results reported in the δ15N notation:

15 δ N = [Rsample/Rstd-1] x 1000

where:

15 14 Rsample= N/ N ratio of the sample 15 14 Rstd= N/ N ratio of the laboratory standard.

The laboratory standard is urea, which has been calibrated against a certified standard (atmospheric nitrogen). Olsen-soluble phosphorus (Olsen P) was determined by extracting 1.00 g of air dried soils for 30 min with 0.5 M

NaHCO3 at pH 8.5, then filtered through Whitman no. 42 filter paper and stored in the refrigerator overnight (Olsen et al. 1954). 4 ml of the filtrate was then mixed with 10 ml of 0.1 M H2SO4 was mixed with 4 ml of the filtrate, and left to stand for two hours. Next, 4 ml of Murphy and Riley Reagent B (Murphy and Riley 1962, Sharpley 2009) was added, the sample mixed well and then left for an hour to allow for colour production, the samples mixed well and left for about one hour for colour production. Olsen P was calculated from a standard curve of the absorbances (absorbance read at 880nm) of 0, 0.4, 1, 2, 4, 6 and 8 mg L-1 P standards. Total Phosphorus (P), total Cadmium (Cd), and total Uranium (U) was calculated from 1.0 g ± 0.01 g of air dried ground soil.The sample was acid reflux extracted at 95ºC for 30 minutes in a solution of 4 ml (1+) HNO3 and 10 ml (1+4) HCl. This solution was then centrifuged, diluted 1:4, HNO3 concentration adjusted to 2%, and then passed through 0.45 µm filter units. Finally, the sample was analysed for total recoverable elements (including P, Cd and U) by inductively coupled plasma-mass spectrometry (Martin 1994). 49

Data were corrected for moisture factor and standardised volumetrically using bulk density measurements. Moisture content was calculated from a representative 20 g subsample of each soil sample. The subsamples were weighed, dried to a constant weight in a 105°C oven, and then reweighed, allowing calculation of moisture content which was then adjusted to moisture factor (Department of Sustainable Resources 1990). The bulk density soil cores were dried in a 105°C oven until a constant weight, and then weighed. Bulk density was then calculated from the dry weight divided by the core volume.

Appendix 2.2

Tests of correlations between potential confounding patch-level variables (elevation, size of remnant, age of fencing, slope of the pasture above each remnant, or type of farming) and the composite index of land- use intensity.p-value> 0.05 =patch level variable is uncorrelated with land-use intensity

Patch level variable df r-value p-value Elevation 22 -0.094 0.671 Remnant size 22 -0.077 0.721 Age of fencing 9 -0.555 0.096 Slope 22 -0.246 0.246 Farming type 22 -0.206 0.334

Appendix 2.3

List of the 141 snail species identified from the leaf-litter samples.

Family Genus Species Author Non native 1 Charopidae Allodiscus conopeus Marshall & Barker, 2008 2 Charopidae Allodiscus dimorphus (L. Pfeiffer, 1853) 3 Charopidae Allodiscus goulstonei Marshall & Barker, 2008 4 Charopidae Allodiscus kakano Marshall & Barker, 2008 5 Charopidae Allodiscus pallidus Marshall & Barker, 2008 6 Charopidae Allodiscus tessellatus Powell, 1941 7 Charopidae Allodiscus urquharti Suter, 1894 8 Arionidae Arion hortensis (d’Audebard de Férussac, 1819) X 9 Arionidae Arion intermedius Normand, 1822 X 10 Charopidae Cavellia anguicula (Reeve, 1852) 11 Charopidae Cavellia buccinella (Reeve, 1852) 12 Charopidae Cavellia colensoi (Suter, 1890) 13 Charopidae Cavellia irregularis (Suter, 1890) 14 Charopidae Cavellia reeftonensis (Suter, 1892) 15 Charopidae Cavellia roseveari (Suter, 1896) 16 Charopidae Cavellia serpentinula (Suter, 1891) 17 Charopidae Cavellia tapirina (Hutton 1882) 18 Charopidae Cavellioropa cookiana (Dell 1952) 19 Charopidae Cavellioropa huttoni (Suter 1890) 20 Charopidae Cavellioropa microrhina (Suter 1909) 21 Charopidae Cavellioropa moussoni (Suter, 1890) 22 Charopidae Charopa bianca (Hutton 1883) 23 Charopidae Charopa coma (Gray 1842) 24 Charopidae Charopa montivaga Suter 1894 25 Charopidae Charopa parva (Suter, 1909) 26 Charopidae Charopa pilsbryi (Suter, 1894) 27 Charopidae Charopidae sp. 105 (M.77007) 28 Charopidae Charopidae sp. 137 (M.56568) 29 Charopidae Charopidae sp. 33 (M.85221) 30 Charopidae Charopidae sp. 36 (M.75729) 31 Charopidae Charopidae sp. 33 (M.85221) 32 Charopidae Charopidae sp. 38 (M.29851) 33 Charopidae Chaureopa subdepressa Climo, 1985 34 Charopidae Chaureopa titirangiensis (Suter 1986) 35 Charopidae Climocella akarana Goulstone 1996 36 Charopidae Climocella hukutaia Goulstone&Mayhill, 1998 37 Charopidae Climocella intermedia Goulstone, 1997 38 Charopidae Climocella kaitaka Goulstone, 1996 39 Charopidae Climocella prestoni (Sykes, 1895) 40 Charopidae Climocella puhore Goulstone& Mayhill, 1998 41 Charopidae Climocella rata Goulstone, 1996 42 Charopidae Climocella triticum Goulstone& Mayhill, 1998 43 Cochlicopidae Cochlicopa lubrica (Müller, 1774) X 44 Pupinidae Cytora cytora (Gray, 1850) 45 Pupinidae Cytora hedleyi (Suter, 1894) 46 Pupinidae Cytora maui Marshall & Barker, 2007 47 Pupinidae Cytora pallida (Hutton, 1883) 48 Pupinidae Cytora torquilla (Suter, 1894) 49 Rhytididae Delos coresia (Gray, 1850) 50 Rhytididae Delos jeffreysiana (Pfeiffer, 1853) 51 Agriolimacidae Deroceras reticulatum (Muller, 1774) 52 Charopidae Fectola infecta (Reeve, 1852) 53 Charopidae Fectola mira (Webster, 1908) 54 Charopidae Flammocharopa accelerata (Climo, 1970) 55 Charopidae Flammocharopa costulata (Hutton, 1882)

56 Charopidae Flammulina chiron (Gray, 1850) 57 Charopidae Flammulina crebriflammis (L. Pfeiffer, 1853) 58 Charopidae Flammulina feredayi feredayi (Suter, 1891) 59 Charopidae Flammulina perdita (Hutton, 1883) 60 Charopidae Flammulina zebra (Le Guillou, 1842) 61 Charopidae Geminoropa vortex (R. Murdoch, 1897) 62 Hydroceneidae Georissa purchasi (Pfeiffer, 1862) 63 Charopidae Granallodiscus granum (L. Pfeiffer, 1857) 64 Charopidae Granallodiscus mayhillae Marshall & Barker, 2008 65 Helicodiscidae Helicodiscus singleyanus (Pilsbry, 1889) X 66 Charopidae Huonodon hectori (Suter, 1890) 67 Charopidae Huonodon pseudoleoidon (Suter, 1890) 68 Punctidae Laoma leimonias (Gray, 1850) 69 Punctidae Laoma mariae mariae (Gray, 1843) 70 Punctidae Laoma marina (Hutton, 1883) 71 Pupinidae Liarea egea egea (Gray, 1850) 72 Pupinidae Liarea hochstetteri carinella (Pfeiifer, 1861) 73 Pupinidae Liarea turriculata turriculata (Pfeiffer, 1855) 74 Charopidae Mocella eta (L. Pfeiffer, 1853) 75 Punctidae Obanella obanella spectabilis (Powell, 1928) 76 Zonitidae Oxychilus cellarius (Müller, 1774) X 77 Charopidae Paracharopa chrysaugeia (Webster, 1904) 78 Charopidae Paracharopa fuscosa (Suter, 1894) 79 Charopidae Paracharopa goulstonei Climo, 1983 80 Punctidae Paralaoma allochroida (Suter, 1890) 81 Punctidae Paralaoma caputspinulae (Reeve, 1852) 82 Punctidae Paralaoma lateumbilicata Suter, 1890) 83 Punctidae Paralaoma miserabilis (Iredale, 1913) 84 Punctidae Paralaoma sericata (Suter, 1890) 85 Punctidae Paralaoma serratocostata Webster, 1906 86 Punctidae Pasmaditta jungermanniae (Petterd, 1879) 87 Charopidae Phenacharopa pseudanguicula (Iredale, 1913) 88 Charopidae Phenacohelix aurea Goulstone, 2001 89 Charopidae Phenacohelix giveni Cumber, 1961 90 Charopidae Phenacohelix hakarimata Goulstone, 2001 91 Charopidae Phenacohelix perplexa (R. Murdoch, 1897) 92 Charopidae Phenacohelix pilula (Reeve, 1852) 93 Charopidae Phenacohelix ponsonbyi (Suter, 1897) 94 Charopidae Phenacohelix tholoides (Suter, 1907) 95 Charopidae Phenacohelix ziczag (Gould, 1848) 96 Punctidae Phrixgnathus ariel Hutton, 1883 97 Punctidae Phrixgnathus celia Hutton, 1883 98 Punctidae Phrixgnathus conella (L. Pfeiffer, 1862) 99 Punctidae Phrixgnathus erigone Gray, 1850) 100 Punctidae Phrixgnathus fulguratus (Suter, 1909) 101 Punctidae Phrixgnathus lucidus (Suter, 1896) 102 Punctidae Phrixgnathus microreticulatus (Suter, 1890) 103 Punctidae Phrixgnathus pirongiaensis (Suter, 1894) 104 Punctidae Phrixgnathus poecilosticta (L. Pfeiffer, 1853) 105 Punctidae Phrixgnathus transitans Suter, 1892 106 Punctidae Phrixgnathus viridulus viridulus (Suter, 1909) 107 Punctidae Punctidae sp. 100 (M.84972) 108 Punctidae Punctidae sp. 102 (M.85773) 109 Punctidae Punctidae sp. 128 (M.88158) 110 Punctidae Punctidae sp. 140 (M.29067) 111 Punctidae Punctidae sp. 178 (M.84473) 112 Punctidae Punctidae sp. 190 (M.97919) 113 Punctidae Punctidae sp. 196 (M.103034) 114 Punctidae Punctidae sp. 203 (M.109730) 115 Punctidae Punctidae sp. 242 (M.83495) 116 Punctidae Punctidae sp. 243 (M.61602) 53

117 Punctidae Punctidae sp. 246 (M.37005) 118 Punctidae Punctidae sp. 247 (M.25419) 119 Punctidae Punctidae sp. 254 (M.61752) 120 Punctidae Punctidae sp. 27 (M.88000) 121 Punctidae Punctidae sp. 50 (M.14135) 122 Punctidae Punctidae sp. 56 (M.62133) 123 Punctidae Punctidae sp. 59 (M.65253) 124 Punctidae Punctidae sp. 67 (M.61613) 125 Punctidae Punctidae sp. 69 (M.55898) 126 Punctidae Punctidae sp. 70 (M.56634) 127 Punctidae Punctidae sp. 71 (M.77798) 128 Punctidae Punctidae sp. 72 (M.93105) 129 Punctidae Punctidae sp. 81 (M.68844) 130 Punctidae Punctidae sp. 86 (M.70256) 131 Rhytididae Rhytida greenwoodi greenwoodi (Gray, 1850) 132 Charopidae Suteria ide (Gray, 1850) 133 Punctidae Taguahelix campbellica (Filhol, 1880) 134 Punctidae Taguahelix elaiodes (Webster, 1904) 135 Punctidae Taguahelix francesci (Webster, 1904) 136 Charopidae Thalassohelix zelandiae (Gray, 1843) 137 Charopidae Therasiella celinde (Gray, 1850) 138 Charopidae Therasiella neozelanica Cumber, 1967 139 Charopidae Therasiella serrata Cumber, 1967 140 Achatinellidae Tornatellinops novoseelandica (Pfeiffer, 1853) 141 Valloniidae Vallonia pulchella (Muller, 1774) X

Appendix 2.4

Summary statistics (coefficient tables) of the most complex GLMM with AICc< 2 (Table 2.2) for each response variable. Significant p-values (<0.05) are shown in bold. F=livestock exclusion factor (fencing), D=distance from edge factor (distance, with and without a polynomial (non-linear) term as appropriate), L=land-use intensity factor (lui).

(a) Detritivore abundance m-2

Estimate Std. Error z value p value F + D (Intercept) 7.367 0.243 30.352 <0.001 fencing -1.651 0.352 -4.689 <0.001 poly(distance, degree = 2)1 2.511 0.787 3.190 0.001 poly(distance, degree = 2)2 -0.707 0.786 -0.899 0.369

(b) Detritivore community composition

Estimate Std. Error z value p value F (Intercept) 0.413 0.162 2.544 0.011 fencing -0.775 0.235 -3.295 0.001

Estimate Std. Error z value p value F + D (Intercept) 5.160 0.236 21.896 <0.001 fencing -1.717 0.343 -5.010 <0.001 poly(distance, degree = 1) 2.007 0.848 2.369 0.018

(d) Land snail community composition

Estimate Std. Error z value p value L + F + D (Intercept) -0.888 0.180 -4.923 <0.001 fencing -1.475 0.268 -5.511 <0.001 lui -0.115 0.060 -1.907 0.056 poly(distance, degree = 2)1 3.560 0.752 4.735 <0.001 poly(distance, degree = 2)2 -2.015 0.750 -2.688 0.007

(e) Land snail species richness

Estimate Std. Error z value p value F (Intercept) 3.411 0.167 20.378 <0.001 fencing -1.364 0.247 -5.521 <0.001

(f) Land snail abundance kg-1 litter mass

Estimate Std. Error z value p value D (Intercept) 3.636 0.221 16.444 <0.001 poly(distance, degree = 2)1 1.234 0.825 1.495 0.135 poly(distance, degree = 2)2 0.574 0.831 0.691 0.490

(g) Land snail species rarefied richness

Estimate Std. Error z value p value L * D + F * D (Intercept) 1.849 0.175 10.555 <0.001 fencing -1.384 0.275 -5.038 <0.001 lui -0.082 0.060 -1.384 0.167 poly(distance, degree = 1) 0.921 0.527 1.749 0.080 lui1:poly(distance, degree =1) -0.115 0.183 -0.630 0.529 fencing:poly(distance, degree =1) 1.795 1.103 1.627 0.104

Chapter 3: Minimal livestock trampling has severe impacts on land snail communities in native forest remnants

Introduction

Agricultural yields per unit area are increasing dramatically in production systems worldwide, through increased inputs, more efficient technology and new crop varieties (Tilman et al. 2002, Ewers et al. 2009). For example, overall crop yield increased globally by 106% in the 40 years prior to 2000 (Green et al. 2005), and all evidence points to the need to increase yields at a similar rate to meet the demands of a growing human population into the foreseeable future (Green et al. 2005, Foley et al. 2011).

Livestock production is a particularly contentious component of production land-use, which has expanded greatly in land area and intensity in recent years. Over 45% of the world’s land surface area is now used for livestock production, predominantly on permanent pastures, but also via the production of livestock feed crops on one third of the world’s arable land (McMichael et al. 2007, Herrero et al. 2009). Livestock production has large- scale impacts on the environment, including severe land degradation, adverse impacts on nutrient cycling, and an 18% contribution to worldwide greenhouse gas emissions (Herrero et al. 2009) Moreover, these impacts are predicted to increase with the doubling of global demand for meat by 2050 (Tilman et al. 2002, Herrero et al. 2009).

In pastoral farming, a key part of this land-use intensification is the improved pasture growth resulting from higher nutrient availability from increased fertiliser inputs, more efficient irrigation and increases in imported feedstock. This has led to a dramatic rise in the capacity of the land to maintain a higher animal stocking rate (Dorrough et al. 2007), allowing farmers to increase meat, dairy and wool production on their farms. In New Zealand, for example, where pastoral farming is one of mainstays of the economy, the first phase of intensification (between 1920-1970) involved a 150% increase in

livestock numbers per area, with very little change in the type of farming practices or the total area under pastoral production. Following on from this (post 1980) the increases in intensification seen in New Zealand have for the most part been due to the growing trend toward conversion from low-intensity dry stock (beef and sheep) to dairy farming, which is much more profitable but involves much more intensive farming practices (MacLeod and Moller 2006, Lee et al. 2008). Beef and sheep stocking rates have actually decreased slightly in New Zealand over the past 20-30 years along with a decrease in the area farmed for these livestock, but the area under dairy farming has dramatically increased (by 34% from 1994 to 2002 alone), coupled with a 20% increase in dairy stock density per ha (Monaghan et al. 2005). This shift to higher stocking rates and more intensive pastoral systems in New Zealand is mirrored globally. For example, in the 40 years prior to 2002 global per capita meat production has increased more than 60%, and in the United States the average number of cattle per livestock operation has increased by 160%.

The increases in livestock densities and on-farm inputs seen worldwide can have a wide range of negative impacts on farmland biodiversity (Tilman et al. 2002, Kleijn et al. 2009). In many systems, such as the calcareous grasslands of Central Europe, livestock grazing can be an important part of the ecosystem but overstocking of land has significant negative impacts on biodiversity and ecosystem functioning through grazing and trampling impacts (Boschi 2007). For example, grazing can lead to unsustainable decreases in vegetation biomass, which increases the need for imported feedstock, and facilitates the establishment of invasive plants. Livestock trampling can also have severe impacts on the soil, causing erosion, changes in nutrient availability and altered soil microbial communities (Bromham et al. 1999, Pietola et al. 2005), as well as impacts on bird and invertebrate communities (Milchunas et al. 1998, Lindsay and Cunningham 2009, Verhulst et al. 2011). For example, Boschi and Baur (2007a) found that increased grazing intensity (both stocking rate and amount of time under grazing) led to decreases in land snail abundance and species richness in calcareous grassland systems in the Jura Mountains, Switzerland. These direct effects of livestock grazing and trampling can also have further, potentially more important, indirect effects on

ecosystem functioning such as nutrient cycling (Seagle et al. 1992, Biondini et al. 1998). In extreme cases, overstocking can be the initial cause of desertification in semi-arid regions, which can lead to degraded land becoming unproductive and unusable in the long term.

The off-site impacts of livestock grazing and trampling on semi-natural vegetation adjacent to pastoral land have been less well-studied, but nevertheless can be severe. In forest systems adjacent to farmland, for example, livestock can cause considerable damage in unfenced remnants though removal of the majority of understory vegetation including the seedlings of canopy and sub canopy species, preventing canopy recruitment and leading to decreases in litter accumulation (Naeth et al. 1991, Burns et al. 2011). Trampling by livestock also causes compaction of the soil and leaf-litter layer, which can impact soil erosion, microbial communities, nutrient and moisture levels and litter mass (Pietola et al. 2005, Jeddi and Chaieb 2010). Finally, livestock can increase the nutrient inputs into systems through dung and urine deposition (Duncan et al. 2008). Considering all of these impacts from livestock grazing and trampling, it is not surprising that there is considerable evidence for differences in biodiversity between fenced and unfenced remnants, particularly for understorey plant and invertebrates communities (chapter 2, Bromham et al. 1999, Lindsay and Cunningham 2009). However, in many of these cases the mechanistic drivers of these differences have not been investigated. Loss of vegetation and changes to soil structure are the most obvious changes to remnant vegetation after livestock damage, but it can be difficult to separate these factors from other potentially correlated impacts that might also cause changes in invertebrate communities, such as variation in soil and litter moisture levels or amount of litter. To better understand the impacts of livestock grazing and trampling on invertebrate communities, the underlying drivers of these impacts must be investigated. Moreover, there may be unanticipated interaction effects between multiple drivers of livestock impacts which might exacerbate the total effects of increasing livestock density. For example, it is not known whether livestock impacts scale proportionate to variation in livestock density per unit area per unit time, or whether there are potential thresholds of livestock grazing or trampling impacts above which more substantial impacts on plant or

invertebrate communities occur. This information will be vital for providing farmers with accurate advice on the environmental impacts of intensifying livestock production, and necessary mitigation strategies to limit livestock access and impacts in semi-natural ecosystems embedded within production landscapes.

Here, I test the mechanistic determinants of livestock trampling impacts on snail communities in semi-natural forest remnants embedded within farmland, using an artificial trampling experiment conducted under field conditions in northern New Zealand. I use a factorial combination of litter manipulation and trampling treatments to partition different causal drivers of livestock impacts on soil and leaf-litter assemblages, and relate treatment differences to covariance in five important proximate measures of habitat structure and microclimate (leaf litter mass, leaf litter complexity, leaf litter moisture content, soil moisture content and soil compaction) which change in relation to intensity of livestock impacts. Many different habitat characteristics determine the ability of a species to persist at a site. In the case of leaf-litter invertebrate communities, and more specifically land snails, the dominant factors influencing distribution are typically soil moisture, litter complexity and diversity, soil calcium concentration and correlated pH, litter depth, amount of dead wood, and litter nutrient quality (Bultman and Uetz 1984, Solem 1984, Martin and Sommer 2004, Müller et al. 2005, Wardle et al. 2006). Of these factors, livestock impacts are expected to be most severe on litter mass due to grazing decrease plant biomass, and soil moisture due to soil compaction from trampling. Here, I partition these relative pathways of potential effects by experimentally increasing the level of artificial trampling to simulate different livestock intensities, as well as experimentally manipulating litter to allow soil compaction to be simulated with and without alteration of litter mass or structure, allowing discrimination of the relative importance of these causal pathways using structural equation modelling. This will provide a quantitative test of the scaling of ecological impacts with increasing intensity of livestock trampling, and identity the dominant mechanistic pathways through which these effects are operating.

Methods

ExperimentalSite

The experiment was conducted in remnant native forest vegetation on farmland in the Waipa District, which is located in the Waikato Region of the North Island, New Zealand (see Chapter 2, Figure 2.1). The majority of forest cover in New Zealand was cleared for farming in the early 20th century, leaving most remaining lowland native vegetation as small remnants embedded within production systems. After the initial clearing, the majority of the remaining remnants were grazed by domestic livestock for a long period, although many have now been fenced to exclude livestock (Burns et al. 2011). The experimental site is typical of this situation andhas been reliably fenced to exclude livestock for over 20 years. All areas sampled were at least 20 m from the remnant edge.

Experimental Design

The mechanistic basis for livestock impacts on leaf-litter invertebrate communities was tested under experimental conditions using a factorial combination of four levels of artificial trampling (0, 2, 4, or 6 trampling events) crossed withthree levels of litter manipulation (litter not crushed or reduced in volume by livestock, litter crushed but not reduced in volume, and litter crushed and reduced by 50% in volume) (Table 3.1). The 12 treatment combinations were applied to separate 1.2x3m plots, and replicated three times in a randomised block design (36 plots in total). The three blocks were determined by spatial location (Appendix 3.1) within the forest remnant and percent ground cover of thread fern (Blechnum filiforme), a common ground cover species at the site which varied from 0 to 95% cover among the blocks. A Before-After- Control-Impact (BACI) design was used for the sampling procedure to assess the impact of each of the treatment combinations.

A mechanical hoof was used to carry out the trampling treatments. A mechanical device was chosen rather than a live cow to enable high repeatability, accuracy and randomisation of trampling in the experiment. In addition, a live cow in the remnant could have had confounding effects via 61

browsing and nutrient enrichment which were not the focus of the study. The mechanical hoof consisted of a compressor-driven pneumatic ram with a freshly-excised cow hoofattached (Appendix 3.2).The hoof was excised from a dead cow just below the carpus, and clamped to the air ram approximately half way down the metatarsal. A fresh hoof was used every day of the trampling treatments. The hooves were obtained from a local abattoir, where the cows were killed humanely under the New Zealand Animal Welfare Act 1999. The trampling pressure applied by the hoof was set at 220kPausing an air pressure regulator to simulate the treading impact of an adult Friesian cow (Di et al. 2001). The mechanical hoof assembly was mounted on nylon rollers so that it could roll laterally along a 1.5 m trolley section, and the entire trolley section could roll length-wise along a 1.5 m wide x3.3m long steel frame that fully encompassed each experimental plot. The design of the frame thus allowed the users to move the hoof to any point within the plot. The height of the hoof above the ground was also adjustable so that the hoof was the same distance from the ground at all times.

As adult Friesian cow hooves are approximately 10x10cm (The 12 hooves used in this experiment varied in hoof area from approximately 91 – 112 cm2), this created a possible 360 (12 x 30) trampling points within the plot, if every point was trampled with no overlap. However, this would not be consistent with the way cows actually walk (with potential overlap in trampling points), so I chose to allocate 360 hoof compressions randomly across each ploton each trampling event. To further ensure variation in spatial offset, or overlap, of individual hoof compressions, I marked 5-cm intervals on the steel trolley and frame assembly to project a 5 x 5 cm grid of 720 possible compression points onto the plot surface, and within that grid randomly allocated our 360 total hoof compressions. If the compression point fell on a tree root, the hoof compression was missed. In practice, the procedure described above resulted in individual points being trampled between 0 and 4 times at each trampling event and total hoof compressions ranging from 333 to 360.

The trampling treatments were applied over a period of 6 weeks, between 13th January and 18th February 2011. Each week within the 6 weeks

had a trampling event, and within that week the treatments were applied over as many days as the number of trampling treatments in that week (1, 2 or 3), due to the amount of time needed to trample the plots (Table 3.2).

Three litter manipulation treatments were designed to discriminate the effects of soil compaction from the effects of litter structural change or litter volume change on land-snail community composition. To separate the effects of soil compaction on snail community composition from other treatment effects, I used a soil-only trampling treatment (S) (Table 3.1). In this treatment, each litter removal event consisted of removing all leaf litter and friable humus from within the plot as rapidly and carefully as possible and placing it onto cotton sheets immediately before the trampling was carried out. Immediately after trampling the litter was spread evenly back onto the plot. In the case of the plots which had no trampling but did have litter removal (S0), the litter was removed from the plots for the same period of time as it took to trample one plot. To separate the effects of litter structural change from litter volume change on land snail community composition, I used a combined soil and litter trampling treatment (SL) (Table 3.1). This treatment consisted of trampling both the soil and the litter, with the litter unchanged before the trampling event. The plots with no trampling in this litter treatment (SL0) represent the overall controls for the entire experiment, because they were not trampled and their litter cover was not manipulated (Table 3.1). Finally, the effect of reduced litter volume on land snail community composition was determined using a ‘half litter removed’ treatment (SLR) (Table 3.1), where all of the litter was removed from the plots as above, and then half of the litter was discarded and the remaining half was spread evenly back onto the plots. Litter reduction was carried out before the first trampling event in all of the plots in this treatment, including the plots not trampled (SLR0).

Table 3.1 Mechanical hoof experiment treatment combinations. A factorial combination of four levels of artificial trampling crossed with three levels of litter manipulation.

Trampling treatments None (0) Low (2) Medium (4) High (6) Litter treatments Soil trampled No trampling, 2 trampling 4 trampling 6 trampling but not litter litter removed events, litter events, litter events, litter (S) then replaced removed removed removed (2 events as before beforetrampling beforetrampling in the ‘low’ trampling then then replaced then replaced treatment) replaced

Soil and litter Control 2 trampling 4 trampling 6 trampling trampled (SL) events events events

Soil and litter No trampling, 2 trampling 4 trampling 6 trampling trampled + half litter events, half events, half events, half Half of the removed litter removed litter removed litter removed litter in the before first before first before first before first plot removed trampling trampling trampling event trampling event (SLR) event event

Table 3.2 Timing of trampling events for each trampling treatment. The shaded boxes mark the week that each event was applied to the relevant treatment plots.

Week 1 2 3 4 5 6 13-15 Jan 20 Jan 26 & 28 Jan 4 & 5 Feb 8 Feb 14-16 Feb None (0) Low (2) 1 2 Medium (4) 1 2 3 4 High (6) 1 2 3 4 5 6

Sampling methods

In each of the 36 plots in the BACI experiment, soil, litter and invertebrate samples were collected both pre-treatment (26 October 2010) and post- treatment (25 Feb 2011). On each occasion, leaf litter samples, penetration resistance measurements and soil samples were taken at four random (non- overlapping) points within each plot. Pre- and post-treatment samples were taken from different sampling points within each plot.

The use of a BACI sampling design enabled the impact of the treatments on snail communities to be accurately determined separate from any seasonal fluctuations in snail abundance or community composition. Any changes in the snail communities from pre- to post-treatment in the control plots were used to determine seasonal fluctuations in the study area, and this was taken into account to ensure natural variation does not prevent the detection of an impact from the treatments effects.

First, the litter samples were collected first from each sampling point using a 33 cm-diameter frame (Chapter 2). All leaf litter and friable humus was scraped from inside the frame and placed into a large cloth bag as rapidly as possible to reduce invertebrate escape. The samples were then transported back to the lab and weighed immediately to obtain total wet litter mass. They were then sieved through a 10mm sieve to obtain a fine ‘bottom fraction’ of sieved litter containing land snails, and a coarse ‘top fraction’. Both the top and bottom fractions of litter were oven-dried at 60˚C for 48 hours (McClaugherty et al. 1985) and weighed to obtain a combined estimate of dry litter mass. The amount of litter per sample (litter mass) was converted to mass m-2 before analysis. Moisture content of the litter was calculated as a percentage of the dry litter mass:

MC%= (WW – WD)/WD x 100

where:

WW= Mass of wet litter

WD = Mass of dry litter

The “bottom fraction” of litter was then sieved through three more sieves of mesh sizes 4.75mm, 2.00 mm and 0.05mm, resulting in four size classes of litter that were weighed separately. These weights were combined using Simpsons diversity measure in Biodiversity Professional software (BD Pro, version 2.0) to obtain a measure of the litter complexity.

Next, penetration resistance was measured to determine soil compaction within the plots. Penetration resistance was measuredat the centre of the circle of bare ground left after taking the litter for the pre-treatment measurement. Measurements were taken to a depth of 10cm using a penetrometer

(Eijkelkamp penetrologger) with a 2 cm2 cone and 60º top angle. The post- treatment penetrometer measurement (February) was taken as near as possible to the pre-treatment measurement (unlike the other measurements which were taken at a different sampling point), in order to avoid bias when comparing pre- and post-treatment measurements as there can be high variability in penetration resistance measurements over small distances.

Lastly, a 2 cm diameter soil core was taken to a depth of 10cm at the centre of the bare patch remaining after litter collection. The soil cores were taken from different sampling points pre- and post-treatment following the same sampling design as the leaf-litter collection. The samples were weighed immediately on return to the lab, and then dried to a constant weight at 1050C. Moisture content was calculated using the same method asfor the litter samples.

The bottom fraction of litter was later searched for land snails. Sieved litter fractions larger than 2 mm were searched by eye, and the remaining fractions were searched under a 10x stereo-microscope. The number of individuals found in each plot (snail abundance) was converted to number per m2before analysis. All land snails, including juveniles, were identified to species where possible, by reference to type specimens and other authoritatively- identified material (including vouchers for undescribed species) held in the Museum of New Zealand Te Papa Tongarewa (NMNZ). Nomenclature was standardised to Spencer et al. (2009).

Statistical Analysis

Determining completeness of sampling effort pre- and post-treatment Across both pre-and post-treatment samples, the completeness of land snail community sampling and species richness estimation was evaluated using sample-based species accumulation curves, re-scaled to number of individuals, in EstimateS version 8.2.0 (Colwell 2005).

Determining variation in land snail community composition pre- and post- treatment Post-treatment plot-level measures of land snail abundance m-2 and land snail richness were standardised using post-treatment minus pre-treatment values to account for underlying spatial variation in snail communities between plots pre- treatment as well as seasonal fluctuations between sampling periods. Plot- standardised values were then converted to treatment minus control differentials. For the response variables, this equates to plot differentials reflecting treatment impacts relative to the controls (with each plot value minus the average of the three control plot values).

To compare land snail community composition between treatment plots I calculated variation in community dissimilarity to control plots based on an ordination of snail species-abundance distributions. Snail community composition was compared between plots, and between pre- and post- treatment samples in a Non-metric Multi-dimensional Scaling (NMDS) ordination on square root transformed abundance data using the Bray Curtis similarity index in Primer v.6 (Clarke 2006). Formal significance tests for differences between pre- and post-treatment samples were performedusing the ANOSIM permutation test(Clarke 1993). Community similarity to control plots was then determined from the underlying Bray-Curtis similarity matrix using the average similarity between each treatment plot and the three control plots. Dissimilarity was calculated as 100 – similarity values. Community dissimilarity to control plots was determined for both pre- and post-treatment samples. Variability or dispersion of snail community composition data between pre- and post treatment samples was also tested in Primer using permutational analysis of multivariate dispersions (PERMDISP: Anderson 2006). Post- treatment measures of snail community composition were standardised using post-treatment minus pre-treatment values to account for underlying spatial variation in snail communities between plots pre-treatment as well as seasonal temporal fluctuations between sampling periods.

Determining underlying drivers of changes in snail communities due to trampling and litter treatments I constructed structural equation models (SEM) in Amos version 19.0 (Arbuckle 2010) to partition the direct versus indirect mechanistic pathways through which trampling intensity and changes in litter structure or volume could influence snail communities. All SEM analyses were carried out on plot-standardised treatment minus control differentials to account for spatial and temporal variation in snail abundance or community composition (as described above).

A causal hypothesis of relationships among variables was created for each response variable (i.e. the ‘full’ SEM models, Appendix 3.3a,b), with litter treatments, trampling treatments and their interaction (‘exogenous’ predictors) having both direct and indirect effects on the response variables tested. I hypothesised that the treatments could have indirect effects on the response variables through a series of habitat structural and microclimatic variables (‘endogenous’ predictors) that are thought to be important for land snail abundance and diversity. The endogenous variables in the full model were leaf litter mass, leaf litter moisture content, soil moisture content, penetration resistance (soil compaction), and leaf litter complexity. In the SEM of land snail species richness, land snail abundance was also entered as a mediating variable to account for bias in richness estimates due to variation in sample abundance between plots (Appendix 3.3b).

To ensure that exogenous variables in the SEMs were uncorrelated, correlation coefficients were calculated for all possible relationships between trampling treatment, litter treatment, and their interaction, prior to the model fitting procedure (Arbuckle 2010), and these were found to be non-significant. Also, all possible correlations between the five endogenous variables in the SEMs were tested prior to model fitting, to simplify the model fitting procedure. Where correlations were significant, relationships were included as paths in the full model (Appendix 3.3a, b).

To determine the most parsimonious final SEM model with the minimum pathways necessary to explain variation in the three response variables tested, all possible combinations of the direct and indirect pathways between the experimental treatments and the response variables were tested using the 68

specification search option in Amos. The Akaike Information Criteria (AIC) was used to compare the model outputs and the model with the lowest AIC was chosen as the best fit model (Schermelleh-Engel et al. 2003). We used bootstrapping with 1,000 random samplesgenerated from the observed covariance matrix to estimate 90th percentile confidence intervals and significance values for the standardised direct, indirect and total effects (Kline 2010) in the final, most parsimonious model. Model fit to the data was assessed by inspecting standardised residual covariances between the different variables in the model, the Chi-square test of the model, and the root-mean-square error of approximation (RMSEA) values. If the model standardized residual covariance values were below 2, the Chi-squared test was non-significant and the RMSEA values were below 0.05 then the model was considered to be a good fit (Arbuckle 2010).

Results

Variation in land snail communities within and between pre- and post- treatment samples

The pre-treatment leaf-litter samples had a total of 1 660 land snail individuals, comprising 91 species in 8 families, with sample abundances ranging from 0 to 56 individuals (mean ± 95% CL, 11.59 ± 1.87 individuals) and species richness per sample ranging from 0 to 29 species (mean ± 95% CL, 7.79 ± 1.04 species) (Appendix 3.4). Post-treatment samples had a total of 2 105 individuals, comprising 78 species in 7 families, with sample abundances ranging from 0 to 59 individuals (mean ± 95% CL, 14.62 ± 2.10 individuals)and species richness per sample ranging from 0 to 34 species (mean ± 95% CL, 10.57 ± 1.30 species) (Appendix 3.4). No additional species were found in the post-treatment samples that had not already been collected in the pre-treatment samples.

Species accumulation curves calculated for pre-treatment and post- treatment samples (Figure 3.1) approached an asymptote, suggesting that sample completeness was comparatively high (particularly as no additional species of land-snails were captured in the post-treatment sampling). Species richness and composition of land snail communities were therefore well characterised at the experimental site. Despite a 27% increase in the total 69

number of snail individuals in the post-treatment samples (perhaps due to seasonal variation in abundance), there was a 16% decrease in rarefied species richness in post-treatment samples (at a standardised sample abundance of N =1 660 individuals).

In an NMDS ordination analysis of variation in land snail community composition among experimental plots, there was significant variation in composition between pre-treatment and post-treatment plots (ANOSIM R=0.111, p-value=0.001, Figure 3.2). Moreover, variation among plots was also higher in pre-treatment samples than post-treatment samples (PERMDISP t- statistic=5.13, p-value=0.001), suggesting that communities became more similar post-treatment (Figure 3.2). It should be recognised of course that pre- vs. post-treatment differences in snail communities could also be due to natural seasonal variation.

Figure 3.1 Comparison of land snail species richness between sampling periods (pre- and post- treatment), while controlling for variation in sampling effort shown as sample based species accumulation curves rescaled to number of individuals sampled. Pre-treatment samples are the dashed line, and post- treatment samples the solid line.Vertical dotted line represents a common sampling effort (1660 individuals sampled) between sampling periods. Error bars are drawn for only every 18th sample for clarity.

Figure 3.2 Variation in land snail community composition within and between pre-treatment (‘B’, before) and post-treatment (‘A’, after) samples in an NMDS ordination (based on square root transformed land snail abundance data and a Bray-Curtis distance metric). Each symbol represents an experimental plot. Control plots (‘AC’ and ‘BC’) are indicated in bold. The stress value is for a 3D NMDS.

The direct and indirect effects of trampling and litter treatments on land snail communities

Land snail density The most parsimonious SEM model for land snail density had acceptable goodness of fit indices, with all residual covariance values <2, non-significant chi squared values (Chi2=14.320, df=27, p-value=0.983), all RMSEA values below 0.001 and the variables in the model explained 72% of variance in land snail density (R2=0.72). The SEM analysis of land snail density determined that both trampling and litter treatments (mediated by litter mass), and their interaction, had significant impacts on snail density (Figure 3.3a). Land snail density decreased with increased trampling intensity, and increased alteration of litter caused a decrease in litter mass which contributed to decreases in land snail density (Figure 3.3a, Figure 3.4a). As well as litter mass, the other endogenous predictors that significantly impacted land snail density (independent of treatment effects) were litter moisture and soil moisture (both with significant positive effects on land snail density). Of all the explanatory variables in the model, litter mass was the most important for land snail density (standardised total effects = 0.680, Appendix 3.5a).

Land snail species richness The final SEM model for land snail species richness had acceptable goodness of fit indices (as above) (Chi2=18.530, df=33, p-value=0.980). The model explained substantially less variation in land snail species richness (R2=0.35) than observed for abundance, and all significant impacts on land snail richness in the SEM analysis were mediated by land snail abundance. Land snail species richness decreased with increasing trampling intensity (mediated by land snail abundance) and decreasing with increasing alteration of litter structure (mediated by litter mass and land snail abundance) (Figure 3.3b, Figure 3.4b). Overall, the mediating effects of land snail abundance dominated the total effect size for species richness (Figure 3.3b, Appendix 3.5b). However, there were weak (non-significant) indirect effects of the trampling by litter

interaction term (mediated by litter complexity), as well as weak direct negative effects of soil moisture, on land snail species richness (Figure 3.3b).

Land snail community composition The final SEM model for land snail community composition explained 32% of variance and had acceptable goodness of fit indices (Chi2=15.191, df=29, p- value=0.983). In the SEM analysis, increased trampling had significant direct positive effects on community dissimilarity to control sites, and litter treatments had no impact (Figure 3.3c, Figure 3.4c). There was also a non-significant trampling by litter treatment interaction effect on litter complexity (Figure 3.3c), but this did not follow through to impact land snail community composition. The environmental variables contributing significantly to explaining the variance in land snail community composition (independent of treatment effect) were penetration resistance and litter moisture, both of which had positive effects on land snail community dissimilarity (i.e., resulted in communities being more dissimilar) (Appendix 3.5c).

(a) Penetration Trampling resistance treatments -25.52

0.01 Land snail 21.77 abundance Treatment m-2 interaction (R2=0.72) Litter 2383.71 -0.01 complexity 2 (R =0.14) 0.13

Litter Litter mass treatments m-2 -362.07 (R2=0.25) 5.81 1.34

Litter

moisture Soil moisture

(b) Land snail Penetration Trampling richness resistance 2 treatments -8.68 (R =0.35) 45.09 0.13 0.01

7.40 Treatment Land snail interaction abundance (R2=0.72) Litter 810.79 -0.01 complexity 2 (R =0.14) 0.13

Litter treatments Litter mass 2 -123.15 (R =0.25) 1.98 -0.60 0.46

Litter moisture Soil moisture

Figure 3.3. Continued on following page

Figure 3.3 Continued from previous page

1.74 resistance Trampling treatments 21.15

0.01

Land snail Treatment community interaction -0.01 2383.71 Litter dissimilarity 2 complexity (R =0.32) (R2=0.14)

Litter Litter mass -2 treatments -362.07 m (R2=0.25) 0.09

Litter

moisture

Figure 3.3 Structural equation models discriminatingdirect versus indirect mechanistic pathways through which trampling intensity and changes in litter structure or volume influence snail communities,showing the most parsimonious models for (a) Land snail density m-2, (b) land snail species richness, and (c) land snail community composition. Arrows represent causal paths from predictor to response variables. The number on each path in the parsimonious models is the value of the unstandardised partial regression coefficient, indicating whether the relationship is positive or negative. The statistical significance of individual regression coefficients is indicated by the colour of the line (black, P ≤ 0.05; grey, > 0.05). The thickness of the line indicates the magnitude of the standardised path coefficients (Appendix 3.5). Squared multiple correlations (R2) are given to represent the variance explained by all the associated pathways linking that variable.

L= S SL SLR L= SSLSLR T= 0 2 4 6 0 2 4 6 0 2 4 6 T= 0 2 4 6 0 2 4 6 0 2 4 6

CONTROL CONTROL

CONTROL

T= 0 2 4 6 0 2 4 6 0 2 4 6 L= S SL SLR

Figure 3.4 Average impacts of experimental litter manipulation and trampling treatments on land snail community responses. (a) Land snail density m-2, (b) land snail species richness, (c) community dissimilarity to control plots. Response variables were standardised using post-treatment minus pre- treatment values as in SEM analysis. Plot-standardised values were then converted to treatment minus control differentials (control=0). Responses are grouped by litter treatment: S=litter removed for trampling, SL=soil and litter trampled, and SLR=soil and litter trampled after removing half of the litter mass (see Table 3.1). Within litter treatments, bars indicate number of trampling events over a 6 week period. SL0=control.

Discussion

Chapter 2 of this thesis as well as previous research elsewhere have provided extensive evidence that unfenced native forest remnants embedded within farmland are severely damaged from livestock trampling and grazing. However, the mechanistic drivers of these impacts are poorly understood. Furthermore, it is unknown at what amount of access to remnants livestock cause damage to native species. Here, I show that even a small amount of livestock impact has severe effects on land snail communities in native forest remnants. For example, land snail density in the lowest impact treatment plots decreased by an average of 42 individuals m-2 compared to the control, and up to an average of 100 individuals m-2 in the highest impact plots. Also, over all of the treatment

plots, species richness declined by an average of 10 species per plot compared with control plots. In addition, changes in litter mass stemming from livestock impacts had a significant negative impact on snail communities, and interacted with trampling effects to alter the total damage caused by livestock in remnant vegetation. The underlying drivers of these changes in land snail communities vary, but are primarily due to changes in the amount of litter available and the effects of unknown mediating variables that were not directly measured in this experiment. Surprisingly, my short-term trampling and litter treatments did not cause significant changes in some potential mediating variables, such as soil moisture, litter moisture, litter complexity, and penetration resistance, but natural variation in these environmental variables among plots was nonetheless still an important determinant of snail community composition. As these variables are known to be influenced by long-term livestock impacts in a range of study systems (Naeth et al. 1991, Chaichi et al. 2005, Pietola et al. 2005), the substantial short-term trampling effects observed here might well be exacerbated even further in the long term.

Trampling treatment effects on environmental variables and land snail communities

In this experiment there were only weak effects from trampling on land snail communities mediated indirectly by the measured proximate variables, instead, the majority of the variance in the land snail community was explained the direct effects of trampling on the response variables, suggesting there were impacts on unmeasured environmental variables that determine changes in land snail community composition. Elsewhere, research suggests that there are three key determinants of snail community composition, habitat structure, moisture levels, and pH (or calcium) (Martin and Sommer 2004, Müller et al. 2005). In the short term trampling did not cause changes in moisture levels, but there were weak (marginally non-significant) positive impacts on land snail species richness that were mediated by increased litter complexity. I did not however, measure variation in pH or calcium levels in the plots, therefore changes in soil pH could be mediating some of the direct impacts from trampling seen in this experiment, particularly as there is numerous studies 77

showing varying impacts of livestock on soil pH (Yong-Zhong et al. 2005, Jeddi and Chaieb 2010). Also, other aspects of habitat structure that were not measured could have been mediating trampling impacts such as root mass and amount of dead wood in the plots. It is apparent however, that even a small amount of trampling causes large changes to snail communities.

Litter treatment effects on environmental variables and land snail communities

The litter manipulation treatments were effective at imposing differences in litter mass between plots, and any significant impacts on land snail communities from litter treatments were mediated by litter mass. The reduction of litter mass had a large significant effect on land snail abundance and land snail species richness, and for these response variables the total effect of litter mass was larger than any of the other variables investigated. The dominant influence of litter mass on land snail communities makes it all the more surprising that so few studies investigating leaf-litter dwelling snail community determinants fail to consider the potential causal effect of variation in litter mass in their analysis (Martin and Sommer 2004).

Environmental influences on land snail communities

Although there were no effects of trampling or amount of litter impact on soil moisture and litter moisture in this experiment, both of these variables were important determinants of the snail community composition. Litter moisture particularly, as it was the only variable that significantly impacted all response variables. This result supports other studies worldwide suggesting moisture levels to be one of the key drivers of snail community composition (Martin and Sommer 2004, Čejka and Hamerlík 2009), and in New Zealand a stable moisture supply is one of key reasons for the high density and richness seen in forest sites (Solem 1984). However the majority of the studies that consider moisture levels solely consider soil moisture (Wareborn 1992, Martin and Sommer 2004), but from these results it appears litter moisture may be a more

important determinant of variation in the abundance and composition of leaf litter dwelling land snail communities.

The lack of impact of the experimental treatments on moisture levels in the experiment is perhaps somewhat surprising, considering there is evidence showing differing levels of grazing intensity impacts moisture levels in research elsewhere (Bromham et al. 1999, Chaichi et al. 2005). However, this may not have been seen in these results due to the short term nature of the experiment and the relatively low-impact treatments applied compared with other studies. In the longer term, repeated trampling treatments might well be expected to have a greater influence on soil and litter moisture levels at the sites, via increased soil compaction. The strong observed link between natural variation in soil and litter moisture content on land snail abundance, as well as the weak effects of soil moisture on land snail species richness suggest that any long term impacts of livestock grazing on moisture levels would only exacerbate the already severe impacts from trampling seen in this experiment.

One environmental variable that showed almost no impact on snail communities was penetration resistance, a measure of soil compaction. Also, surprisingly the trampling treatments did not appear to cause substantial changes in the penetration resistance. This is most likely due to the relatively low impact treatments seen in this experiment, and with continued trampling it would be unlikely for this to remain the case. There is ample evidence in the literature for increasing soil compaction with increased grazing intensity (Usman 1994, Chaichi et al. 2005). Also, increases in soil compaction are related to decreases in soil infiltration rate leading to decreased moisture levels which could then impact snail communities (Yates et al. 2000). Severe runoff events can also increase with increased soil compaction, and these are likely to affect snail communities (Rauzi and Hanson 1966, Chanasyk et al. 2003).

Overall these results suggest that even low impact disturbances by livestock have large impacts on land snail communities, but the underlying drivers of these impacts require further investigation, in longer term, higher impact studies. Also, it will not be possible to discriminate livestock effects on land snail community composition without taking into account natural variation in a wide range of environmental conditions. 79

Conservation Management Implications

The number of landowners spending the time and effort to fence remnants in New Zealand is continuing to rise as farmers become more aware of conservation issues, and they realise the conservation value of excluding livestock from the remnants and allowing them to recover. However, many farmers (as high as 77% of farmers in the region this study was conducted in) still value their forest remnants (both fenced and unfenced) for stock shelter (Miles et al. 1998, Jay 2005). This observation is substantiated by the fact that even some farmers that have fenced their remnants for biodiversity conservation still let their livestock into the remnants once or twice a year during bad weather or during lambing and calving (pers. obs.). The results of this experiment however suggest that even this minimal amount of livestock access will cause significant impacts on land snail communities, and should be discouraged. The results also further support the need to maintain livestock exclusion as a priority conservation management action for native forest remnants on farmland.

Appendix 3.1

Experimental plot layout.36 plots in 3 blocks (marked by different fill patterns). Red large grid fill depicts separate spatial block. Blue diagonal and black small grid depict two distinct environmental blocks (blue=<50% cover of thread fern, black=>50% cover)

37 m

20 m 81

Appendix 3.2

(a) Full mechanical hoof frame set up for trampling event. (b) Plot after trampling event (S treatment), showing impressions made from the mechanical hoof. (c) cow hoof attached to the air ram.

(a)

(b) (c)

Appendix 3.3

Full SEM models tested in Amos with the specification search option for three response variables. Models were used to test the relative direct and indirect effects of litter and trampling effects on land snail community composition, abundance and species richness. Model (a) was used for land snail community similarity to reference sites, as well as land snail abundance m-2. Model (b) was used for land snail species richness.

Penetration (a) resistance Trampling treatments Litter complexity

Litter Litter mass Snail community treatments m-2 dissimilarity OR Snail density m-2

Litter moisture Litter * Trampling Soil moisture content

Penetration (b) resistance Trampling Snail species treatments Litter richness complexity

Litter Litter mass treatments

Snail abundance Litter moisture Litter * Trampling Soil moisture content

Appendix 3.4

List of the 108 snail species identified from 288 leaf-litter samples.

Family Genus Species Author Non native 1 Charopidae Allodiscus goulstonei Marshall & Barker, 2008 2 Charopidae Allodiscus kakano Marshall & Barker, 2008 3 Charopidae Allodiscus pallidus Marshall & Barker, 2008 4 Charopidae Allodiscus tessellatus Powell, 1941 5 Charopidae Allodiscus urquharti Suter, 1894 6 Charopidae Cavellia anguicula (Reeve, 1852) 7 Charopidae Cavellia buccinella (Reeve, 1852) 8 Charopidae Cavellia colensoi (Suter, 1890) 9 Charopidae Cavellia irregularis (Suter, 1890) 10 Charopidae Cavellia reeftonensis (Suter, 1892) 11 Charopidae Cavellia roseveari (Suter, 1896) 12 Charopidae Cavellia serpentinula (Suter, 1891) 13 Charopidae Cavellia tapirina (Hutton 1882) 14 Charopidae Cavellioropa cookiana (Dell 1952) 15 Charopidae Cavellioropa huttoni (Suter 1890) 16 Charopidae Cavellioropa microrhina (Suter 1909) 17 Charopidae Cavellioropa moussoni (Suter, 1890) 18 Charopidae Charopa bianca (Hutton 1883) 19 Charopidae Charopa coma (Gray 1842) 20 Charopidae Charopa montivaga Suter 1894 21 Charopidae Charopidae sp. 105 (M.77007) 22 Charopidae Charopidae sp. 137 (M.56568) 23 Charopidae Charopidae sp. 33 (M.85221) 24 Charopidae Charopidae sp. 36 (M.75729) 25 Charopidae Chaureopa titirangiensis (Suter 1986) 26 Charopidae Climocella akarana Goulstone 1996 27 Charopidae Climocella cavelliaformis Goulstone 1996 28 Charopidae Climocella hukutaia Goulstone & Mayhill, 1998 E 29 Charopidae Climocella intermedia Goulstone, 1997 E 30 Charopidae Climocella kaitaka Goulstone, 1996 E 31 Charopidae Climocella rata Goulstone, 1996 E 32 Charopidae Climocella triticum Goulstone& Mayhill, 1998 33 Cochlicopidae Cochlicopa lubrica (Müller, 1774) X 34 Pupinidae Cytora cytora (Gray, 1850) 35 Pupinidae Cytora hedleyi (Suter, 1894) 36 Pupinidae Cytora pallida (Hutton, 1883) 37 Rhytididae Delos coresia (Gray, 1850) 38 Rhytididae Delos jeffreysiana (Pfeiffer, 1853) 39 Charopidae Fectola infecta (Reeve, 1852) 40 Charopidae Flammocharopa accelerata (Climo, 1970) 41 Charopidae Flammulina crebriflammis (L. Pfeiffer, 1853) 42 Charopidae Flammulina zebra (Le Guillou, 1842) 43 Charopidae Geminoropa vortex (R. Murdoch, 1897) 44 Hydroceneidae Georissa purchasi (Pfeiffer, 1862) 45 Charopidae Granallodiscus granum (L. Pfeiffer, 1857) 46 Charopidae Granallodiscus mayhillae Marshall & Barker, 2008 47 Charopidae Huonodon hectori (Suter, 1890) 48 Charopidae Huonodon pseudoleoidon (Suter, 1890) 49 Punctidae Laoma leimonias (Gray, 1850) 50 Punctidae Laoma mariae mariae (Gray, 1843) 51 Punctidae Laoma marina (Hutton, 1883) 52 Pupinidae Liarea turriculata turriculata (Pfeiffer, 1855) 53 Charopidae Mocella eta (L. Pfeiffer, 1853) 54 Punctidae Obanella spectabilis (Powell, 1928) 55 Charopidae Paracharopa chrysaugeia (Webster, 1904)

56 Charopidae Paracharopa fuscosa (Suter, 1894) 57 Charopidae Paracharopa goulstonei Climo, 1983 58 Punctidae Paralaoma allochroida (Suter, 1890) 59 Punctidae Paralaoma caputspinulae (Reeve, 1852) 60 Punctidae Paralaoma lateumbilicata (Suter, 1890) 61 Punctidae Paralaoma miserabilis (Iredale, 1913) 62 Punctidae Paralaoma sericata (Suter, 1890) 63 Punctidae Pasmaditta jungermanniae (Petterd, 1879) 64 Charopidae Phenacharopa pseudanguicula (Iredale, 1913) 65 Charopidae Phenacohelix giveni Cumber, 1961 66 Charopidae Phenacohelix hakarimata Goulstone, 2001 67 Charopidae Phenacohelix perplexa (R. Murdoch, 1897) 68 Charopidae Phenacohelix ponsonbyi (Suter, 1897) 69 Punctidae Phrixgnathus ariel Hutton, 1883 70 Punctidae Phrixgnathus celia Hutton, 1883 71 Punctidae Phrixgnathus erigone (Gray, 1850) 72 Punctidae Phrixgnathus fulguratus (Suter, 1909) 73 Punctidae Phrixgnathus lucidus (Suter, 1896) 74 Punctidae Phrixgnathus microreticulatus (Suter, 1890) 75 Punctidae Phrixgnathus phrynia Hutton, 1883 76 Punctidae Phrixgnathus pirongiaensis (Suter, 1894) 77 Punctidae Phrixgnathus poecilosticta (L. Pfeiffer, 1853) 78 Punctidae Phrixgnathus viridulus viridulus (Suter, 1909) 79 Punctidae Punctidae sp. 100 (M.84972) 80 Punctidae Punctidae sp. 102 (M.85773) 81 Punctidae Punctidae sp. 128 (M.88158) 82 Punctidae Punctidae sp. 140 (M.29067) 83 Punctidae Punctidae sp. 178 (M.84473) 84 Punctidae Punctidae sp. 186 (M.57571) 85 Punctidae Punctidae sp. 190 (M.97919) 86 Punctidae Punctidae sp. 196 (M.103034) 87 Punctidae Punctidae sp. 203 (M.109730) 88 Punctidae Punctidae sp. 216 (M.47067) 89 Punctidae Punctidae sp. 242 (M.83495) 90 Punctidae Punctidae sp. 243 (M.61602) 91 Punctidae Punctidae sp. 246 (M.37005) 92 Punctidae Punctidae sp. 247 (M.25419) 93 Punctidae Punctidae sp. 254 (M.61752) 94 Punctidae Punctidae sp. 27 (M.88000) 95 Punctidae Punctidae sp. 50 (M.14135) 96 Punctidae Punctidae sp. 56 (M.62133) 97 Punctidae Punctidae sp. 59 (M.65253) 98 Punctidae Punctidae sp. 69 (M.55898) 99 Punctidae Punctidae sp. 70 (M.56634) 100 Punctidae Punctidae sp. 71 (M.77798) 101 Punctidae Punctidae sp. 72 (M.93105) 102 Punctidae Punctidae sp. 86 (M.70256) 103 Punctidae Taguahelix campbellica (Filhol, 1880) 104 Punctidae Taguahelix elaiodes (Webster, 1904) 105 Charopidae Thalassohelix zelandiae (Gray, 1843) 106 Charopidae Therasiella serrata Cumber, 1967 107 Achatinellidae Tornatellinops novoseelandica (Pfeiffer, 1853) 108 Charopidae Allodiscus goulstonei Marshall & Barker, 2008

Appendix 3.5

Standardized path coefficients from the final structural equation models for each response variable (Figure 3.3 a, b and c, respectively), showing the direct effects, indirect effects (product of the component direct effects within each indirect pathway, summed for all indirect pathways between the independent and dependent variables) and total effects (sum of direct and indirect effects) of factors influencing the respective response variables. Standardised path coefficients express the number of standard deviations of change in the dependent variable for every one standard deviation of change in the independent variable. Bootstrapped confidence intervals were used to estimate significance levels for each of the standardised direct, indirect and total effects below. †, 0.05

Direct effects Indirect effects Total effects (a) Snail abundance Litter treatments -0.202† -0.202† Trampling treatments -0.309** -0.309** Trampling * Litter 0.215† 0.215† Penetration resistance 0.275** 0.275** Soil moisture 0.180* 0.180* Litter moisture 0.282** 0.282** Litter mass m-2 0.680** 0.680** Litter complexity

(b) Snail species richness Litter treatments -0.116* -0.116* Trampling treatments -0.119 -0.119 Trampling * Litter 0.066* 0.066* Penetration resistance 0.158* 0.158* Soil moisture -0.249 0.104* -0.145 Litter moisture 0.162** 0.162** Litter mass 0.391** 0.391** Litter complexity 0.218* 0.218 Snail abundance 0.575** 0.575**

(c) Snail community composition Litter treatments Trampling treatments 0.346† 0.346† Trampling * Litter Penetration resistance 0.319** 0.319** Soil moisture Litter moisture 0.313 0.313 Litter mass m-2 Litter complexity

Chapter 4: The conservation management implications of land-use intensification in New Zealand

The expansion and intensification of agriculture is one of the most significant causes of biodiversity loss worldwide. However, demand for increased food production continues to increase, so much so that the global demand for grain is expected to double by 2050 (Tilman et al. 2002). This is largely due to an expected doubling of human demand for meat associated with increases in income, which is for the most part dependent on increased production of stock feed (Herrero et al. 2009). The land area used for livestock production is already extensive, and increases in land-use intensity have enabled higher stocking rates on pastoral land (Dorrough et al. 2007, Herrero et al. 2009), plus the ability to produce high-protein feed supplements from arable cropping. However, pastoral production causes large-scale impacts on farmland through trampling and grazing and often these impacts scale with various components of land-use intensity (Pietola et al. 2005, Yong-Zhong et al. 2005, Boschi and Baur 2007b). Furthermore, there is extensive evidence that the semi-natural forest remnants embedded with pastoral production landscapes that are accessed by livestock are also degraded (Yates et al. 2000, Smale et al. 2008). Yet, there has not been any research investigating if these off-site impacts scale with land-use intensification. If the off-site impacts of livestock vary with land- use intensification, then the relative benefits of livestock exclusion might also vary under differing land-use intensities. Furthermore, livestock exclusion as a conservation management action could be compromised if inputs into production landscapes spill-over into adjacent remnants even after removal of livestock. The presence or absence of scaling effects of land-use intensification on adjacent forest remnants protected for conservation has important implications for approaches to conservation in production landscapes.

The ecological benefits of livestock exclusion do not scale with land-use intensification

Surprisingly although livestock cause severe damage in unfenced semi-natural vegetation embedded within production landscapes, I found only weak evidence to suggest that these impacts scale with land-use intensification (Chapter two). This is especially surprising considering the large variation in stocking rates across farms that was incorporated in the land-use intensification index. Furthermore, the results from chapter three determined that for land snails at least (and there is evidence elsewhere that results for land snails can be extended to other detritivore groups), any disturbance at all from livestock, even low impact trampling, such as that implemented during the experiment can have severe ecological consequences. Overall, although higher intensity grazing than the levels I tested may cause further damage than I observed, the observed damage from even a small amount of trampling suggests that maximum benefit to conservation will be achieved if livestock are not permitted to access native forest remnants at all.

This research provides strong evidence in support of the argument for livestock exclusion as a priority conservation management action for native forest remnants embedded within production landscapes (Porteous 1993, Pettit et al. 1995, Spooner et al. 2002, Prober et al. 2011). Worldwide, the exclusion of livestock from remnant native vegetation provides benefits for a wide range of plant and animal species, and the ecosystem services driven by these organisms. Benefits include higher native seedling establishment, litter cover, litter decomposition rates, and invertebrate species richness compared with unfenced native remnant vegetation (Latham and Blackstock 1998, Lindsay and Cunningham 2009, Wassie et al. 2009). My study specifically showed that fencing for stock exclusion provides huge benefits for detritivore communities. Unfenced remnants had on average 74% fewer detritivore individuals per m2, 78% lower snail species richness compared with fenced remnants, and snail community composition was 20% less similar to reference sites than in fenced remnants (Chapter two). Moreover, I demonstrated experimentally that even very low intensities of livestock impacts can cause declines of as great as 100 land snail individuals m-2 despite only minimal visible disturbance to litter and 88

soil structure (Chapter three). Taken together, these data suggest that any stock access into forest remnants can cause ecological degradation, and stock exclusion should remain a priority even under low livestock densities.

Despite the very large differences in detritivore communities between fenced and unfenced remnants, and the a priori hypothesis that the effects of intensification of livestock densities on farms should exacerbate impacts within adjacent unfenced remnants (Chapter 2, Figure 1.3), there was surprisingly little evidence for differences in the relative benefit of livestock exclusion in response to land-use intensity. Instead, the benefits of livestock exclusion were extremely high under all land-use intensities studied here. This result suggests that inputs onto production lands, such as nitrogenous fertilisers, that spill-over into adjacent fenced natural systems are potentially only having limited impacts on detritivore communities. One possible explanation for this result could be that nutrient movement into forest remnants may not be high enough to elicit responses from detritivore communities. Research investigating the impacts of increasing nutrient input impacts is most often carried out on-site (i.e., on the farm) were nutrient levels will likely be higher than off-site, and therefore impacts are more likely to be detected. A second possible explanation for the result is that the lack of an observable effect might be due to the confounding of strongly opposing positive and negative mechanisms influencing detritivore community composition in forest remnants. For example, land snail abundance is largely dependent on amount of litter mass present, which is most often reduced at remnant edges, whereas the nutrient quality of the litter might actually improve at forest edges causing opposing positive impacts on land snail communities. This potential for both positive and negative impacts to be influencing detritivore communities could mask any spill-over effects from surrounding land-use intensity.

To determine which of these explanations is more likely an analysis of the soil geochemistry across edge gradients is required to determine the magnitude and extent of nutrient spill-over from farm inputs from the pasture into forest remnants. Also, mechanistic discrimination of the effects of livestock density from the effects of fertiliser application through experiments will be important to further understand the impacts land-use intensification. The

expectation might be that taxa more directly impacted by nutrient flows (such as plants) would be more substantially affected by surrounding land-use intensification. Clearly, this research will need to be extended to other species groups to get a wider representation of land-use intensification impacts over the whole ecosystem.

The evidence provided in this thesis suggests that livestock exclusion has positive benefits for detritivore communities and that there are only weak impacts of spill-over from surrounding land-use intensity on detritivores in protected remnants embedded within production landscapes. This provides support for a land-sparing approach to conservation on production land under current land-use intensities in New Zealand.

Is land sparing a viable option for balancing conservation and production in New Zealand?

Land-sparing and land-sharing are the two extremes of a gradient of approaches proposed for balancing biodiversity conservation and production. In New Zealand, there is a long history of the land-sparing ethic to conservation management, with the vast majority of the productive land given over to agriculture, and most of the remaining portion of unproductive land well protected in an extensive conservation estate. The perception is that wildlife friendly farming will have limited effectiveness for increasing native biodiversity on farmland in New Zealand. Consequently, conservation in agricultural landscapes in New Zealand is typically focussed on protecting semi-natural vegetation remnants, rather than increasing biodiversity on farmland. Land- sparing as an approach to balancing biodiversity conservation and production promotes the separation of conservation and production. Land is set aside for conservation to offset increases in land-use intensification elsewhere under the assumption that the increases in land-use intensity will not impact the land that is spared for conservation (Green et al. 2005, Phalan et al. 2011) . However, if spill-over from surrounding production land did cause degradation of land- spared for conservation this would undermine any arguments for land-sparing as an option for biodiversity conservation. Surprisingly though my research

provided only relatively weak evidence of variation in detritivore communities scaling with land-use intensification in protected remnants embedded within production landscapes. This result has positive implications for the apparent land-sparing approach to biodiversity conservation seen in New Zealand. However, if New Zealand is going to continue with a land-sparing approach to help prevent ongoing biodiversity loss, an effort must be made to increase the representativeness of lowland ecosystems under conservation management. Conservation of lowland ecosystems relies on there being an increase in efforts to conservation on private land (Cocklin and Doorman 1994, Norton and Miller 2000). For this to occur, farmers will need to be convinced that it is in their interest to protect native remnant vegetation on their farms through education of the possible benefits from protection and possibly financial incentives provided by local and/or central government.

One of the key objections to land-sparing is that land-use intensification has not actually resulted in a net increase in protected areas, but instead land- use intensification is often correlated to decreases in the area of native vegetation in the landscape (Ewers et al. 2009). In New Zealand, existing protected areas are likely to remain so, but it is certain that if New Zealand is going to continue with a land-sparing approach, new approaches to balancing conservation and production increases in protected land are needed to conserve a more representative range of ecosystems. Lowland native ecosystems are severely underrepresented in the conservation estate and this is mostly due to the 82% of non-conservation land below 500 m is being dominated by production ecosystems on land under private ownership (Norton and Miller 2000). If New Zealand is going to protect a more representative range of these lowland commercially productive ecosystems, it cannot rely on the large formally protected areas characteristic of New Zealand conservation efforts, but must also focus on conservation on private land where the majority of the last remnants of lowland ecosystems remain.

Although from a conservation manager and ecologists perspective the benefits of livestock exclusion are well recognised, and it is well established as a priority conservation management action for remnant vegetation on farmland, for some landowners the decision is not so clear cut. Farmers with native

vegetation on their land have many reasons for allowing stock access including shelter, belief that grazing has positive benefits for the remnant (i.e., by keeping weeds and non-native plants under control), the high cost of fencing, and probably most importantly, perceived and real economic cost of retiring the land from production completely (Cocklin and Doorman 1994, Miles et al. 1998, Jay 2005). However, many farmers do not realise there can also be benefits to production as well as other economic benefits from excluding livestock and allowing degraded remnants to recover. The potential benefits to farmers from protecting native vegetation on farmland include: remnants acting as windbreaks and sources of predators of pest species and pollinators (farms with crops), increased soil stability, the provision of alternative products, increased property values, and opportunities for conservation tourism and education (Jay 2005). Also, many landowners do not realise the large biodiversity conservation benefits of fencing due to the poor translation of research outcomes into a format that is accessible formats to farmers (e.g., flyers or newsletters). In addition, farmers may assume that limited stock access is acceptable because there are no obvious visual changes to the remnants even though there can be severe impacts that are difficult to observe but could have critical consequences for ecosystem function and the integrity of remnants vegetation (Chapter three). Therefore, more education on both the economic benefits as well as the conservation benefits of complete livestock exclusion is needed for landowners, in order to help convince them it is a viable option for balancing production with conservation on their land.

Landowners will be more likely to protect remnant vegetation on their land if there are direct financial incentives for taking the land out of production. Yet, in New Zealand, even though there are the available regulatory tools under the Resource Management Act to promote biodiversity on private land there are relatively few financial incentives available to make this happen in practice. Currently, the majority of funds provided for conservation on private land are either for purchase or to help with the costs of protection (e.g. fencing, surveys and the legal costs of covenanting) through organisations such as the Nature Heritage Fund and QEII trust. Although the provision of these funds has increased biodiversity conservation on private land, for many landowners who

want to retain ownership there is still the need for financial incentives to offset the costs of taking the land out of production, not just the costs of protection. Surveys of farmers have shown the importance of financial incentives in the decision to undertake conservation of remnant vegetation on their land; for example, in the Rodney Ecological District, 82.5% of landowners with covenants on their land stated that financial incentives put in place by the local council were the most important reason behind the decision to covenant (Cocklin and Doorman 1994). Central and local government will need to take a more pro-active approach to conservation on private land in the future for the protection of higher proportion of lowland ecosystems.

Placing the effectiveness of land-sparing for conservation in a global context

Although this research addresses one of the possible caveats against land- sparing as a viable option for balancing biodiversity and production, there are many other potential caveats to take into account when assessing if land- sparing is likely to be an effective strategy for decreasing biodiversity loss (compared with a land-sharing approach) in a particular system. In particular, historical context and type of agriculture will play an important role in whether land-sparing or land-sharing is a more feasible option for future land-use planning. No single approach to conserving biodiversity in production landscapes is best for all species considered, and instead there needs to be a plurality of approaches to biodiversity conservation depending on agricultural history and the potential for a given type of agriculture to harbour native biodiversity.

Ecosystems with relatively short agricultural histories are more likely to be impacted by land-use intensification as native species in these systems have not had a long period of extensive farming to which to adapt, prior to intensification of farming practices. In these systems, decreases in land-use intensification may not result in the increased native biodiversity that is assumed under land-sharing because the native species do not have the evolutionary adaptations to survive in agricultural systems under any land-use

intensity. The inability of the majority of native species to persist on farmland under even low land-use intensities in these systems means that land-sparing is probably a more viable option for biodiversity conservation. In New Zealand, the low intensity extractive pastoralism phase of early agriculture lasted only a few decades before intensification began in earnest in the 1920s, compared to possibly 1000s of years in European agro ecosystems (MacLeod and Moller 2006). This short history has limited the ability of native biodiversity to adapt to the new farming practices and to the presence of non-native species. This is especially important when considering livestock production as New Zealand’s ecosystems had not been exposed to the trampling and browsing effects of hard hoofed terrestrial mammals until livestock were introduced for pastoral farming (Wardle et al. 2001, Lee et al. 2008). This lack of exposure to grazing and trampling in New Zealand agro ecosystems means that New Zealand’s native species are particularly sensitive to recent land-use intensification. Other countries with recent agricultural histories, such as Australia, have similar issues. Over 56% of Australia’s land area is used for livestock grazing, yet until around 200 years ago there had been no extensive evolutionary history of grazing by large herbivores, so that most native species are ill-equipped to persist under the elevated grazing pressures that arose with the introduction of livestock production farming (Morton et al. 1995, Landsberg et al. 2003).

In long-established agro-ecosystems (e.g. Europe) where native species and exotic agricultural species have coevolved for a long period of time, it is possible that agriculture can have low impacts and even help to maintain native species diversity if land-use intensity remains extensive (Olff and Ritchie 1998, Boschi 2007). To increase yields and therefore profits in these systems, however, increases in land-use intensity are needed through increased agricultural inputs, which at the same time can cause decreases in biodiversity (Green et al. 2005, Kleijn et al. 2009). The semi-natural calcareous grasslands of central Europe can harbour an extremely high diversity of invertebrate and plants, and this has been explained by the intermediate disturbance hypothesis (WallisDeVries et al. 2002, Shea et al. 2004, Boschi 2007). Grazing at light to moderate intensities provides structural heterogeneity in the habitat allowing a wide range of species to persist (Boschi 2007). However, even in these

systems, the extreme increases in land-use intensification that started at the beginning of the 20th century have caused considerable declines to native biodiversity in agricultural landscapes (Poschlod et al. 2005).

On the other hand, a land-sharing approach to conserving biodiversity can have benefits for biodiversity in these systems. For example, Boschi and Baur (2007b) found that pastures without fertilizer application and with low grazing intensity harboured higher snail species richness and a higher abundance of threatened snails than pastures with annual addition of fertilizer and higher grazing intensity. Despite this and other evidence of decreases in land-use intensification providing benefits for biodiversity, biodiversity conservation on farmland (typically through agri-environment schemes) has had mixed results in European agricultural systems (Kleijn and Sutherland 2003, Kleijn et al. 2006). Key initiatives have had benefited some species groups (Knop et al. 2006, Carvell et al. 2007), but had no impact on other species groups (Feehan et al. 2005, Knop et al. 2006), and there is evidence of negative impacts on certain species (Kleijn et al. 2001). Therefore, either current agri- environment schemes are not entirely effective, or land sharing is not a viable conservation approach for the conservation of diverse groups of species, even in landscapes with long-established agro-ecosystems, implying that protection of natural areas in these landscapes will remain vitally important.

One of the underlying assumptions of the land-sparing versus land sharing debate is that increases in yield result in biodiversity loss (Green et al. 2005). However, this trade-off may not operate in every case (Clough et al. 2011). For example, if it is possible to obtain higher yields under wildlife friendly farming than conventional farming, and the practices remain cost effective (wildlife friendly farming is often more labour insensitive than conventional farming), then this would undermine land sparing as an option in those systems (though even in these systems large natural areas will likely still play an important role in conservation). However, there is little research providing empirical tests of the biodiversity/yield relationships, which is limits the generalisation of this issue. Tropical agroforests have the potential to be production systems where land-sharing is a viable option because the vegetation structure in these agroforests resemble that of natural rainforests,

which results in high biodiversity within the agroforest systems (Steffan- Dewenter et al. 2007, Clough et al. 2011). In a review of studies across the tropics comparing biodiversity in agroforestry with that in neighbouring forest reserves, Bhagwat et al. (2008) found that the mean species richness in agroforests was over 60% of what was found in natural rainforest, and the mean similarity of community composition was as high as 65% (for mammals). A study of biodiversity in agroforests in Sulawesi, Indonesia revealed that the species richness of trees, fungi, invertebrates and vertebrates in small holder cacao agroforests did not decline with increasing cacao yields – the first evidence of a win-win situation for biodiversity and yield so far (Clough et al. 2011). This particular study provided comprehensive support for the use of wildlife friendly farming in these systems, while still advocating the protection of natural areas to support species only present in less disturbed sites.

Even in systems with limited potential to harbour native biodiversity on production land there are still opportunities for certain types of production to provide habitat for native species, or at least to buffer and so reduce species losses from adjacent natural habitat or to provide connectivity between patches of remnant vegetation. New Zealand plantation forestry is a key example of a type of land-use that can provide some benefits to biodiversity conservation. There is ample evidence for plantation forestry providing habitat for native species, even threatened and uncommon species and plantations can therefore contribute to their conservation (e.g., Norton 1998, Brockerhoff et al. 2008). For example, plantation forest provides habitat for many indigenous birds and is likely to be the only remaining habitat of the critically endangered ground beetle, Holcaspis brevicula (Brockerhoff et al. 2008). The opportunities for conservation in plantation forestry can be realised with management strategies that consider effects on biodiversity such as increasing diversity of planted trees, longer rotational length between destructive harvests and using a variety of harvesting approaches. Of course the conservation value of plantation forest is still not comparable to natural forest and it is a step away from the best examples of wild-life friendly farming, but in landscapes dominated by pastoral farming, plantation forests are increasingly recognised for their contribution to biodiversity conservation (Carnus et al. 2006, Pawson et al. 2008).

Conclusions

Although New Zealand appears to be constrained on a land sparing trajectory, with little hope for win-win scenarios for biodiversity on farmland, particularly when considering livestock production, this thesis provides a positive outlook for the present conservation management actions used to increase biodiversity in forest remnants embedded within farmland under current land-use intensities. However, if New Zealand continues further along the land-use intensification trajectory there will need to be a closer relationship between practitioners, government and landowners to ensure the maintenance of biodiversity conservation on private land, and a better understanding of how any further increases in intensification will impact current and future conservation efforts. Lessons learned in New Zealand may be applicable to agricultural landscapes elsewhere that share similar histories of land-use and conservation culture.

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